TEST BANK for Business Analytics 2nd Edition by Sanjiv Jaggia,Alison Kelly,Kevin Lertwachara & Chen

TEST BANK

TEST BANK

Chapter 1: Introduction to Business Analytics Chapter 2: Data Management and Wrangling Chapter 3: Summary Measures Chapter 4: Data Visualization Chapter 5: Probability and Probability Distributions Chapter 6: Statistical Inference Chapter 7: Regression Analysis Chapter 8: More Topics in Regression Analysis Chapter 9: Logistic Regression Chapter 10: Forecasting with Time Series Data Chapter 11: Introduction to Data Mining Chapter 12: Supervised Data Mining: k-Nearest Neighbors and Naive Bayes Chapter 13: Supervised Data Mining: Decision Trees Chapter 14: Unsupervised Data Mining Chapter 15: Spreadsheet Modeling Chapter 16: Risk Analysis and Simulation Chapter 17: Optimization: Linear Programming Chapter 18: More Applications in Optimization

1)

Which of the following broad categories is not a type of analytic technique?

A) manipulative analytics B) descriptive analytics C) predictive analytics D) prescriptive analytics

2) The people of Appleton, WI represent the __________, whereas we analyze the education level of a subset or __________ to make inferences about the population.

A) information; cross-section B) population; information C) items; sample D) population; sample

3) A massive volume of both structured and unstructured data that is extremely difficult to manage, process, and analyze is known by which catch phrase?

A) wrangling B) big data C) data mining D) general data

4) The 2019 FIFA Women’s World Cup contained 52 matches in total with 24 teams competing. The use of __________ data will display team standings during and at the end of the tournament.

A) split-section B) organized C) cross-sectional D) numerical

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5) According to a report in US Today, 42% of young people between the ages of 17–30 have at least two tattoos. What does the 42% represent?

A) categorical data B) random data C) population D) sample set

6) According to a report in US Today, 38% of young people between the ages of 18–29 have at least one tattoo. What does the 38% represent?

A) categorical data B) random data C) population D) sample set

7) According to a report in US Today, 45% of young people between the ages of 18–28 have at least two tattoos. What do the overall observations in the study represent?

A) categorical data B) random data C) population D) sample set

8) According to a report in US Today, 38% of young people between the ages of 18–29 have at least one tattoo. What do the overall observations in the study represent?

A) categorical data B) random data C) population D) sample set

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9) Mary asks her friends on Facebook for recommendations for the best restaurants in Chicago. The results are then placed in a table for review. What does the data represent?

A) time-series data B) cross-sectional data C) numerical data D) quantitative data

10)

In big data, the most important aspect of any analytic initiative is __________.

A) volume B) veracity C) values D) variety

11)

What term refers to the credibility and quality of data?

A) volume B) veracity C) values D) variety

12) When compiling data, it is important to know data comes in all types, forms, and granularity. This is known as A) volume.

B) veracity.

C) values.

D) variety.

13) A New York Times article notes that there are an increasing number of people calling for tech companies to ease their grip on the personal data of consumers. The concern is that a handful of companies holds most of the data. The immense amount of data is also called

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A) volume.

B) veracity.

C) values.

D) variety.

14)

Unstructured data is best defined as A) not conforming to a predefined, row-column format.

B) not conforming to a way to analyze data.

C) conforming to a predefined, row-column format.

D) conforming to a managing velocity.

15) Tobias Smith is working with his company’s data to examine inventory information. His intent is to use the variables to express ratios on inventory turnover. Based on this description, what is the strongest level of measurement being used?

A) continuous variable B) interval scale C) categorical D) ratio scale

16)

The height of plants is what type of variable?

A) distraction B) discrete numerical C) categorical D) continuous numerical

17)

The time in hours spent sleeping per day is what kind of variable?

A) distraction B) discrete numerical C) categorical D) continuous numerical

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18) Molly Nelson has been collecting temperatures in degrees Fahrenheit, daily over the past five spring seasons, to determine the optimal point to plant her heirloom tomatoes. Because the difference between each degree is the same, irrelevant of the temperature, this is what type of measurement scale?

A) ratio B) nominal C) ordinal D) interval

19) Cassidy is researching the impacts of eating breakfast on college students who have classes prior to 9 am. To do this, she issued a Likert scale questionnaire to the students, with a scale of 1 through 10 to answer a series of 20 questions. What is the type of measurement scale?

A) ratio B) nominal C) ordinal D) interval

20) A large retailer is asking each customer at checkout for their zip code. If the zip code is the only recorded variable, what would the summarized results field headers be in tabular format?

A) zip code B) customer number, zip code, count C) zip code, count D) count

21) A large retailer is asking each customer at checkout for their zip code. If the zip code is the only recorded variable, what is the type of measurement scale?

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A) ratio B) nominal C) ordinal D) interval

22)

Which one is a drawback of interval-scaled data?

A) The zero point is arbitrarily chosen.

B) The degree of measurement is not a whole number.

C) The scale is categorized and qualitative.

D) The scale in nominal and zero point is meaningful.

23)

Of the following numerical variables, which is continuous?

A) number of newborn babies B) number of goals scored C) number of children D) time in minutes

24)

Of the following numerical variables, which is continuous?

A) number of goals scored B) number of stocks C) number of children D) weight

25)

Which one of the following variables is numerical?

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A) name B) population C) hair color D) country

26)

Which one of the following variables is numerical?

A) city B) population C) state D) color

27) An instructor hands out course evaluations where students have a rank of 0 to 5. What is the best way for the data to be measured?

A) filtered B) ordinal C) nominal D) numerical

28) During the winter, the ice festival committee measures the depth of the ice during the month of February. What is the type of measurement scale?

A) ratio B) interval C) nominal D) numerical

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29) At the local animal shelter, each animal is marked if they are a boy or a girl. What is this type of measurement scale?

A) filtered B) ordinal C) nominal D) numerical

30)

The following table is an example of what type of format?

Inv_Nbr 12232 13425 19932

Inv_Name Filter, Air Gasket Battery

Inv_Cost $9.65 $4.32 $32.00

A) hypertext B) extensible C) delimited D) fixed-width

31) What is the following file format called?

Inv_Nbr, Inv_Name, Inv_Cost 876521,battery,45.00 A) delimited format B) extensible markup C) fixed-width format D) hypertext format

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32) What type of markup language is this? <Data> <Inventory> Inv_Nbr>102304</Inv_Nbr> Inv_Name>Filter</Inv_Name> </Inventory> </Data> A) HTML B) XML C) DFF D) JSON

33) The following is an example of which markup language? { “Inventory”: [ { “Inv_nbr”: “0199284”, “Inv_Name”: “Filter” } ] } A) HTML B) XML C) DFF D) JSON

34) The following is an example of which markup language? <table> <tr> <th> Inventory_Nbr </th> <th> Inventory Description </th> </tr> </table>

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A) HTML B) XML C) DFF D) JSON

35)

Which is not a viable markup language?

A) HTML B) XML C) DFF D) JSON

36)

Which of the following is not related to data privacy?

A) Data collection B) Data ethics C) Data usage D) Data transmission

37) __________ is a set of data that are organized and processed in a meaningful and purposeful way.

A) Data B) Knowledge C) Statistics D) Information

38)

Which of the following is an example of a fixed-width format?

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A) Cust_Nbr 1232 1325 1972

Cust_Name Mike Barnes Lakshmi Singh Seo-Jun Hak

Cust_Bal $1,059.65 $2,914.32 $932.00

B)Cust_Nbr,Cust_Name,Cust_Bal 1232,Mike Barnes,$1,059.65 1325,Lakshmi Singh,$2914.32 1972,Seo-Jun Hak,$932.00 C)<table> <tr> <th> Cust_Nbr </th> <th> Cust_Name</th> <th> Cust_Bal</th> </tr> </table> D){ “Customer”: [ { “Cust_nbr”: “1325”, “Cust_Name”: “Lakshmi Singh” } ] }

39)

Which of the following is an example of a JSON markup language?

A) Cust_Nbr 1232 1325 1972

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Cust_Name Mike Barnes Lakshmi Singh Seo-Jun Hak

Cust_Bal $1,059.65 $2,914.32 $932.00

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B)Cust_Nbr,Cust_Name,Cust_Bal 1232,Mike Barnes,$1,059.65 1325,Lakshmi Singh,$2914.32 1972,Seo-Jun Hak,$932.00 C)<table> <tr> <th> Cust_Nbr </th> <th> Cust_Name</th> <th> Cust_Bal</th> </tr> </table> D){ “Customer”: [ { “Cust_Nbr”: “1325”, “Cust_Name”: “Lakshmi Singh”, “Cust_Bal”: “$2,914.32” } ] }

40) The ability to use qualitative reasoning with quantitative tools allows management to make decisions to improve business performance. ⊚ true ⊚ false

41) The use of historical information to predict what could happen in the future describes prescriptive analytics. ⊚ true ⊚ false

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42) Data are the compilation of facts, figures, or other contents, both numerical and nonnumerical. ⊚ true ⊚ false

43) Generally speaking, it is not feasible to obtain complete population data due to expense and near impossibility to examine every member of the population. ⊚ true ⊚ false

44) Collecting height data annually on the sample set of participants is an example of time series data. ⊚ true ⊚ false

45)

Structured and unstructured data are only machine generated. ⊚ true ⊚ false

46)

Numerical variables are either discrete or continuous. ⊚ true ⊚ false

47) When each piece of data in a file is separated by a comma, it is called delimiter and the file is called a comma-spliced file. ⊚ true ⊚ false

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48)

When coding in HTML, <table> is a tag used to provide structure for textual data. ⊚ true ⊚ false

49) In XML, tags are not case-sensitive and are interchangeable. For example, <City>and <city>represent the same pieces of information. ⊚ true ⊚ false

50) Sally created a table that summarizes the dollar amount of last year’s sales for each store.

This is an example of descriptive analytics. ⊚ true ⊚ false

51) data.

Social media data, such as Twitter, Facebook, and TicTok are examples of structured ⊚ ⊚

52)

true false

The characteristics marital status and income are examples of observations. ⊚ true ⊚ false

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Answer Key Test name: ch 1 1) A 2) D 3) B 4) C 5) D 6) D 7) C 8) C 9) B 10) C 11) B 12) D 13) A 14) A 15) D 16) D 17) D 18) D 19) C 20) C 21) B 22) A 23) D 24) D 25) B 26) B Version 1

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27) B 28) A 29) C 30) D 31) A 32) B 33) D 34) A 35) C 36) B 37) D 38) A 39) D 40) TRUE 41) FALSE 42) TRUE 43) TRUE 44) TRUE 45) FALSE 46) TRUE 47) FALSE 48) TRUE 49) FALSE 50) TRUE 51) FALSE 52) FALSE

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Student name:__________ 1)

Which of the following is NOT a process of the data management system?

A) acquire B) distribute C) store D) summarize

2)

Which term represents data items, events, or things stored in a database file?

A) instance B) entity C) settings D) quantitative

3) Mary in the accounting department has been assigned a specific vehicle as her company car to perform audits. This represents which type of relationship?

A) 1 : 1 B) 1 : M C) M : N D) M : M

4)

Select, From, and Where keywords are statements used in _____________.

A) DBMS B) XML C) SQL D) JAVA

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5) The primary purpose of a(n) _____________ is to support decision-making and provide a composite view of the organization.

A) data warehouse B) data mart C) entity D) attribute

6) A non-relational database structure that can support the storage of a wide range of data, including structured, semi-structured, and unstructured is called _____________.

A) SQL B) Free Range C) NoSQL D) Recreational

7) Mary has been tasked with reviewing a large data file. She wants to begin by first inspecting the number of values in each cell, both numeric and non-numeric, for any blank entries. The plan is to first find the blank or missing values for first review. Using Excel, what function(s) should she use to complete this task?

A) COUNT B) COUNTA C) COUNTIF D) Both COUNT and COUNTA

8) Molly wants to view observations with missing values in Inventory. However, her data set is quite large. What functions should she use to complete her task in R?

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A) > is.na (myData.Inventory) B) > is.na (myData$Inventory) C) > which (is.na(myData$Inventory)) D) > which (is.na(myData.Inventory))

9) In the presence of outliers in a data set, extremely small or large values, it is preferred to use the _____________ instead of the _____________ to impute missing variables.

A) median; mean B) mean; median C) subset; total D) average; range

10) In a data set with 17 variables, if 14% of the values, randomly spread across observations, are missing (blank), what is the probable percent of complete and usable observations?

A) 86% B) 14% C) 7.70% D) 3.62%

11) In a data set with 20 variables, if 8% of the values, randomly spread across observations, are missing (blank), what is the probable percent of complete and usable observations?

A) 92% B) 8% C) 18.87% D) 15.29%

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12) Using the simple mean imputation strategy, what value would be placed in the missing observation in x1? x1 77 83

x2 22

91

32 41

88

28

A) No value because excluded B) 85 C) 83 D) 68

13) Using the simple mean imputation strategy, what value would be placed in the missing observation in x1? x1 76 82

x2 22

91

32 41

88

28

A) No value because excluded B) 84 C) 83 D) 90

14) x1?

Using the omission strategy, what value would be placed in the missing observation in x1 75

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x2 22

4

81 91

32 41

88

28

A) No value because excluded B) 84 C) 82 D) 67

15) x1?

Using the omission strategy, what value would be placed in the missing observation in x1 76 82

x2 22

91

32 41

88

28

A) No value because excluded B) 84 C) 83 D) 90

16) When performing an analysis, one technique is called RFM. Which of the following is not reflective of RFM? A) recency B) frequency C) monetary D) relevancy

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17) Mark wants to have a better understanding of his client base at the credit union. To do so, he is running a report to show loan amount approval with corresponding credit scores. He realized the data set is quite large and wants to create categories by grouping. To do this, he needs to do all the following except A) identify the value he wants to transform into smaller groups or bins.

B) remove 20% of the data to create a training set.

C) ensure the data sets are not overlapping.

D) identify how he wants the observations to be labeled in the bin.

18) In Analytic Solver, Aimee is trying to create a new column called RFM. This column is merging multiple values into one cell. The function to accomplish this is called?

A) TRANSFORM B) CONCATENATE C) VARIABLE D) VLOOKUP

19)

The function that provides a natural logarithm in Excel is?

A) INT function B) LN function C) YEARFRAC function D) VLOOKUP function

20) In R, Mary wants to understand the number of days between rain events in Chicago, IL. What function is used to find the number of rain events between today and January 1, 2026?

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A) difftime B) as.numeric C) diffdate D) floor

21) Using R, what is the formula that will allow for the weekday function to display the day of the week for November 15, 2020?

A) >weekdays< (as.Date(“2020-11-15”) B) > format(as.Date(“2020-11-15”), “%d”) C) > weekdays(as.Date(“2020-11-15”)) D) > Sys.Date(“2020-11-15”)

22) Four observations were binned into one group. In this group, the values are: 40, 45, 38, and 21. What is the average of the group?

A) 38 B) 37 C) 35 D) 36

23) Four observations were binned into one group. In this group, the values are: 40, 45, 38, and 33. What is the average of the group?

A) 41 B) 40 C) 38 D) 39

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24) The following table contains 2 variables with 2 observations. A new variable was created named Sum. This is the sum of the values x1 and x2 for each observation. What is the average value of Sum if the chart is completed? x1 80

x2 40

82

32

Sum

A) 117 B) 64 C) 120 D) 114

25) The following table contains 2 variables with 2 observations. A new variable was created named Sum. This is the sum of the values x1 and x2 for each observation. What is the average value of Sum if the chart is completed? x1 76

x2 22

82

32

Sum

A) 106 B) 53 C) 98 D) 114

26) When too many variables are categorized in an analysis, several potential issues may occur. Which of the following is not one of the issues that may occur?

A) model performance suffers B) rarely occurring categories may not be captured accurately C) difficulty in differentiating among observations D) an increase in the number of categories as the data set becomes larger

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27) Henry wants to analyze income, but the sheer number of categories in the data’s current form will make a clear analysis less meaningful. In Excel with Analytic Solver, how will Henry determine the frequency of each category to transform his data?

A) Income variable is selected and Analytic Solver produces frequency levels for each Income category from most to least frequent.

B) Inspect the frequency of Income category: >table(myData$Income).

C) Income variable is selected and Analytic Solver produces a new category for non-use variables.

D) Apply a limit to the number of categories from the drop-down to a reasonable number.

28) Using R, what function is used to evaluate the categories in the variable to identify the dummy variables?

A) referral B) if C) ifelse D) view

29) In the following table, there are four observations with three variables. Which category is the best fit to be transferred into dummy variables?

Marital Status Single Married Single Married

Age 24 26 33 28

Income $45,000 $33,000 $53,000 $59,000

A) age B) marital status C) income D) none are a good fit for a dummy variable

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30) Ann is analyzing a data set that contains two variables, Job Title and 401K. 401K contains the name of the three companies that carry the retirement accounts. It is mandatory to have an account, thus no observation is blank. If 401K was transformed to dummy variables, how many should be created?

A) 2 B) 3 C) 4 D) 1

31) Transform the marital status into dummy variables where Single = 1 and Married = 0.

How many would have the category score of 0?

Marital Status Married Single Single Single Single Married

Age 24 26 33 28 36 29

Income $45,000 $33,000 $53,000 $59,000 $62,000 $48,000

A) 4 B) 6 C) 2 D) 0

32) Transform the marital status into dummy variables where Single = 1 and Married = 0.

How many would have the category score of 0?

Marital Status Single Married Single Married Married

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Age 24 26 33 28 36

Income $45,000 $33,000 $53,000 $59,000 $62,000

10

Married

29

$48,000

A) 2 B) 6 C) 4 D) 0

33) Michael is examining a data set and trying to determine which category he can transform into a dummy variable. Of the four variables, Employee Number, Pay Rate, Hire Date, and Sex, which is the best fit to use a dummy variable?

A) employee number B) pay rate C) hire date D) sex

34) Marcus wants to include the month of the year in the analysis as categories. How many dummy variables will be needed?

A) 12 B) 11 C) 6 D) 1

35) Kara is reviewing categories where a series of numbers represent the type of loan. She would prefer the actual name of the loan be retained when running her analysis. Using Microsoft Excel, what function will allow Kara to retain the category name instead of recording them in numbers?

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A) log function B) view function C) IF function D) head function

36) Using the following table view, Mark wants to create a relationship between the two tables. What will he need to add to establish a relationship?

A) primary key B) foreign key C) instances D) entities

37) Which of the following Excel functions will Ibrahim use to determine how many employees make more than $20 per hour?

A) COUNT B) COUNTA C) COUNTIF D) COUNTIFS

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38) Which of the following Excel equations will identify the number of married individuals under the age of 30?

Marital Status Single Married Single Married Married Married

Age 24 26 33 28 36 29

Income $45,000 $33,000 $53,000 $59,000 $62,000 $48,000

A) =COUNT(A2:A7, “=Married”, B2:B7,“<30”) B) =COUNTA(A2:A7, “=Married”, B2:B7,“<30”) C) =COUNTIF(A2:A7, “=Married”, B2:B7,“<30”) D) =COUNTIFS(A2:A7, “=Married”, B2:B7,“<30”)

39) Using the imputation strategy for categorical values, what value would be placed in the missing observation in Fav_Color?

Age 22

Fav_Color Purple Blue

32 41 28

Purple Red

A) No value because excluded B) Blue C) Purple D) Red

40) What data preparation technique is Maeve using when she extracts a payroll data set into two separate files, one for hourly employees and one for salary employees?

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A) Separating B) Subsetting C) Typesetting D) Wrangling

41)

Which of the following is NOT an example of categorical data transformation?

A) Category binning B) Category reduction C) Category scores D) Dummy variables

42) The variable x1 contains three categories ranging from “Poor” to “Good.” Convert the category names into category scores into x2 (i.e., 1 = “Poor”, 2 = “OK”, and 3 = “Good”). How many observations have a category score of 1? x1 OK

x2

Good Good Poor OK Good

A) 0 B) 1 C) 2 D) 3

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43) The process of retrieving, cleaning, integrating, transforming, and enriching data to support analysis is called data wrangling. ⊚ true ⊚ false

44)

A foreign key (FK) is the only unique identifier in a table structure. ⊚ true ⊚ false

45) In R, the following represents how to receive results from column 3, row 2 > myData[3,2]. ⊚ true ⊚ false

46)

In R, to sort data in descending order, we use a negative parameter in the order function. ⊚ true ⊚ false

47) Simple mean imputation is the best route for replacing large quantities of missing variables within a data set without distorting the relationship among variables. ⊚ true ⊚ false

48)

To view only a portion of the data that is of interest, subsetting is used. ⊚ true ⊚ false

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49)

Converting data from one structure to another is called data transformation. ⊚ true ⊚ false

50)

Subsetting is a technique used to convert numerical values into categorical variables. ⊚ true ⊚ false

51)

A dummy variable takes on a value of 1 or 0 to describe two categories of a variable. ⊚ true ⊚ false

52) Megan took a phone survey where each question posed had an answer range of unsatisfied to completely satisfied describing her purchase experience. Because the categories are in equal increments, the category can be recoded into a number transforming the category into what is called a category score. ⊚ true ⊚ false

53)

The strategy of removing observations with missing data is called omission. ⊚ true ⊚ false

54) Changing an individual’s date of birth to age, combining height and weight to create body mass index, calculating percentages, or converting values to natural logarithms are examples of transforming categorical data.

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⊚ ⊚

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true false

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Answer Key Test name: ch 2 1) D 2) B 3) A 4) C 5) A 6) C 7) D 8) C 9) A 10) C 11) C 12) B 13) B 14) A 15) A 16) D 17) B 18) B 19) B 20) A 21) C 22) D 23) D 24) A 25) A 26) D Version 1

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27) A 28) C 29) B 30) A 31) C 32) C 33) D 34) B 35) C 36) B 37) C 38) D 39) C 40) B 41) A 42) B 43) TRUE 44) FALSE 45) FALSE 46) FALSE 47) FALSE 48) TRUE 49) TRUE 50) FALSE 51) TRUE 52) TRUE 53) TRUE 54) FALSE

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Student name:__________ 1)

Which of the following functions do you use to calculate the pth percentile in Excel?

A) PERCENTILE.INC B) QUANTILE.INC C) quantile D) summary

2)

Ashley has a data set of sales transactions in the following format: Cust 1 2

Gender Female Male

Clothing 246 171

Tech 64 345

⋮ 130

⋮ Male

⋮ 52

⋮ 58

Which of the following functions will return the average sales for the technology department for females in R? A) =AVERAGEIF(B2:B131, “Female”, D2:D131) B) =AVERAGEIF(D2:D131, “Female”, B2:D131) C) tapply(myData$Gender, myData$Tech, mean) D) tapply(myData$Tech, myData$Gender, mean)

3)

Find the mode of the values: 1, 4, 5, 3, 4, 6, 1, 2, 4, 3, 2 A) 1 B) 2 C) 3 D) 4

4)

Find the median of the values: 1, 4, 5, 3, 4, 6, 1, 2, 4, 3, 2

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A) 1 B) 2 C) 3 D) 4

5) Survey results provided the skewness coefficient is 0.21672 and the (excess) kurtosis coefficient is −1.15926. These values imply that the return value for the survey is __________ skewed, and the distribution has a __________ tail than a normal distribution.

A) negatively; longer B) negatively; shorter C) positively; longer D) positively; shorter

6) Four investment return distributions are the same, except for skewness (e.g., same mean and standard deviation). Which investment would you chose to increase the likelihood of positive returns?

A) −0.25 B) 0.05 C) 0.55 D) 0.75

7) The coefficient correlation for rent and square footage is computed to be 0.84, this means the relationship between the two variables are __________.

A) strong and positive B) weak and negative C) weak but positive D) strong but negative

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8) In a boxplot, the dashed vertical line in the middle of the box represents which of the following measures of location?

A) mean B) median C) mode D) percentile

9)

What is the median in the following table on the variable score?

Student_ID R304110 R304003 R102234 R209939

Score 0.98 0.88 0.65 0.92

A) 0.86 B) 0.88 C) 0.90 D) 0.92

10)

What is the only meaningful measure of the central location for a categorical variable?

A) mean B) mode C) median D) model

11)

What is the arithmetic mean in the following table on the variable score?

Student_ID R304110 R304003

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Score 0.97 0.80 3

R102234 R209939

0.65 0.92

A) 0.92 B) 0.80 C) 0.725 D) 0.8350

12)

What is the arithmetic mean in the following table on the variable score?

Student_ID R304110 R304003 R102234 R209939

Score 0.98 0.88 0.65 0.92

A) 0.92 B) 0.88 C) 0.765 D) 0.8575

13) Carmen is a professor at a local university. After collecting data on her Introduction to Business course for a year, she wants to calculate the z-score for a student who scores 95 on the final exam. The mean and the standard deviation scores on the exam are 76 and 5, respectively.

Calculate the z-score.

A) 2.80 B) 3.80 C) 1.80 D) 1.20

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14) Carmen is a professor at a local university. After collecting data on her Introduction to Business course for a year, she wants to calculate the z-score for a student who scores 90 on the final exam. The mean and the standard deviation scores on the exam are 76 and 6, respectively.

Calculate the z-score.

A) 1.33 B) 2.33 C) 0.33 D) 2.67

15)

The empirical rule states all the following except A) <p>almost all observations fall in the interval .

B) <p>approximately 95% of all observations fall in the interval <i> C) <p>approximately 65% of all observations fall in the interval <i> D) <p>approximately 68% of all observations fall in the interval .

. .</i></p>

16) The mean credit score is 625 out of 340 used car loan applicants with a standard deviation of 15. Assuming a bell-shaped curve, what is the number of loan applicants that fall within a score of 595 and 655?

A) 60 B) 231 C) 323 D) 340

17) The mean credit score is 640 out of 300 used car loan applicants with a standard deviation of 16. Assuming a bell-shaped curve, what is the number of loan applicants that fall within a score of 608 and 672?

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A) 96 B) 204 C) 285 D) 300

18) Candice is preparing for her final exam in Statistics. She knows she needs 71 out of 100 to earn an A overall in the course. Her instructor provided the following information to the students. On the final, 200 students have taken it with a mean score of 63 and a standard deviation of 6. Assume the distribution of scores is bell-shaped. Calculate to see if a score of 71 is within one standard deviation of the mean.

A) Yes, 71 is the upper limit of one standard deviation from the mean.

B) Yes, the upper level of one standard deviation is 69.

C) Yes, 71 is greater than the 57, one standard deviation below the mean.

D) No, 71 is greater than one standard deviation above the mean, 69.

19) Candice is preparing for her final exam in Statistics. She knows she needs 80 out of 100 to earn an A overall in the course. Her instructor provided the following information to the students. On the final, 200 students have taken it with a mean score of 72 and a standard deviation of 6. Assume the distribution of scores is bell-shaped. Calculate to see if a score of 80 is within one standard deviation of the mean.

A) Yes, 80 is the upper number of one standard deviation from the mean.

B) No, the upper level of one standard deviation is 78.

C) Yes, 80 is greater than the 66, one standard deviation below the mean.

D) No, 80 is greater than the mean of 72.

20)

Using the following Boxplot, identify the median score on the test.

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A) 28 B) 68 C) 60 D) 90

21)

Using the following Boxplot, what is the star to the far right considered?

A) upper quartile mark B) an outlier C) a whisker D) a deviation

22) In the following Boxplot, the left whisker is longer than the right whisker. This indicates that the underlining distribution is __________.

A) negatively skewed B) median skewed C) positively skewed D) no indicated skew

23)

The degree of strength of the linear relationship between x and y is called?

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A) correlation determination B) index C) standard deviation D) correlation coefficient

24) As observations become more dispersed, the difference between the minimum and maximum observation of a variable is called __________.

A) the range B) the variable C) the interquartile D) the mean absolute deviation

25)

The interquartile range is IQR = Q3 − Q1. Thus, it can be thought of as A) the 75% interquartile range.

B) the quartile or 25% of the variable.

C) the middle 50% of the variable.

D) the incorporation of all observations.

26)

Find the Mean Absolute Deviation (MAD) of 13, 9, 9, 11, 13.

A) 4.0 B) 1.60 C) 11.0 D) 3.0

27)

Find the Mean Absolute Deviation (MAD) of 10, 9, 3, 8, 10.

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A) 5 B) 2 C) 8 D) 4

28) The following table is the summary statistics for Scores. Calculate the Sharpe ratio of growth. Assume Rf = 4.

Type

Minimum

Maximum

Mean

Scores

8

61

40.8182

Standard Deviation 5.83193

A) 4.152 B) 1.458 C) 6.999 D) 6.313

29) The following table is the summary statistics for Scores. Calculate the Sharpe ratio of growth. Assume Rf = 4.

Type

Minimum

Maximum

Mean

Scores

8

61

30.8182

Standard Deviation 4.93193

A) 3.699 B) 1.232 C) 6.472 D) 5.438

30) Alex is working on an investment portfolio reviewing Gas and Diesel summary statistics.

Which investment would be the better risk per unit, assuming Rf = 2?

Type

Minimum

Maximum

Mean

Gas

113.9

125.9

120.9

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Standard Deviation 3.620927

9

Diesel

118.3

123.4

114.5

2.783401

A) Gas provides a higher Sharpe ratio with 32.8369, thus more reward per unit of risk.

B) Gas provides a higher Sharpe ratio with 58.6395, thus more reward per unit of risk.

C) Diesel provides a higher Sharpe ratio with 55.8583, thus more reward per unit of risk.

D) Diesel provides a higher Sharpe ratio with 40.4182, thus more reward per unit of risk.

31) Alex is working on an investment portfolio reviewing Gas and Diesel summary statistics.

Which investment would be the better risk per unit, assuming Rf = 2?

Type Gas Diesel

Minimum

Maximum

Mean

113.9 118.32

125.9 123.4

119.9 120.5

Standard Deviation 3.620927 2.783401

A) Gas provides a higher Sharpe ratio with 32.5607, thus more reward per unit of risk.

B) Gas provides a higher Sharpe ratio with 73.9700, thus more reward per unit of risk.

C) Diesel provides a higher Sharpe ratio with 153.8165, thus more reward per unit of risk.

D) Diesel provides a higher Sharpe ratio with 42.5738, thus more reward per unit of risk.

32) The standard deviation of midterm scores and the final exam are 14.0 and 11.0, respectively. Which of the two exams is riskier and why?

A) Both the midterm and the final share the same amount of risk.

B) The midterm exam is riskier because the standard deviation is higher.

C) The midterm exam is riskier because the standard deviation is lower.

D) There is not enough information to determine which is the riskier of the two.

33) The standard deviation of midterm scores and the final exam are 8 and 6, respectively.

Which of the two exams is riskier and why?

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A) Both the midterm and the final share the same amount of risk.

B) The midterm exam is riskier because the standard deviation is higher.

C) The final exam is riskier because the standard deviation is lower.

D) There is not enough information to determine which is the riskier of the two.

34) Survey results provided the skewness coefficient is −0.141974 and the (excess) kurtosis coefficient is 1.15926. These values imply that the return value for the survey is __________ skewed, and the distribution has a __________ tail than a normal distribution.

A) positively; longer B) positively; shorter C) negatively; longer D) negatively; shorter

35) In analyzing the S&P 500 and XYZ Incorporated in a five-year study, the covariance (S&P 500, XYZ Incorporated) is −605.50. What kind of linear relationship does the S&P 500 and XYZ Incorporated have?

A) a positive linear relationship B) a negative linear relationship C) no linear relationship D) a neutral linear relationship

36) In analyzing the S&P 500 and the XYZ Incorporated in a five-year study, the covariance (S&P 500, XYZ Incorporated) is 9,107.30. What kind of linear relationship does the S&P 500 and the XYZ Incorporated have?

A) a negative linear relationship B) a positive linear relationship C) no linear relationship D) a neutral linear relationship

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37)

If the correlations coefficient is 0, then x and y A) are not linearly related.

B) are absolute and perfectly related.

C) have a perfect positive relationship.

D) have a perfect negative relationship.

38) If the coefficient correlation is computed to be 0.05, this means the relationship between the two variables are __________blank.

A) strong, positive B) weak, negative C) strong, negative D) weak, positive

39) If the coefficient correlation is computed to be −0.85, this means the relationship between the two variables are __________.

A) strong, positive B) weak, negative C) weak, positive D) strong, negative

40)

Find the mean of the values: 1, 4, 5, 3, 4, 6, 1, 2, 4, 3, 2 A) 3 B) 3.18 C) 3.27 D) 4

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41) The number of defects in a production process, the salary of a business graduate, the rental price in a neighborhood, and the number of orders for a subscription-based service are examples of dispersion measures. ⊚ true ⊚ false

42) If approximately p percent of the observations have values less than the pth percentile, then approximately (100 − p) percent of the observations have values greater than the pth percentile. ⊚ true ⊚ false

43)

A percentile is calculated using the QUANTILE.INC function in Excel. ⊚ true ⊚ false

44) The variance and the standard deviation are the two most widely used measures of association. ⊚ true ⊚ false

45) A symmetric distribution is one that is a mirror image of itself on both sides of its center, whereas the skewness coefficient measures the degree to which a distribution is not symmetric about its mean. ⊚ true ⊚ false

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46) The covariance between two variables x and y indicates both the direction and the strength of the linear relationship. ⊚ true ⊚ false

47) In a boxplot, if the median is in the center of the box and the left and right whiskers are equidistant from their respective quartiles, a symmetrical shape is implied. ⊚ true ⊚ false

48) Central location is defined as the way numerical data tends to cluster around a middle or central value. ⊚ true ⊚ false

49) In data sets that contain outliers, the arithmetic mean is used as the measure of the central location. ⊚ true ⊚ false

50)

Converting observations into z-scores is also called doubling the observation. ⊚ true ⊚ false

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Answer Key Test name: ch 3 1) A 2) D 3) D 4) C 5) D 6) D 7) A 8) B 9) C 10) B 11) D 12) D 13) B 14) B 15) C 16) C 17) C 18) D 19) B 20) C 21) B 22) A 23) D 24) A 25) C 26) B Version 1

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27) B 28) D 29) D 30) D 31) D 32) B 33) B 34) C 35) B 36) B 37) A 38) D 39) D 40) B 41) FALSE 42) TRUE 43) FALSE 44) FALSE 45) TRUE 46) FALSE 47) TRUE 48) TRUE 49) FALSE 50) FALSE

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Student name:__________ 1) Using the following table, what is the percent of the relative frequency of a Blue car being observed?

Car Color Black Red Blue Silver

Frequency 251 198 195 293

Relative Frequency 0.2680 0.2110 0.2080 0.3130

A) 20.81% B) 2.0810% C) 19.50% D) 0.2081%

2) Using the following table, what is the percent of the relative frequency of a Blue car being observed?

Car Color Black Red Blue Silver

Frequency 251 198 203 293

Relative Frequency 0.2660 0.2100 0.2150 0.3100

A) 21.48% B) 2.1480% C) 20.30% D) 0.2148%

3) Using R, Bart wants to create a bar chart showing the frequency of the color of cars that pass over the I-270 overpass at the Main Street exit. What function should he use?

A) table B) barplot C) abline D) view

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4) Bill wants to calculate the width of each interval by using the approximation formula. He first created a frequency distribution with 5 intervals. The minimum and maximum for the variable are −31.0 and 65.42 respectfully. Calculate the width of each interval for Bill.

A) 13.084 B) 6.200 C) 19.284 D) −19.284

5) Bill wants to calculate the width of each interval by using the approximation formula. He first created a frequency distribution with 5 intervals. The minimum and maximum for the variable are −32.4 and 65.42 respectfully. Calculate the width of each interval for Bill.

A) 13.084 B) 6.604 C) 19.564 D) −19.564

6) Marin produced the following histogram based on his observations on the age of players willing to sample a video game. He then organized the age into frequencies and interval width of the respective intervals. Based on the results, which range has the best frequency for future video game sampling?

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A) 16–18 B) 19–22 C) 23–25 D) 8–10

7)

Using the following histogram, how would the distribution be described?

A) bell-shaped B) symmetric skewed C) positively skewed D) negatively skewed

8) As a researcher, you should be mindful on constructing and interpreting graphs. All of the following are recommended guidelines you should follow except for one. Which one does not reflect a guideline?

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A) The simplest graph should be used for a given set of data.

B) Axis should be clearly marked and labeled.

C) On a bar chart or histogram, bar widths should always reflect various widths of the data.

D) Be mindful of the upper limits on the vertical axis to prevent compression, hiding variant details.

9) The following chart represents the results of a two categorical variable study reflecting on education level survey in 2006 and the same survey results from 2016. Which type of chart is being displayed?

A) scatterplot B) contingency table C) histogram D) stacked column chart

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10)

Below is the Scatterplot of 2 numeric variables. What type of relationship is represented?

A) nonlinear relationship B) linear relationship C) matrix relationship D) no relationship

11) Simone is a marketing consultant hired to review the product sales for a new high-end barista machine line. The product line has four variations, selling in four specialty store regions.

To clearly show where each variation is selling best and in which regions, she plans to provide a color-scaled chart using percentage by type and location. What is the name of the chart she will be using?

A) pivot table B) bubble plot C) color map D) heat map

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12) The following scatterplot is of % of fat and BMI Index Score. What type of linear relationship is being displayed?

A) negative linear relationship B) positive linear relationship C) no relationship D) scattered negative relationship

13)

Which visualization method would best represent the following frequency distribution?

Car Color Black Red Blue Silver

Frequency 251 198 203 293

Relative Frequency 0.266 0.210 0.215 0.310

A) bar chart B) box plot C) histogram D) line chart

14) What is the relative frequency of individuals who rated the restaurant “Excellent” from the following sample?

Rating Poor

Frequency 25

Ok

19

Good

20

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Relative Frequency

6

Excellent

29

A) 0.27 B) 0.29 C) 0.31 D) 0.33

15)

How would you describe the following histogram?

A) bell-shaped B) negatively skewed C) positively skewed D) symmetrically skewed

16)

Which of the chapter recommended guidelines is violated in the graph below?

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A) The simplest graph should be used for a given set of data.

B) Axis should be clearly marked and labeled.

C) On a bar chart or histogram, bar widths should always be consistent as differing widths may create distortions.

D) Be mindful of the upper limits on the vertical axis to prevent compression, hiding variant details.

17) Which of the following would likely be used to report data related to gender and phone model purchased?

A) contingency table B) frequency distribution C) line chart D) scatterplot

18)

Which is the most popular beverage from this sample of 270 men and women?

A) men; beer B) men; soft drink C) women; beer D) women; soft drink

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19)

Which of the following would use a stacked column chart to visualize the results?

A) daily stock prices B) gender and restaurant ratings C) income and purchase price D) location and house prices

20)

Which of the following would use a contingency table to visualize the results?

A) daily stock prices B) gender and restaurant ratings C) income and purchase price D) location and house prices

21) What type of relationship is represented in the following scatterplot of average income per state and median house price?

A) positive relationship B) negative relationship C) no relationship D) steady relationship

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22)

Which of the following would use a scatterplot to visualize the results?

A) daily stock prices B) gender and restaurant ratings C) income and purchase price D) location and house prices

23) What type of relationship is represented in the following scatterplot of the returns for stock portfolios A and B?

A) positive relationship B) negative relationship C) no relationship D) steady relationship

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24)

Which of the following statements is true regarding the scatterplot below?

A) females tend to spend less on technology than males B) females tend to spend less on clothing than males C) males tend to spend more on clothing than females D) males tend to spend less on technology than females

25) Which visualization method will Jorge want to use if he has square footage, property value, and property type (e.g., single-family home, a condominium) data?

A) bubble plot B) heat map C) line chart D) scatterplot with a categorical variable

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26) The relationship between math and writing scores in public schools is _____________ but _____________ than private schools.

A) negative; higher B) negative; lower C) positive; higher D) positive; lower

27) Which visualization method will Jorge want to use if he wants to understand the relationships between study time, screen time, and academic performance (e.g., GPA) data?

A) bubble plot B) heat map C) line chart D) scatterplot with a categorical variable

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28) While birth rate and life expectancy have a _____________ relationship, life expectance and gross national income (size of bubble) have a _____________ relationship.

A) negative; negative B) negative; positive C) positive; negative D) positive; positive

29) Which visualization method will Jorge want to use if he wants to understand daily sales over the course of the year?

A) bubble plot B) heat map C) line chart D) scatterplot with a categorical variable

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30) The following line chart shows the daily close prices for Citigroup and Overstock.com stock prices for the last 30 days. Which stock appears to be more volatile?

A) Citigroup B) Overstock.com C) Both are volatile D) Neither are volatile

31) Which visualization method will Jorge want to use if he wants to understand the relationship between number of downloads for various audiobook genres?

A) bubble plot B) heat map C) line chart D) scatterplot with a categorical variable

32) Which Tees4U t-shirt color and size combination is most popular, according to their heatmap, which uses red-yellow-green in ascending order?

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A) Blue XS B) Green M C) Purple M D) Yellow XL

33)

An easy way to convert relative frequencies into percentages is by dividing by 100. ⊚ true ⊚ false

34) When working with numerical variables, the frequency distribution is equal to the number of observations that falls into each interval. ⊚ true ⊚ false

35) Constructing a contingency table allows for a clear visualization of the relationship between two categorical variables. ⊚ true ⊚ false

36) In a scatter plot diagram, if there is no discernable pattern, then there is a positive relationship between the numerical variables. ⊚ true ⊚ false

37) A scatterplot with a categorical variable allows for the dynamic view of the addition of a categorical variable to the numeric plot points adding an additional layer of visible detail. ⊚ true ⊚ false

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38) A study focused on the following numerical variables: Age, Income, and Candy (lbs.). A bubble chart will help the researcher understand the relationship based on the location of the age and income plots and the size of the bubble based on candy consumption. ⊚ true ⊚ false

39)

A line chart displays the numerical variable of a series of data points connected by a line. ⊚ true ⊚ false

40) A bar chart is a series of rectangles where the width and height of each rectangle represent the interval width and frequency (or relative frequency) of the respective interval for numerical variables. ⊚ true ⊚ false

41)

Stacked column charts can be used to visualize more than one categorical variable. ⊚ true ⊚ false

42) Heat maps are useful in identifying combinations of the numerical variables that have economic significance. ⊚ true ⊚ false

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Answer Key Test name: ch 4 1) A 2) A 3) B 4) C 5) C 6) A 7) C 8) C 9) D 10) B 11) D 12) B 13) A 14) C 15) C 16) D 17) A 18) A 19) B 20) B 21) A 22) C 23) C 24) A 25) D 26) D Version 1

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27) A 28) B 29) C 30) B 31) B 32) B 33) FALSE 34) TRUE 35) TRUE 36) FALSE 37) TRUE 38) TRUE 39) TRUE 40) FALSE 41) TRUE 42) FALSE

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Student name:__________ 1)

What is the probability when a personal assessment is made without referencing data?

A) empirical probability B) classical probability C) subjective probability D) exclusive probability

2)

The union of two events is denoted as A) A ⊂ S. B) A ∪ B. C) P(A ∪ B).

D) A ∩ B.

3) Which rule is being followed when summing P(A) and P(B) then subtracting P(A ∩ B) from the sum?

A) complement B) multiplication C) addition D) joint probability

4)

Mutually exclusive events __________.

A) have joint probability of zero B) contain the multiple of two probabilities C) are conditional on interest D) contain all possible experiment outcomes

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5) Using conditional probability, if P(A) = 0.60, P(B) = 0.20, and P(A ∩ B) = 0.13, then P(A ∣ B) = A) 1.50.

B) 0.35.

C) 0.65.

D) 0.13.

6) Using conditional probability, if P(A) = 0.50, P(B) = 0.20, and P(A ∩ B) = 0.15, then P(A ∣ B) = A) 0.40.

B) 0.30.

C) 0.75.

D) 2.5.

7) In Holland, 68% of the people own a car. If five adults are randomly selected, what is the probability that none of the five have a car?

A) 7.12% B) 0.34% C) 39.32% D) 0.35%

8) In Holland, 60% of the people own a car. If five adults are randomly selected, what is the probability that none of the five have a car?

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A) 7.8% B) 1.02% C) 40% D) 1.03%

9) In Holland, 30% of the people own a car. If five adults are randomly selected, what is the probability that no more than two own a car?

A) 37.2% probability that more than two own a car.

B) 30.9% probability that more than two own a car.

C) 66.8% probability that no more than two own a car.

D) 83.7% probability that no more than two own a car.

10)

A simple event __________.

A) contains unlimited outcomes B) contains exactly two outcomes C) contains only subsets of the outcome D) contains a single outcome

11) Michael has interviewed for two jobs. He feels that he has a 58% chance of getting an offer on Job A and a 62% chance of getting an offer on Job B. He also believes there is a 55% chance of getting an offer on both jobs. What is the probability that he receives an offer on at least one of the jobs?

A) 0.70 B) 0.25 C) 0.10 D) 0.65

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12) Michael has interviewed for two jobs. He feels that he has a 65% chance of getting an offer on Job A and a 45% chance of getting an offer on Job B. He also believes there is a 40% chance of getting an offer on both jobs. What is the probability that he receives an offer on at least one of the jobs?

A) 0.60 B) 0.25 C) 0.10 D) 0.70

13) Michael has interviewed for two jobs. He feels that he has a 65% chance of getting an offer on job A and a 45% chance of getting an offer on job B. He also believes there is a 40% chance of getting an offer on both jobs. What is the probability that he does not get an offer at either job?

A) 0.10 B) 0.30 C) 0.70 D) 0.25

14) Two events are __________ if the occurrence of one event does not affect the probability occurrence of another.

A) simple B) dependent C) independent D) comparative

15)

Which is not a characteristic of the normal distribution?

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A) It is bell-shaped.

B) It is inverse.

C) It is asymptotic.

D) It is symmetric.

16) Simone, owner of the Blue Canoe Coffee Shop, ran a report showing the identified valued customer visits the shop on average 24 times in a 30-day period. Simone now wants to break down the information further to determine how many visits she should expect in a 20-day period from a tracked value customer. How many visits should Simone expect?

A) 16 B) 3 C) 5 D) 1

17) Simone, owner of the Blue Canoe Coffee Shop, ran a report showing the identified valued customer visits the shop on average 18 times in a 30-day period. Simone now wants to break down the information further to determine how many visits she should expect in a 5-day period from a tracked value customer. How many visits should Simone expect?

A) 3 B) 5 C) 1 D) 4

18) Which of the following Excel formulas will calculate the probability the percentage of returns will be greater than 17% for a balanced portfolio that historically earned 7% with a standard deviation of 10%?

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A) =NORM.DIST (17,7,10,FALSE) B) =NORM.DIST (17,7,10,TRUE) C) =1 − NORM.DIST (17,7,10,FALSE) D) =1 − NORM.DIST (17,7,10,TRUE)

19) Which of the following R formulas will calculate the probability the percentage of returns will be between 5% and 15% for a balanced portfolio that historically earned 7% with a standard deviation of 10%?

A) pnorm(15,7,10, lower.tail=TRUE) - pnorm(5,7,10, lower.tail=TRUE) B) pnorm(5,7,10, lower.tail=TRUE) - pnorm(15,7,10, lower.tail=TRUE) C) pnorm(15,7,10, lower.tail=FALSE) - pnorm(5,7,10, lower.tail=FALSE) D) pnorm(5,7,10, lower.tail=FALSE) - pnorm(15,7,10, lower.tail=FALSE)

20) The Daytona 500 runs 40 race cars. Of the 40, 19 cars crashed. This is a probability of 0.475 that a car will crash in the race. This is an example of which probability?

A) subjective B) empirical C) classical D) random

21)

Which is the best probability to determine the outcome of rolling seven with two dice?

A) subjective B) empirical C) classical D) random

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22)

Which one does not satisfy the Poisson process?

A) Success is presented as an integer between zero and infinity.

B) Number of successes counted in nonoverlapping intervals are independent.

C) The interval is the same for probability failure as in success in exceeding the size of the interval.

D) Probability of success in an interval is the same for all intervals of equal size and proportionality.

23)

Events are considered __________ if they include all outcomes in the sample space.

A) posterior B) exhaustive C) a sample D) a rule

24)

The intersection of two events is denoted as __________.

A) A ∩ B B) A ∪ B C) P (A ∪ B)² D) P (Ac) = 1 − P(A)

25) Marketing analysis determined 50% of females between the ages of 25 and 34 years search for green technology and practice being green, as compared to 33% of men in the same age group. What is the probability that a randomly selected woman between the age of 25 and 34 does not search for green technology?

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A) 12% probability B) 56% probability C) 50% probability D) 44% probability

26) Marketing analysis determined 44% of females between the ages of 25 and 34 years search for green technology and practice being green, as compared to 32% of men in the same age group. What is the probability that a randomly selected woman between the age of 25 and 34 does not search for green technology?

A) 12% probability B) 68% probability C) 56% probability D) 44% probability

27) Marketing analysis determined 48% of females between the ages of 25 to 34 years old search for green technology and practice being green, as compared to 36% of men in the same age group. What is the probability that a randomly selected man between the age of 25 and 34 does not search for green technology?

A) 56% probability B) 64% probability C) 12% probability D) 68% probability

28) Marketing analysis determined 44% of females between the ages of 25 to 34 years old search for green technology and practice being green, as compared to 32% of men in the same age group. What is the probability that a randomly selected man between the age of 25 and 34 does not search for green technology?

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A) 44% probability B) 68% probability C) 32% probability D) 56% probability

29) The __________ of the discrete random variable X, denoted by E(X), or simply μ, is a weighted average of all possible values of X. A) summary value B) corresponding value C) random value D) expected value

30) Howard Simpson at Organics Central Market ran a report showing the identified valued customer visits the market on average 20 times in a 50-day period. Howard now wants to break down the information further to determine how many visits he should expect in a 5-day period from a tracked value customer. What is the probability of a valued customer visiting all 5 days?

A) 4% B) 10% C) 8% D) 9%

31) Howard Simpson at Organics Central Market ran a report showing the identified valued customer visits the market on average 18 times in a 30-day period. Howard now wants to break down the information further to determine how many visits he should expect in a 5-day period from a tracked value customer. What is the probability of a valued customer visiting all 5 days?

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A) 10% B) 8% C) 4% D) 9%

32) Which theorem can the posterior probability be found using the prior probability and conditional probability?

A) Fisher B) Poisson C) Bernoulli D) Bayes’

33) Andrea decided her job opportunities will increase conditional on completing her bachelor’s degree. Based on her assumption, what probability would best fit?

A) classical probability B) conditional probability C) complement rule D) empirical probability

34) Based on the provided table, the expected employee bonus is 1.90 or 1,900. What is the variance and the standard deviation of the annual bonus amount? xi 6 3 5

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P (X = xi) 0.20 0.15 0.05

x1P (X = xi) 1.20 0.45 0.25 Total = 1.90

(xi − μ)2 P (X = xi) ? ? ?

Total = ?

10

A) σ = 9.36 = 3.059 B) σ = 15.13 = 3.890 C) σ = 9.85 = 3.138 D) σ = 4.03 = 2.007

35) Based on the provided table, the expected employee bonus is 4.95 or 4,950. What is the variance and the standard deviation of the annual bonus amount? xi 8 5 0

P (X = xi) 0.40 0.35 0.25

x1P (X = xi) 3.2 1.75 0 Total = 4.95

(xi − μ)2 P (X = xi) ? ? ?

Total = ?

A) B) C) D)

36) Tiffany Ham’s business is thriving in Houston, TX. To reward her team, Tiffany is implementing a performance incentive program. Annual Bonuses begin at $5,000 for excellent performance, $3,000 for good performance, and $1,500 for fair performance, and $0 for poor performance. The probability levels are 0.20, 0.25, 0.25, and 0.05, respectively. What is the expected value of the annual bonus amount for an employee?

A) $2,300 B) $2,325 C) $2,125 D) $2,375

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37) Tiffany Ham’s business is thriving in Houston, TX. To reward her team, Tiffany is implementing a performance incentive program. Annual Bonuses begin at $5,000 for excellent performance, $3,000 for good performance, and $1,500 for fair performance, and $0 for poor performance. The probability levels are 0.15, 0.40, 0.25, and 0.20, respectively. What is the expected value of the annual bonus amount for an employee?

A) $1,875 B) $2,300 C) $2,325 D) $2,375

38) In reviewing retirement portfolios, Kim determined the probability of a client owning stock is 0.70 and the probability of owning a bond is 0.20. The probability of a customer who owns bonds already owning stock is 0.60. What is the probability a client owns both securities in their retirement portfolio?

A) 0.52 B) 0.30 C) 0.39 D) 0.42

39) In reviewing retirement portfolios, Kim determined the probability of a client owning stock is 0.70 and the probability of owning a bond is 0.40. The probability of a customer who owns bonds already owning stock is 0.55. What is the probability a client owns both securities in their retirement portfolio?

A) 0.52 B) 0.30 C) 0.40 D) 0.39

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40) Alison has been hired to sell two different homes on the same street that two houses apart. She predicts that Home A has a 73% chance in selling on the first week of being listed, whereas Home B is in lesser condition and has a 33% probability. There is also a 16% chance both homes will not sell on the first week of it being listed. What is the probability that house A does not sell given that house B does not sell due to its poor condition?

A) 0.267 B) 0.239 C) 0.250 D) 0.700

41) Alison has been hired to sell two different homes on the same street that two houses apart. She predicts that Home A has a 75% chance in selling on the first week of being listed, whereas Home B is in lesser condition and has a 30% probability. There is also a 20% chance both homes will not sell on the first week of it being listed. What is the probability that house A does not sell given that house B does not sell due to its poor condition?

A) 0.267 B) 0.286 C) 0.250 D) 0.700

42)

Find P(A|B) using the following table of probabilities.

Prior Probability P(A) = 0.70 P(AC) = 0.30

Conditional Probability P(B|A) = 0.90 P(B|AC) = 0.05

Joint Probability P(B∩A) = 0.630 P(B∩AC) = 0.015

A) 0.397 B) 0.645 C) 0.950 D) 0.977

43)

Discrete random variables are NOT associated with which of the following items?

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A) a probability that the random variable X assumes a particular value x B) the sum of all probabilities is equal to or greater than 1 C) can be defined in terms of their cumulative distribution function D) a probability that each value of x is 0 ≤ P(X = xi) ≤ 1

44) In 2020, 63% of high school graduates were enrolled in colleges or universities. If five high school graduates are randomly selected, what is the probability that no more than three are enrolled in college or university?

A) 39.1% B) 34.2% C) 60.9% D) 63.0%

45) In 2020, 63% of high school graduates were enrolled in colleges or universities. If five high school graduates are randomly selected, what is the probability of having three that are enrolled in college or university?

A) 20.1% B) 34.2% C) 60.9% D) 63.0%

46) How many defects should Umair at East Side Manufacturing expect out of 20 manufactured items if the plant usually identifies 3 defects in every 15 items manufactured.

A) 3 B) 4 C) 5 D) 6

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47)

The sample space contains all probable outcomes of an experiment. ⊚ true ⊚ false

48) If 42% of interns are between the ages of 20 to 25, the complement rule dictates P(Ac) = 1 − P(A) = 1 − 0.42 = 0.58. ⊚ true ⊚ false

49) B.

The union of two events A and B, denoted as A ∪ B, will contain outcomes of both A and ⊚ ⊚

true false

50) According to the total probability rule, P(A) equals the sum of P(A ∩ B) and P(A ∩ Bc), and is considered conditional on two mutually exclusive and exhaustive events independent of an experiment. ⊚ true ⊚ false

51)

Bayes’ theorem is a procedure for updating probabilities based on new information. ⊚ true ⊚ false

52)

A discrete random variable is denoted as distinct countable values x1, x2, x3,… ⊚ true ⊚ false

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53) The expected value of the discrete random variable X, denoted by simply μ, is also referred as the maximum of all possible values of X. ⊚ true ⊚ false

54) A result of attaching probabilities to the outcomes of a Bernoulli process is called a binomial distribution. ⊚ true ⊚ false

55) The Poisson process is satisfied only if the number of successes counted in nonoverlapping intervals is independent and is not dependent on the proportional size of an interval. ⊚ true ⊚ false

56) The Gaussian distribution is the most extensively used probability distribution in statistical work. ⊚ true ⊚ false

57) Bayes’ Theorem cannot be extended to include n, mutually exclusive and exhaustive events. ⊚ true ⊚ false

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Answer Key Test name: ch 5 1) C 2) B 3) C 4) A 5) C 6) C 7) B 8) B 9) D 10) D 11) D 12) D 13) B 14) C 15) B 16) A 17) A 18) D 19) A 20) B 21) C 22) C 23) B 24) A 25) C 26) C Version 1

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27) B 28) B 29) D 30) A 31) A 32) D 33) B 34) D 35) D 36) C 37) C 38) D 39) D 40) B 41) B 42) D 43) B 44) C 45) B 46) B 47) TRUE 48) TRUE 49) TRUE 50) FALSE 51) TRUE 52) TRUE 53) FALSE 54) TRUE 55) FALSE 56) TRUE Version 1

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57) FALSE

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Student name:__________ 1) <p>If the average derived from a specific sample is $43,000, then = 43,000 is the _____________ of the population mean.

A) deviation B) estimate C) point D) static point

2) In a population set, 19 students of the 143 observations received a failing grade in the Accounting Principles course. What is the estimate of the sample proportion of failure in the course?

A) 0.1329 B) 7.526 C) 0.7186 D) 0.0133

3) In a population set, 13 students of the 132 observations received a failing grade in the Accounting Principles course. What is the estimate of the sample proportion of failure in the course?

A) 0.0985 B) 10.154 C) 0.6842 D) 0.0099

4) Cameron is the quality inspector for the Mason Pot Company, an artesian cooperative crafting bowls. In the smaller series, the mean diameter is5 inches with a standard deviation of 0.4 inch. First, find the expected value. Then answer: what is the standard error of the sample mean derived from a random sample of 19 bowls?

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A) 0.092 B) 0.9 C) 0.002 D) 0.19

5) Cameron is the quality inspector for the Mason Pot Company, an artesian cooperative crafting bowls. In the smaller series, the mean diameter is4 inches with a standard deviation of 0.2 inch. First, find the expected value. Then answer: what is the standard error of the sample mean derived from a random sample of 12 bowls?

A) 0.058 B) 0.58 C) 0.001 D) 0.12

6)

Megan ran an analysis with a population standard deviation σ = 0.90. When reviewing

sample sizes of 4 and 8, the standard error for the sample mean was = 0.45 and = 0.32 respectively. Doing a comparison, what does the expected value and the standard error of the sample mean reflect?

A) By comparison, the results confirm the sample is normally distributed with increased variability.

B) The standard error for the sample mean is lower confirming the initial population standard deviation is incorrect.

C) By comparing, the results confirm that averaging reduces variability.

D) The standard error for the sample mean is lower, increasing variability in the sample set.

7)

The Central Limit Theorem, by definition, implies that as the sample size increases, the

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A) average of the large number of independent observations has an approximate normal distribution.

B) average of the large number of independent observations reflects the leftmost tail of the distribution.

C) expected underlining distribution is only justified when n ≤ 30.

D) expected normal distribution declines.

8)

What is the expected value of the sample proportion?

A) number of success B) sample population C) standard error D) population proportion

9) CNBC (2018) reported 68% of households are using streaming services compared to standard cable. What are the expected value and the standard error of the sample proportion derived from a random sample of 600?

A) EP¯ = 0.32 and seP¯ = 0.0190 B) EP¯ = 0.0190 and seP¯ = 0.32 C) EP¯ = 0.68 and seP¯ = 0.0190 D) EP¯ = 0.8615 and seP¯ = 0.0310

10) CNBC (2018) reported 58% of households are using streaming services compared to standard cable. What are the expected value and the standard error of the sample proportion derived from a random sample of 500?

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11)

A)

= 0.42 and <em>

</em> = 0.0221</p>

B)

= 0.0221 and <em>

</em> = 0.42</p>

C)

= 0.58 and <em>

</em> = 0.0221</p>

D)

= 0.7615 and <em>

</em> = 0.0341</p>

A range of values used to estimate a population parameter of interest is called A) estimated error.

B) confidence interval.

C) standard error.

D) probability of success.

12) In a meeting, Matt estimates the average miles per gallon (mpg) of the fleet cars is 35 mpg. With a certain level of confidence, he announces the actual mpg is in the 34 to 36 mpg range. What is the Margin of Error?

A) 2 mpg B) 0.50 mpg C) 35 mpg D) 1 mpg

13) In a meeting, Matt estimates the average miles per gallon (mpg) of the fleet cars is 24 mpg. With a certain level of confidence, he announces the actual mpg is in the 22 to 26 mpg range. What is the Margin of Error?

A) 4 mpg B) 0.50 mpg C) 24 mpg D) 2 mpg

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14)

If the confidence coefficient is 0.67, what is the implied probability of error α?

A) 0.33 B) 0.67 C) 1 D) 0.5

15) If the confidence coefficient is 0.85, what is the implied probability of error α? {MISSING IMAGE}

A) 0.15 B) 0.85 C) 1 D) 0.05

16) <p>In a sample of 37, the mean is = 92.01 with a standard deviation of s = 9.90.

Assuming the results follow a normal distribution, construct the 90% confidence interval for the population mean. The t Table = 1.688.

A) 90% confidence interval is 92.01 ± 2.45.

B) 90% confidence interval is 94.76.

C) 90% confidence interval is between 89.26 and 94.76.

D) 90% confidence interval is 89.56 ± 1.688.

17) <p>In a sample of 25, the mean is = 86.92 with a standard deviation of s = 9.87.

Assuming the results follow a normal distribution, construct the 90% confidence interval for the population mean. The t Table = 1.711.

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A) 90% confidence interval is 86.92 ± 3.68.

B) 90% confidence interval is 90.30.

C) 90% confidence interval is between 83.54 and 90.30.

D) 90% confidence interval is 86.82 ± 1.711.

18) <p>In a sample of 32, the mean is = 92.60 with a standard deviation of s = 7.07.

Assuming the results follow a normal distribution, construct the 90% confidence interval for the population mean. The t Table = 1.696.

A) 90% confidence interval is 92.60 ± 2.12.

B) 90% confidence interval is 2.12.

C) 90% confidence interval is between 90.31 and 94.89.

D) 90% confidence interval is 90.48 ± 1.70.

19) <p>In a sample of 60, the mean is = 76.29 with a standard deviation of s = 7. Assuming the results follow a normal distribution, construct the 90% confidence interval for the population mean. The t Table = 1.671.

A) 90% confidence interval is 76.29 ± 1.51.

B) 90% confidence interval is 77.97.

C) 90% confidence interval is between 74.61 and 77.97.

D) 90% confidence interval is 76.29 ± 1.684.

20) <p>In a sample of 12, and = 78, with a standard deviation of s = 7, the standard error of the sample mean is A) 1.099.

B) 2.021.

C) 2.140.

D) 3.132.

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21) <p>In a sample of 9, and <em> = 76, with a standard deviation of s = 5, the standard error of the sample mean is A) 0.745.

B) 1.667.

C) 1.786.

D) 2.778.

22) In a sample of 36, 10 were found to have over 200 Blu-Ray Discs in one location.

Construct the point estimate for the population proportion.

A) 0.56 B) 0.03 C) 0.14 D) 0.28

23) In a sample of 30, 9 were found to have over 200 Blu-Ray Discs in one location.

Construct the point estimate for the population proportion.

A) 0.60 B) 0.045 C) 0.15 D) 0.30

24) In a sample of 34 iPhones, 21 had over 94 apps downloaded. Construct a 90% confidence interval for the population proportion of all iPhones that obtain over 94 apps. Assume z0.05 = 1.645.

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A) 0.62 ± 0.14 B) 0.62 ± 0.09 C) 0.36 ± 0.13 D) 0.36 ± 0.14

25) In a sample of 25 iPhones, 12 had over 85 apps downloaded. Construct a 90% confidence interval for the population proportion of all iPhones that obtain over 85 apps. Assume z0.05 = 1.645.

A) 0.48 ± 0.16 B) 0.48 ± 0.09 C) 0.29 ± 0.15 D) 0.29 ± 0.16

26) In a sample of 39 iPhones, 11 had over 99 apps downloaded. Construct a 99% confidence interval for the population proportion of all iPhones that obtain over 99 apps. Assume z0.005 = 2.576.

A) 0.11 ± 0.19 B) 0.11 ± 0.09 C) 0.28 ± 0.18 D) 0.28 ± 0.19

27) In a sample of 50 iPhones, 13 had over 100 apps downloaded. Construct a 99% confidence interval for the population proportion of all iPhones that obtain over 100 apps.

Assume z0.005 = 2.576.

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A) 0.13 ± 0.16 B) 0.13 ± 0.09 C) 0.26 ± 0.15 D) 0.26 ± 0.16

28)

A Type II error occurs when A) the null hypothesis is rejected when the null hypothesis is true.

B) the null hypothesis is rejected, proving the hypothesis is true.

C) the null hypothesis is not rejected when the null hypothesis is false.

D) the null hypothesis is not rejected when the null hypothesis is true.

29) According to the National Retail Federation, the average shopper will spend $1,007.24 during the holiday shopping season. What is the null and alternate hypothesis?

A) Sample Population is needed to complete the hypothesis.

B) H0:μ ≠ 1007.24; HA : μ = 1007.24 C) H0:μ ≥ 1007.24; HA : μ ≤ 1007.24 D) H0:μ = 1007.24; HA : μ ≠ 1007.24

30) Peter provided an analysis for the building of a multi-unit retail strip for Corning Construction using the following hypothesis: H0:μ = Do not build the Strip Mall; Build the Strip Mall. Based on his calculations of the area, and the probabilities for growth, he shows the project will be profitable. The client built the strip mall to a profit loss. What type of error would this represent?

A) Type I error because the building was built.

B) Type II error because the building was built.

C) Type I error because the building was built, but it was not profitable.

D) Type II error because the building was built and was profitable.

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31)

Using R, what function is used to denote the hypothesized value of the mean?

A) alternative B) option conf.

C) t.test D) mu

32) In a sample of 420 students, 82% prefer online resources versus printed materials. The standard error for the sample proportion is A) 0.001.

B) 0.019.

C) 0.015.

D) 0.041.

33) In a sample of 500 students, 80% prefer online resources versus printed materials. The standard error for the sample proportion is A) 0.002.

B) 0.018.

C) 0.014.

D) 0.040.

34)

Which one of the following is not a characteristic of t distribution?

A) t distribution is defined by the degrees of freedom.

B) t distribution is bell-shaped.

C) t distribution is symmetric along zero.

D) t distribution is commonly known as population mean.

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35) According to the United States Census Bureau, the average single-family home constructed in 2016 was 2,438 square feet. Carmen wondered if families in her area still want a smaller home in comparison to the average. She randomly selected 45 people and asked what the ideal square footage of a home is for them. From the responses, she calculated the sample mean equals 1,550 square feet with a sample standard deviation of 54 square feet. What are the competing hypotheses?

A) H0:μ ≠ 2,438; HA : μ = 2,438 B) H0:μ ≠ 1,550; HA : μ = 1,550 C) H0:μ ≥ 2,438; HA : μ < 2,438 D) H0:μ = 1,550; HA : μ ≠ 1,550

36) According to the United States Census Bureau, the average single-family home constructed in 2016 was 2,463 square feet. Carmen wondered if families in her area still want a smaller home in comparison to the average. She randomly selected 49 people and asked what the ideal square footage of a home is for them. From the responses, she calculated the sample mean equals 1,640 square feet with a sample standard deviation of 66 square feet. Calculate the value of the test statistic.

A) −87.29 B) 87.52 C) −2.67 D) 1.62

37) According to the United States Census Bureau, the average single-family home constructed in 2016 was 2,438 square feet. Carmen wondered if families in her area still want a smaller home in comparison to the average. She randomly selected 45 people and asked what the ideal square footage of a home is for them. From the responses, she calculated the sample mean equals 1,550 square feet with a sample standard deviation of 54 square feet. Calculate the value of the test statistic.

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A) −110.31 B) 110.54 C) −2.45 D) 1.32

38)

If t34 = −4.322 and α = 0.05, then what is the exact p-value for a left-tailed test?

A) 6.375551e-05 B) 0.9999362 C) 0.95 D) 0.095

39)

What does α = 0.01 reflect?

A) There is a 1% chance of rejecting a true null hypothesis.

B) There is a 1% chance of accepting a true null hypothesis.

C) Without an identified μ, there is no significant meaning.

D) The 1% is the lowest significance level.

40) In a World Atlas study, 10% of people have blue eye color. Lane decided to observe 41 people and she concluded 6 people had blue eyes. Calculate the z-score.

A) 0.1463 B) 0.0460 C) 0.9891 D) 0.1480

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41) In a World Atlas study, 10% of people have blue eye color. Lane decided to observe 35 people and she concluded 5 people had blue eyes. Calculate the z-score.

A) 0.1428 B) 0.0430 C) 0.8452 D) 0.0041

42) In a 2019 Quinnipiac University poll of registered voters, 52% oppose making all U.S public colleges free. The Glangariff Group in Michigan collected data from 600 voters, where 342 support a taxpayer-funded free college program. What is the competing hypothesis?

A) H0:p = 0.52; HA:p ≠ 0.52 B) H0 = 0.51; HA ≠ 0.51 C) H0:p = 0.51; HA:p ≠ 0.51 D) H0:p = 0.57; HA:p ≠ 0.57

43) In a 2019 Quinnipiac University poll of registered voters, 54% oppose making all U.S public colleges free. The Glangariff Group in Michigan collected data from 540 voters, where 321 support a taxpayer-funded free college program. Calculate the value of the test statistic.

A) 2.18 B) 2.54 C) 12.11 D) 2.13

44) In a 2019 Quinnipiac University poll of registered voters, 52% oppose making all US public colleges free. The Glangariff Group in Michigan collected data from 600 voters, where 342 support a taxpayer-funded free college program. Calculate the value of the test statistic.

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A) 2.09 B) 2.45 C) 12.02 D) 2.04

45) A p-value of 0.07698 was reported for a study with a 10% significance level. Should the null hypothesis be accepted or rejected?

A) Accept the null hypothesis because the p-value = 0.07698 is part of α = 0.10 B) Accept the null hypothesis because the p-value = 0.07698 is less than α = 0.10 C) Reject the null hypothesis because the p-value = 0.07698 is less than α = 0.10 D) Not enough information to determine.

46) Which of the following represents the null hypothesis to test whether the health insurance premiums of men (μm) and women (μw) differ from each other?

A) H0: μm − μw = 0 B) H0: μm − μw ≠ 0 C) H0: μm − μw < 0 D) H0: μm − μw > 0

47) Jackie wanted to know if there was any difference between low-carb and low-fat diets.

Jackie ran a t-test to test the hypothesis for H0: μLC − μLF = 0 using experiment data she received from her nutrition professor. Given the results below, which of the statements is not true? t-Test: Two-Sample Asswning Unequal Variances Mean Variance Obsetvations Hypothesized Mean Difference

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Low-carb

Low-fat

9.33 2.9106 20 0

6.60 2.1552 20

14

elf tStat P(f<=t) one-tail t Critical one-tail P(f<=t) two-tail t Critical two-tail

37 5.4343 0.0000 1.6871 0.0000 2.0262

A) At theα = 0.05 level,H0 is rejected B) The test statistic value is 5.4343.

C) There is no difference between diets.

D) The two-tail test is used for this analysis.

48) Buster believes the new manufacturing process will result in assembly times that are onehalf minute faster than the previous process. Running 35 widgets through the old process and 30 widgets through the new process, Buster found the mean assembly time to be 32.6571 minutes (s.d. 1.924) and 32.1667 minutes (s.d. 1.949), respectively. Calculate the t-statistic for testingμnew-μold. Assume the population variances are unequal.

A) −2.055 B) −1.017 C) −0.019 D) 1.017

49) Buster believes the new manufacturing process will result in assembly times that are faster than the previous process. Running 35 widgets through the old process and 30 widgets through the new process, Buster found the mean assembly time to be 32.6571 minutes (s.d.

1.924) and 32.1667 minutes (s.d. 1.949), respectively. Calculate the t-statistic for testingμoldμnew. Assume the population variances are unequal.

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A) −2.055 B) −1.017 C) −0.019 D) 1.017

50) In a 2019 Quinnipiac University poll of registered voters, 52% oppose making all U.S public colleges free. The Glangariff Group in Michigan collected data from 600 voters, where 342 support a taxpayer-funded free college program. What is the null hypothesis to test whether these two proportions are different from each other at theα = 0.05 level?

A) H0: μD ≠ 0 B) H0: μD < 0 C) H0: μD > 0 D) H0: μD = 0

51) Calculate the test statistic used to determine whether the mean difference in monthly estimated sales and actual sales for the last three years (36 months), given the following information.

Average Sales Standard Deviation Sample size

Estimated

Actual

Difference

75972.2222 15882.5801 36

75725.0833 15974.4273 36

247.1389 1997.9809 36

A) −0.742 B) −0.066 C) 0.066 D) 0.742

52) Many sample sizes can be drawn from a population, but there is only one overall population.

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⊚ ⊚

true false

53) The value of the sample mean will remain static even when the data set from the population is changed. ⊚ true ⊚ false

54)

<p>The sampling distribution of is closely related to the normal distribution. ⊚ true ⊚ false

55) In general, a normal distribution approximation is justified for the sample proportion when np ≥ 5 and n (1 − p) ≥ 5. ⊚ true ⊚ false

56)

The more degrees of freedom, the broader the tails of the distribution. ⊚ true ⊚ false

57) With a df = 20, and α = 0.05 and a referencing table value of 1.74742, the upper tail suggests that P(T20 ≥ 1.74742) = 0.05. ⊚ true ⊚ false

58)

The population proportion p is the essential measure for a quantitative variable.

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⊚ ⊚

true false

59) An alternative hypothesis, denoted HA , is defined as the contradiction of the default state or status quo. ⊚ true ⊚ false

60)

When the p-value is greater than alpha, then the null hypothesis is rejected. ⊚ true ⊚ false

61) In performing a test statistic for p, the formula is only valid if a normal distribution. ⊚ true ⊚ false

(approximately) follows

62) Samples of men and women salaries are independent random samples if the process that generates one sample is completely separate from the process that generates the other sample. ⊚ ⊚

true false

63) When testing two population means based on samples that we believe arenot independent, one option is to use unmatched-pairs sample testing. ⊚ ⊚

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Answer Key Test name: ch 6 1) B 2) A 3) A 4) A 5) A 6) C 7) A 8) D 9) C 10) C 11) B 12) D 13) D 14) A 15) A 16) C 17) C 18) A 19) A 20) B 21) B 22) D 23) D 24) A 25) A 26) D Version 1

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27) D 28) C 29) D 30) C 31) D 32) B 33) B 34) D 35) C 36) A 37) A 38) A 39) A 40) C 41) C 42) A 43) B 44) B 45) C 46) A 47) C 48) A 49) B 50) D 51) D 52) TRUE 53) FALSE 54) FALSE 55) TRUE 56) FALSE Version 1

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57) TRUE 58) FALSE 59) TRUE 60) FALSE 61) TRUE 62) TRUE 63) FALSE

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Student name:__________

1) This represents which relationship?

A) Positive linear B) Negative linear C) No linear D) Multiple linear

2) The Department of Natural Resources conducted a study examining the condition of fish in the Wolfe River (Wisconsin). In total 110 fish were captured. The variables that were measured are: mile marker of location in river, species (rainbow trout, northern pike, musky, walleye, andpanfish), length, and weight. Which item is not numerical?

A) amount of fish captured B) length C) species D) mile marker

3) In a simple linear regression based on 20 observations, it is found b1 = 3.15 and se(b1) = 1.05. Consider the hypothesis : H0 : β1 = 0 and HA : β1 ≠ 0 . Calculate the value of the test statistic.

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A) 3.00 B) 0.333 C) 1.420 D) 0.121

4) In a simple linear regression based on 20 observations, it is found b1 = 3.25 and se(b1) = 1.35. Consider the hypothesis: H0 : β1 ≠ 0 and HA : β1 ≠ 0 . Calculate the value of the test statistic.

A) 2.41 B) 0.415 C) 1.42 D) 0.121

5) In the following equation ŷ = 20,000 + 5x with given sales (γ in dollars) and marketing (x in $500), what does the equation imply?

A) An increase of one unit in marketing is associated with an increase of $22,500 in sales.

B) An increase of one unit in marketing is associated with an increase of $2,500 in sales.

C) An increase of five units in marketing is associated with an increase of $2,500 in sales.

D) An increase of five units in marketing is associated with an increase of $22,500 in sales.

6) In the following equation ŷ = 30,000 + 4x with given sales (γ in dollars) and marketing (x in $500), what does the equation imply?

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A) An increase of one unit in marketing is associated with an increase of $32,000 in sales.

B) An increase of one unit in marketing is associated with an increase of $2,000 in sales.

C) An increase of four units in marketing is associated with an increase of $2,000 in sales.

D) An increase of four units in marketing is associated with an increase of $32,000 in sales.

7) What is the relationship between the predictor variables and the response variable called when the omission of various relevant factors can influence the response variable?

A) deterministic B) independent C) regressive D) stochastic

8)

If R2 = 0.48, then how much of the sample variation is y?

A) 31.0% B) 38.0% C) 62% D) 48%

9)

If R2 = 0.62, then how much of the sample variation is y?

A) 31% B) 7.8% C) 38% D) 62%

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10)

If SST = 2,000 and SSE = 575, then the coefficient of determination is A) 0.77.

B) 0.23.

C) 0.71.

D) 0.57.

11)

If SST = 2,500 and SSE = 575, then the coefficient of determination is A) 0.43.

B) 0.23.

C) 0.77.

D) 0.57.

12)

If the coefficient of the determination is 0.50, what is the percent of the R2?

A) 50% B) 55% C) −0.50 D) −0.55

13)

If the coefficient of the determination is 0.60, what is the percent of the R2?

A) 60% B) 40% C) −0.60 D) −0.40

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14)

In a linear regression, ε, read as epsilon, is A) a dummy variable.

B) the unrounded number.

C) the random error.

D) the relationship between variables.

15)

In a linear regression model, the competing hypotheses take all but which form?

A) H0:βj = βj0 and HA:βj ≠ βj0 B) H0:βj ≤ βj0 and HA:βj > βj0 C) H0:βj ≥ βj0 and HA:βj < βj0 D) H0:βj > βj0 and HA:βj ≠ βj0

16)

The slope coefficient β, is called A) regression.

B) beta.

C) alpha.

D) intercept.

17)

Which of the following is not a goodness-of-fit measure?

A) adjusted coefficient of determination B) coefficient of determination C) standard error of the estimate D) simple regression model

18) When determining if there is evidence of a linear relationship between variables, OLS estimators must be _____________ for the test to be valid.

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A) normally distributed B) scattered C) a single variable D) predicted

19)

Based on the following table, what is the sample regression equation?

Intercept Cost Grad Debt

Coefficients

Standard Error

t Stat

p-value

10,789.9253 0.1211 163.0168 159.0355

7,623.4613 0.0730 80.9780 110.6640

1.311 3.917 2.574 1.207

0.1927 0.0002 0.0114 0.2300

A) Earnings = 10,789.9253 + 0.1211Cost + 163.0168Grad + 159.0355Debt B) Earnings = 10,789.9253 − 0.121Cost + 163.0168Grad + 159.036Debt C) Earnings = 10,789.9253 − 0.121Cost + 163.0168Grad − 159.036Debt D) Earnings = 10,789.9253 + 0.121Cost + 163.0168Grad − 159.036 Debt

20)

Based on the following table, what is the sample regression equation?

Intercept Cost Grad Debt

Coefficients

Standard Error

t Stat

p-value

10,004.9665 0.4349 178.0989 141.4783

7,634.3338 0.1110 69.1940 117.2120

1.311 3.917 2.574 1.207

0.1927 0.0002 0.0114 0.2300

A) Earnings = 10,004.9665 + 0.4349Cost + 178.0989Grad + 141.4783Debt B) Earnings = 10,004.9665 − 0.4349Cost + 178.0989Grad + 141.4783Debt C) Earnings = 10,004.9665 + 0.4349Cost + 178.0989Grad − 141.4783Debt D) Earnings = 10,004.9665 − 0.4349Cost + 178.0989Grad − 141.4783 Debt

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21) If there is a 30,000 average Service Cost in marketing services, with a 70% Cost Increase and 60% of client Payment for services upfront in addition to advantages of City, the predicted annual earnings for the firm are: Intercept Service Cost Cost Increase Payment City

Coefficients

Standard Error

t Stat

p-value

10,597.4751 0.4292 175.5073 136.5302 2,778.4538

7,575.3077 0.1106 69.1755 116.4507 1,098.6256

1.399 3.879 2.537 1.172 2.529

0.1646 0.0002 0.0126 0.2435 0.0128

A) $43,950.80.

B) $35,758.99.

C) $34,591.26.

D) $46,729.25.

22) If there is a 30,000 average Service Cost in marketing services, with a 70% Cost Increase and 60% of client Payment for services upfront in addition to advantages of City, the predicted annual earnings for the firm are: Intercept Service Cost Cost Increase Payment City

Coefficients

Standard Error

t Stat

p-value

10,004.9665 0.4349 178.0989 141.4783 2,526.7888

7,634.3338 0.1110 69.1940 117.2120 1103.4026

1.311 3.917 2.574 1.207 2.290

0.1927 0.0002 0.0114 0.2300 0.0239

A) $44,007.59.

B) $35,518.89.

C) $34,351.16.

D) $46,534.38.

23)

If SSE = 160 and SSR = 340, then the coefficient of determination is

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A) 0.20.

B) 0.68.

C) 0.67.

D) 0.40.

24)

If SSE = 200 and SSR = 300, then the coefficient of determination is A) 0.20.

B) 0.60.

C) 0.67.

D) 0.40.

25) If ŷ = 50 − 7x with y = product and x = price of product, what happens to the demand if the price is increased by 2 units?

A) increases by 49 units B) increases by 14 units C) decreases by 14 units D) decreases by 49 units

26) If ŷ = 110 − 5x with y = product and x = price of product, what happens to the demand if the price is increased by 3 units?

A) increases by 15 units B) increases by 125 units C) decreases by 15 units D) decreases by 95 units

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27) The standard error of the estimate is Se = the sample data?

. What result best fits

A) when Se = 1.526 B) when Se = 2.001 C) when Se = 0.543 D) when Se = 0.005

28) Based on goodness-of-fit measures, which is the preferred model based on the results below?

Standard error of the estimate Se Coefficient of determination R2 Adjusted R2

Model 1

Model 2

Model 3

6,498.4419

5,946.7042

5,789.4569

0.2934

0.4023

0.4288

0.2902

0.3890

0.4187

A) Model 1 B) Model 2 C) Model 3 D) Both Model 1 and Model 2

29) In the goodness-of-fit measures, interpret the coefficient of determination for Earnings with Model 3 and what the sample variation of Earnings explains.

Standard error of the estimate Se Coefficient of determination R2 Adjusted R2

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Model 1

Model 2

Model 3

6,637.5388

5,527.6696

5,676.6253

0.6835

0.7620

0.7078

0.4389

0.5125

0.1808

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A) 0.8475% of the sample variation in Earnings is explained by the model selection.

B) 0.0100 of the sample variation in Earnings determines the model selection.

C) 18.08 of the sample variation in Earnings determines the regression model.

D) 70.78% of the sample variation in Earnings is explained by the regression model.

30) In the goodness-of-fit measures, interpret the coefficient of determination for Earnings with Model 3 and what the sample variation of Earnings explains.

Standard error of the estimate Se Coefficient of determination R2 Adjusted R2

Model 1

Model 2

Model 3

6,498.4419

5,946.7042

5,789.4569

0.2934

0.4023

0.4288

0.2902

0.3890

0.4187

A) 41.87% of the sample variation in Earnings is explained by the regression model.

B) 0.8475 of the sample variation in Earnings determines the model selection.

C) 0.010 of the sample variation in Earnings determines the model selection.

D) 42.88% of the sample variation in Earnings is explained by the regression model.

31) Based on goodness-of-fit measures, what is the percentage of the sample variation unexplained by Model 2?

Standard error of the estimate Se Coefficient of determination R2 Adjusted R2

Model 1

Model 2

Model 3

6,366.0057

5,406.2443

5,634.9908

0.5297

0.8722

0.9332

0.6303

0.8575

0.9743

A) 87.22% B) 85.75% C) 97.43% D) 12.78%

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32) Based on goodness-of-fit measures, what is the percentage of the sample variation unexplained by Model 2?

Standard error of the estimate Se Coefficient of determination R2 Adjusted R2

Model 1

Model 2

Model 3

6,498.4419

5,946.7042

5,789.4569

0.2934

0.4023

0.4288

0.2902

0.3890

0.4187

A) 38.90% B) 40.23% C) 41.87% D) 59.77%

33) Conduct a test to determine if the predictor variables are jointly significant in explaining Earnings at α = 0.05.

ANOVA

df

SS

MS

F

Regression Residual

1 18

43,515,315.94 10,339,427.93

43,515,315.94 574,412.663

75.756

Total

19

283,587,703.60

Significance F 0.000

A) P(F(1,18) ≥ 75.756), p-value is less than α = 0.05, we reject H0, and the predictor variables are jointly significant in explaining Earnings.

B) P(F(1,19) ≥ 85.468), p-value is less than α = 0.05, we accept H0, and the predictor variables do not jointly explain Earnings.

C) P(F(1,18) ≥ 75.756), p-value is less than α = 0.05, we accept H0, and the predictor variables are jointly significant in explaining Earnings.

D) P(F(1,19) ≥ 85.468), p-value is less than α = 0.05, we reject H0, and the predictor variables jointly do not explain Earnings.

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34) Conduct a test to determine if the predictor variables are jointly significant in explaining Earnings at α = 0.05.

ANOVA

df

SS

MS

F

Regression Residual

1 18

44182633.37 13774291.07

44182633.37 765238.393

57.737

Total

19

57956924.44

Significance F 0.000

A) P(F(1,18) ≥ 57.737), p-value is less than α = 0.05, we reject H0, and the predictor variables are jointly significant in explaining Earnings.

B) P(F(1,19) ≥ 60.945), p-value is less than α = 0.05, we accept H0, and the predictor variables do not jointly explain Earnings.

C) P(F(1,18) ≥ 57.737), p-value is less than α = 0.05, we accept H0, and the predictor variables are jointly significant in explaining Earnings.

D) P(F(1,19) ≥ 60.945), p-value is less than α = 0.05, we reject H0, and the predictor variables jointly do not explain Earnings.

35) Camber Seal is a financial planner hired to review KMB stock. She is considering the CAPM where the KMB risk-adjusted stock returnR −Rf is used as the response variable and the risk-adjusted market returnRm −Rf is used as the predictor variable. KMB stock is considered staple products, whether the economy is good or bad. Given estimates for the beta coefficient is 0.7157, standard error of 0.1067, and ap-value of 0.045 with a formulated hypothesis ofH0 : β ≥ 1HA :β < 1. At a 5% significance level, what is the risk determination of the stock against the market?

A) β is significantly less than one, thus, H0 : is rejected and less risky than the market.

B) β is significantly higher than one, thus, H0 : is accepted and less risky than the market.

C) β is significantly less than one, thus, H0 : is accepted and riskier than the market.

D) β is significantly higher than one, thus, H0 : is rejected and riskier than the market.

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36) Camber Seal is a financial planner hired to review KMB stock. She is considering the CAPM where the KMB risk-adjusted stock returnR −Rf is used as the response variable and the risk-adjusted market returnRm −Rf is used as the predictor variable. KMB stock is considered staple products, whether the economy is good or bad. Given estimates for the beta coefficient is 0.7503, standard error of 0.1391, and ap-value of 0.039 with a formulated hypothesis ofH0 : β ≥ 1HA :β < 1. At a 5% significance level, what is the risk determination of the stock against the market?

A) β is significantly less than one, thus,H0 : is rejected and less risky than the market.

B) β is significantly higher than one, thus,H0 : is accepted and less risky than the market.

C) β is significantly less than one, thus, H0 : is accepted and riskier than the market.

D) β is significantly higher than one, thus, H0 : is rejected and riskier than the market.

37)

To conduct a test of joint significance, you want to employ which test?

A) regressed mean F test B) left-tailed F test C) double-tailed F test D) right-tailed F test

38) The simple linear regression model y = β0 + β1 x + ɛ implies that if x _____________, we expect y to change by β1, irrespective of the value of x.

A) goes up by one unit B) is a straight line C) curves by one unit D) goes down by one unit

39) Abe is calculating a stock investment risk. If the hypothesis is H0 : β ≥ 1 HA : β < 1, the p-value 0.027, and α = 0.05, is the investment riskier than the market?

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A) β is equal to one, so the investment is less risky than the market.

B) β is more than one, so the investment is riskier than the market.

C) β is less than one, so the investment is less risky than the market.

D) β is less than one, so the investment is riskier than the market.

40)

Which one of the following is not a common violation in the test of validity?

A) estimation B) multicollinearity C) changing variability D) nonlinear patterns

41) In a study where the least squares estimates were based on 34 sets of sample observations, the total sum of squares and regression sum of squares were found to be: SST = 4.46 and SSR = 4.22. What is the error sum of squares?

A) 1.07 B) 0.24 C) 0.320 D) 0.93

42) In a study where the least squares estimates were based on 34 sets of sample observations, the total sum of squares and regression sum of squares were found to be: SST = 4.53 and SSR = 4.21. What is the error sum of squares?

A) 1.07 B) 0.32 C) 0.929 D) 8.74

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43) It is important to review residual plots to identify any signs of _____________ and correlated observations in cross-sectional and time-series studies.

A) variable studies B) residual plot crosses C) changing variability D) standard error

44) In Excel, to construct a residual plot, input of a y range and an x range is needed. Aimee is examining the relationship between age and square foot range (sqft) of living space. In the scenario provided, what would be the y and what would be the x range data?

A) In selecting a regression, residual plot is the first selection before range input.

B) Input y range would be age and Input x range would be sqft.

C) Input y range would be blank to produce a concise x range.

D) Input y range would be sqft and Input x range would be age.

45) Using the 95% confidence interval data below, what is the mean net profit/loss range when x1 = $5 million and x2 = $3.5 million and the standard error is 0.2144?

Coefficient Standard s Error Intercep 0.62113 0.0273 t 2 x1new 0.08746 0.0122 4 x2new 0.11226 0.0108 0

t Stat

P-value

Lower 95% Upper 95%

22.7384 9 7.14528

0.0000 0 0.0000 0 0.0000 0

0.5669 1 0.0631 7 0.0908 2

10.3930 8

0.6753 5 0.1117 5 0.1336 9

A) $570,000 to $680,000 B) $190,000 to $1,050,000 C) $710,000 to $810,000 D) $340,000 to $1,190,000

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46) Using the 95% prediction interval data below, what is the mean net profit/loss range when x1 = $5 million and x2 = $3.5 million and the standard error is 0.2144?

Coefficient Standard s Error Intercep 0.62113 0.0273 t 2 x1new 0.08746 0.0122 4 x2new 0.11226 0.0108 0

t Stat

P-value

Lower 95% Upper 95%

22.7384 9 7.14528

0.0000 0 0.0000 0 0.0000 0

0.5669 1 0.0631 7 0.0908 2

10.3930 8

0.6753 5 0.1117 5 0.1336 9

A) $570,000 to $680,000 B) $190,000 to $1,050,000 C) $710,000 to $810,000 D) $340,000 to $1,190,000

47)

Which of the following statements about Adjusted R2 is false?

A) It is possible to increase Adjusted R2 unintentionally by including a predictor variable that has no foundation in the model.

B) Adjusted R2 explicitly accounts for the sample size n and the number of predictor variables k.

C) Adjusted R2 imposes a penalty for any additional predictor variable that is included in the analysis.

D) In models with the same response variable, the model with the higher Adjusted R2 is preferred because it explains more of the sample variation in y.

48) How many dummy variables will be created if the following four modes of transportation to work are captured: biking, public transportation, driving alone, and carpooling?

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A) 1 B) 2 C) 3 D) 4

49)

Regression analysis captures the relationship between only two distinct variables. ⊚ true ⊚ false

50) The response variable is the outcome of a variable, whereas the predictor is the input variable(s). ⊚ true ⊚ false

51) A regression model treats all predictor variables as numerical, where observations of a categorical variable are first converted into numerical values. ⊚ true ⊚ false

52)

R2 in linear regression is the correlation coefficient. ⊚ true ⊚ false

53) R2, also known as the coefficient of determination, quantifies the proportion of the sample variation in the predictor variables (xi) that is explained in the sample regression equation. ⊚ true ⊚ false

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54) The total sum of squares (SST) can be broken into two: explained variation and unexplained variation. ⊚ true ⊚ false

55) half.

When using Excel for a one-tailed test, the returned p-value will need to be divided in ⊚ ⊚

true false

56) When working with big data, a sample size is significantly large if the variability virtually disappears. ⊚ true ⊚ false

57) If the Ordinary Lease Squares (OLS) required assumptions of linear regression are met, OLS estimators of the regression coefficients βj are unbiased. ⊚ true ⊚ false

58) If residual plots exhibit strong nonlinear patterns, the inferences made by a linear regression model can be quite accurate. ⊚ true ⊚ false

59) For interval estimates for the response variable y, the prediction interval is narrower than the confidence interval.

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⊚ ⊚

true false

60) Adjusted R2 is used to compare competing linear regression models when the models have the same numbers of predictor variables. ⊚ true ⊚ false

61) While tables are often used to report regression results, a notes section below the table is recommended to explain any important notations. ⊚ true ⊚ false

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Answer Key Test name: ch 7 1) A 2) C 3) A 4) A 5) B 6) B 7) D 8) D 9) D 10) C 11) C 12) A 13) A 14) C 15) D 16) B 17) D 18) A 19) A 20) A 21) D 22) D 23) B 24) B 25) C 26) C Version 1

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27) D 28) C 29) D 30) D 31) D 32) D 33) A 34) A 35) A 36) A 37) D 38) A 39) C 40) A 41) B 42) B 43) C 44) B 45) A 46) B 47) A 48) C 49) FALSE 50) TRUE 51) TRUE 52) FALSE 53) FALSE 54) TRUE 55) TRUE 56) TRUE Version 1

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57) TRUE 58) FALSE 59) FALSE 60) FALSE 61) TRUE

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Student name:__________ 1) In a regression model, the _____________ exists when a predictor variable has a different partial effect on the outcome of another predictor variable.

A) target effect B) interaction effect C) dummy effect D) predictor effect

2) What is the predicted value (ŷ) when the numerical variable is x = 70 for the regression equation ŷ = −768 + 27.2x − 0.174x2?

A) 1,858.6 B) 1,528.5 C) 283.4 D) Not enough sufficient data to calculate the predicted value.

3) What is the predicted value (ŷ) when the numerical variable is x = 70 for the regression equation ŷ = −810 + 24.4x − 0.142x2?

A) 1822.2 B) 1593.8 C) 202.2 D) Not enough sufficient data to calculate the predicted value.

4) A variable with a value of x1x2 is added to the general linear regression model to account for two predictive variables, x1 and x2 and the effect on the response variable. This type of effect is called _____________.

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A) transformative B) dummy C) interaction D) predictive

5) Consider the sample correlation coefficients in the table below. How much of the variability in time can be explained by boxes using the alternative way of to determine the coefficient of determination?

Time

Boxes

Boxes

0.7552

Weight

0.6396

0.3517

Route

0.092

−0.1239

Weight

0.0110

A) 77% B) 26.6% C) 48.3% D) 57.0%

6) Consider the sample correlation coefficients in the table below. How much of the variability in time can be explained by boxes using the alternative way of to determine the coefficient of determination?

Time

Boxes

Boxes

0.7452

Weight

0.6572

0.3801

Route

0.082

−0.1615

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Weight

0.0114

2

A) 75% B) 25.5% C) 49.5% D) 55.5%

7) A study was completed on cholesterol in 100 male adults 40–60 years of age to determine if there is a relationship between cholesterol concentration and time spent watching TV. The researchers wanted to determine if there are any predictive results, such as if the amount of time spent watching TV increases or decreases cholesterol levels. Based on the following regression results, is TV-watch statistically significant?

Variable Constant TV_time Adjusted R2

Model 1

p−value −2.05169 0.048801 0.1280

0.0000 0.001

A) The p-value = 0.001 and is statistically significant because it is under the 5% level.

B) The p-value = 0.003 and is not statistically significant because it is under the 5% level.

C) The p-value = 0.000 and is statistically significant because it is under the 5% level.

D) The p-value = 0.000 and is not statistically significant because it is under the 5% level.

8) A study was completed on cholesterol in 100 male adults 40–60 years of age to determine if there is a relationship between cholesterol concentration and time spent watching TV. The researchers wanted to determine if there are any predictive results, such as if the amount of time spent watching TV increases or decreases cholesterol levels. Based on the following regression results, is TV-watch statistically significant?

Variable Constant TV_time Adjusted R2

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Model 1 −2.13478 0.044069 0.1426

p−value 0.000 0.002

3

A) The p-value = 0.002 and is statistically significant because it is under the 5% level.

B) Thep-value = 0.002 and is not statistically significant because it is under the 5% level.

C) The p-value = 0.000 and is statistically significant because it is under the 5% level.

D) The p-value = 0.000 and is not statistically significant because it is under the 5% level.

9) Consider a linear regression model where y represents the response variable and d1 and d2 represent two dummy variables. The model is estimated as ŷ = −2.95 + 1.83x + 4.70d1 − 2.79d2 + 0.89d1d2. Compute ŷ for x = 3, d1 = 1, and d2 = 0.

A) 14.25 B) 3.66 C) 12.01 D) 7.24

10) Consider a linear regression model where y represents the response variable and d1 and d2 represent two dummy variables. The model is estimated as ŷ = −2.53 + 2.04x + 4.20d1 − 2.86d2 + 0.68d1d2. Compute ŷ for x = 3, d1 = 1, and d2 = 0.

A) 13.53 B) 3.71 C) 12.85 D) 7.79

11) Sam, a marketing manager for XYZ big box stores, is trying to determine if there is a relationship between shelf space (in feet) and sales (in hundreds of dollars). To do this, Sam selected the top 12 producing locations. The regression results produced the following adjusted R2 values: Model 1: 0.8874 and Model 2: 0.6028. Which model is more suitable of a prediction?

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A) Model 1 B) Model 2 C) Both are suitable D) Neither model

12) Sam, a marketing manager for XYZ big box stores, is trying to determine if there is a relationship between shelf space (in feet) and sales (in hundreds of dollars). To do this, Sam selected the top 12 producing locations. Using the provided Model 1 results, what is the estimated equation on Sales?

Variable Constant Number of shelves Shelf Space Adjusted R2

Model 1 10.763 0.206 0.077 0.6413

p−value 0.0000 0.000 0.001

A) ^Sales = 10.763 + 0.206shelves + 0.077shelf_space B) ^Sales = −0.206shelves + 0.077shelf_space C) ^Sales = 0.206shelves + 0.077shelf_space D) ^Sales = 0.077shelf_space ÷ (2 × 0.206shelves)

13) Sam, a marketing manager for XYZ big box stores, is trying to determine if there is a relationship between shelf space (in feet) and sales (in hundreds of dollars). To do this, Sam selected the top 12 producing locations. Using the provided Model 1 results, what is the estimated equation on Sales?

Variable Constant Number of shelves Shelf Space Adjusted R2

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Model 1 10.365 0.211 0.074 0.6523

p−value 0.000 0.000 0.000

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A) ^Sales = 10.365 + 0.211shelves + 0.074shelf_space B) ^Sales = −0.211shelves + 0.074shelf_space C) ^Sales = 0.211shelves + 0.074shelf_space D) ^Sales = 0.074shelf_space ÷ (2 × 0.211shelves)

14) Sam, a marketing manager for XYZ big box stores, is trying to determine if there is a relationship between shelf space (in feet) and sales (in hundreds of dollars). To do this, Sam selected the top 12 producing locations. Using the provided Model 1 results, which option best interprets the impact of the coefficients and p-values?

Variable Constant Number of shelves Shelf Space Adjusted R2

Model 1 10.365 0.211 0.074 0.6523

p−value 0.000 0.000 0.001

A) The predictor variables are below one offering a negative correlation.

B) All predictor variables are positive with a significant influence on sales.

C) There is no impact presented with the coefficients and p-value results.

D) The p-value offers significant influence where the coefficient provides reduction on sales.

15) The estimate regression equation for the average cost of widgets is: ^Widgets = 7.15 − 0.2377 Output + 0.1886 Output2. Both predictor variables are statistically significant at 5% level, confirming the quadratic effect. What is the predictive average from an output level of 2 million units to 3 million units?

A) The increase in output units results in a $0.02 decrease in predictive average cost.

B) The increase in output units results in a $0.24 increase in predictive average cost.

C) The increase in output units results in a $0.70 decrease in predictive average cost.

D) The increase in output units results in a $0.70 increase in predictive average cost.

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16) The estimate regression equation for the average cost of widgets is: ^Widgets = 7.23 − 0.2478 Output + 0.1954 Output2. Both predictor variables are statistically significant at 5% level, confirming the quadratic effect. What is the predictive average from an output level of 2 million units to 3 million units?

A) The increase in output units results in a $0.02 decrease in predictive average cost.

B) The increase in output units results in a $0.24 increase in predictive average cost.

C) The increase in output units results in a $0.73 decrease in predictive average cost.

D) The increase in output units results in a $0.73 increase in predictive average cost.

17) Todd uses the quadratic regression model to determine the predictive average unit cost of baseballs produced in his production facility. After determining predictive average costs at multiple unit batch size amounts in millions, he now wants to know what the output level that maximizes his costs would be. Given b1 = −0.3654 and b2 = 0.0207, what is the level that will maximize his average cost in units?

A) 7.2 million units B) 2.7 million units C) 18.00 million units D) 8.83 million units

18) Todd uses the quadratic regression model to determine the predictive average unit cost of baseballs produced in his production facility. After determining predictive average costs at multiple unit batch size amounts in millions, he now wants to know what the output level that maximizes his costs would be. Given b1 = −0.3802 and b2 = 0.0198, what is the level that will maximize his average cost in units?

A) 7.5 million units B) 3.7 million units C) 19.20 million units D) 9.60 million units

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19) Ava Diego, a doctoral student, is researching car loans issued at a local bank. She prepared a sample of 200 to determine if there is a relationship between the loan amount, length of the loan, and interest rate provided. The regression results are in the table below. Which model is more suitable for prediction and what is the best fit reason?

Variable Constant Interest Rate Loan Length Interest × Loan Adjusted R2

Model 1 114.325 106.505

p-value 0.000 (0.000)

Model 2 110.54 108.650

p-value 0.000 (0.000)

0.2074

(0.000)

0.3290

(0.006)

NA

−0.1430

(0.0005)

0.2178

0.2089

A) Model 2 is the most suitable because of the p-value variance.

B) Model 1 is the most suitable because of the higher adjusted R2 value.

C) Model 2 is the most suitable because of the lower adjusted R2 value.

D) Neither provide enough results data to predict the model or reasoning.

20) An estimated linear regression of annual fuel expenditures y on annual income x is represented as the following equation: = 2,000 + 0.02x. What is the estimated slope coefficient value?

A) 0.02 B) 2b1 + 0.02 C) 40 D) 40x

21) An estimated linear regression of annual fuel expenditures y on annual income x is represented as the following equation: = 2,200 + 0.05x. What is the estimated slope coefficient value?

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A) 0.05 B) 2b1 + 0.05 C) 110 D) 110x

22)

An estimated linear regression of annual fuel expenditures y on annual income x is

represented as the following equation: = 2,200 + 0.05x. Jim was offered a new job that would increase his salary by $2,000. What would be his potential increase in fuel costs? Based on this information, is the assumption of increased fuel cost against annual salary meaningful?

A) The increase in fuel cost is $100.00 annually; linearity assumption is justified.

B) The increase in fuel cost is $100.00 annually; linearity assumption is not justified.

C) The increase in fuel cost is $50.00 annually; linearity assumption is justified.

D) The increase in fuel cost is $1000.00 annually; linearity assumption is not justified.

23) In the following logarithmic regression model, β1 × 0.01 measures the approximate unit change in E(y) when x increases. If β1 = 11,500, then what is the unit change in E(y)?

A) 11.5 units B) 1,150 units C) 115 units D) 11,500 units

24) In the following logarithmic regression model, β1 × 0.01 measures the approximate unit change in E(y) when x increases. If β1 = 6,000, then what is the unit change in E(y)?

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A) 6 units B) 600 units C) 60 units D) 6,000 units

25) In an exponential regression model, the exact percentage of change can be calculated as: (exp(β1) − 1) × 100. If β1 = 0.17, what is the percent increase in E(y)?

A) 25% B) 19% C) 28% D) 75%

26) In an exponential regression model, the exact percentage of change can be calculated as: (exp(β1) − 1) × 100. If β1 = 0.25, what is the percent increase in E(y)?

A) 25% B) 28% C) 75% D) 22%

27) In the model y = β0 + β1ln(x) + ε, the predicted value is = b0 + b1ln(x). What is the impact of the estimated slope coefficient?

A) b1 measures the approximate change in when x increases by 1 unit.

B) b1 × 0.01 measures the approximate change in when x increases by 1%.

C) b1 measures the approximate change in when x increases by 1%.

D) b1 × 100 measures the approximate change in when x increases by 1 unit.

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28) In the model y = β0 + β1x + ε, the predicted value is = b0 + b1x. What is the impact of the estimated slope coefficient?

A) b1 measures the approximate change in when x increases by 1 unit.

B) b1 × 0.01 measures the approximate change in when x increases by 1%.

C) b1 measures the approximate change in when x increases by 1%.

D) b1 × 100 measures the approximate change in when x increases by 1 unit.

29) In the model ln(y) = β0 + β1x + ε, the predicted value is ( ) = exp(b0 + b1x + What is the impact of the estimated slope coefficient?

÷ 2).

A) b1 measures the approximate change in when x increases by 1 unit.

B) b1 × 0.01 measures the approximate change in when x increases by 1%.

C) b1 measures the approximate change in when x increases by 1%.

D) b1 × 100 measures the approximate change in when x increases by 1 unit.

30) 12.

<p>Consider the following quadratic model, = 21 + 1.50x − 0.25x2. Predict y when x =

A) 3 B) 40 C) 12 D) 9

31) 12.

<p>Consider the following quadratic model, = 25 + 1.5x − 0.25x2. Predict y when x =

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A) 40 B) 12 C) 9 D) 7

32) Why are the partial effects of the two predictor variables more difficult to interpret when a model contains the interaction of two numerical variables?

A) The partial effect of either predictor variable depends on the value of the other predictor variable.

B) Models with an interaction of two numerical variables is no more difficult to interpret than other interactions.

C) The partial effects can only be interpreted at the sample means of x1 and x2.

D) Comparing models with and without the interaction terms depends on the value ofR2.

33) Christian Za is using the holdout method to partition his data into two independent and mutually exclusive sets: 75% in a training set and 25% in a validation set. Based on an R2 value of 0.4532 and RMSE of 0.3268 for the training set and an R2 value of 0.1426 and RMSE of 0.5371 for the validation set, which of the competing models is the preferred model?

A) The validation data set would be the preferred model.

B) The training data set would be the preferred model.

C) Both models are preferred models.

D) There is not enough data to determine the preferred model.

34) Compute givenx1 = 25 withx2 evaluated at 5 where =−97.2850 + 5.5416x1 + 3.0928x2 − 0.1210x1x2.

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A) −7.38 B) 22.87 C) 41.59 D) 71.88

35)

Which of the following statements about the logarithmic regression model is true?

A) The model allows us to estimate the percentage change in E(y) when x increases 1%.

B) The model is useful when only the predictor variable is better captured in percentages.

C) The model is useful when the response and predictor variables are best captured in percentages.

D) The model allows us to estimate the percent change in E(y) whenx increases by one unit.

36)

Which of the following statements about the exponential regression model is true?

A) The model allows us to estimate the unit change in E(y) whenx increases 1%.

B) The model is useful when only the predictor variable is better captured in percentages.

C) The model is useful when the response and predictor variables are best captured in percentages.

D) The model allows us to estimate the percent change in E(y) whenx increases by one unit.

37) A regression model made to conform to a sample set of data, compromising predictive power is called _____________.

A) cross-validation B) flooding C) overfitting D) binary choice

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38) A nonlinear regression model where both the response and predictor variables are transformed into natural logs is called a _____________.

A) logistic regression model B) log-transformed model C) log-log regression model D) linear probability regression model

39)

<p style="margin-bottom: 20px;">

What regression model best matches this scatterplot?

A) logistic regression model B) log-log regression model C) log-transformed model D) linear probability regression model

40) In cross-validation, if k equals the sample size, the resulting method is also called the _____________.

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A) sensitive data cross-validation method B) leave-one-out cross-validation method C) equality cross-validation method D) partition cross-validation method

41) In using both the linear probability regression and the logistic regression models for n = 40, the following table is the analysis of the holdout method. Based on the table, what is the impact of changing the ŷ to binary predictions?

Observation 18 19 : 40 Accuracy

Y 1 0 : 1

ŷ (Model 1) 1.0263 0.1987 : 0.1509 100

ŷ (Model 2) 0.9907 0.3032 : 0.1989 100

A) There is no relevant outcome changing ŷ to a binary variable.

B) By comparing ŷ to y, the validation results are not able to be validated.

C) By comparing ŷ to y, the accuracy of the model is 100%, but can change with a larger validation set.

D) By comparing ŷ to y, the accuracy of the model is 100% and will not change based on a larger validation set.

42) In determining the partial effect on dummy variable d in a regression model with an interaction variable ŷ = b0 + b1x + b2d + b3xd, the numeric variable x value needs to be known. ⊚ true ⊚ false

43) In a regression model with two dummy variables and an interaction variable d1d2:y = β0 + β1d1 + β2d2 + β3d1d2 + ε, the interaction variables are easy to estimate. ⊚ true ⊚ false Version 1

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44) The quadratic regression model is appropriate for use when the slope changes in magnitude as well as sign. ⊚ true ⊚ false

45) In the quadratic regression model, if β2 > 0 then the relationship between x and y is an inverted U-shape. ⊚ true ⊚ false

46)

ln(y) = β0 + β1 ln(x) + ε represents the exponential regression model. ⊚ true ⊚ false

47) In the quadratic regression model, if β2 < 0 then the relationship between x and y is an inverted U-shape. ⊚ true ⊚ false

48) For cross-validation methods, the holdout method and the k-fold cross-validation method, competing regression models are assessed with the mean square error (MSE). ⊚ true ⊚ false

49)

The natural logarithm converts changes in a variable into percentage changes. ⊚ true ⊚ false

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50) In the k-fold cross-validation method, the smaller the k value, the greater the reliability of the k-fold method. ⊚ true ⊚ false

51) Because software packages use random draws of the observations to partition data, the results will not be identical to a fixed partitioning of the observations. ⊚ true ⊚ false

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Answer Key Test name: ch 8 1) B 2) C 3) C 4) C 5) D 6) D 7) A 8) A 9) D 10) D 11) A 12) A 13) A 14) B 15) D 16) D 17) D 18) D 19) B 20) A 21) A 22) B 23) C 24) C 25) B 26) B Version 1

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27) B 28) A 29) D 30) A 31) D 32) A 33) B 34) C 35) B 36) D 37) C 38) C 39) B 40) B 41) C 42) TRUE 43) TRUE 44) TRUE 45) FALSE 46) FALSE 47) TRUE 48) FALSE 49) TRUE 50) FALSE 51) TRUE

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Student name:__________ 1) Consider the estimated logistic regression modelp̂ = ŷ = infer aboutb1?

What can we

A) x exerts a negative impact B) x exerts a positive impact C) x exerts no impact D) x exerts a linear impact

2) Consider the estimated logistic regression modelp̂ =ŷ = infer aboutb1?

What can we

A) x exerts a negative impact B) x exerts a positive impact C) x exerts no impact D) x exerts a linear impact

3)

Given the logistic regression modelp̂ =ŷ =

estimatep̂ wherex = 10.

A) −0.32 B) 0.24 C) 0.32 D) 0.68

4)

Given the logistic regression modelp̂ =ŷ =

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estimatep̂ wherex = 20.

1

A) 0.24 B) 0.32 C) 0.68 D) 0.84

5)

WhichR function do you use to construct a logistic regression model?

A) glm B) lm C) predict D) summary

6) Genie is worried that her binary response model will not perform as well with data outside of the estimation sample. What technique should she use to assess the predictive power of her model?

A) sample-validation B) population-validation C) model-validation D) cross-validation

7) In using both the linear probability regression and the logistic regression models for n = 40, the following table is the analysis of the holdout method. Based on the table, what is the impact of changing the ŷ to binary predictions?

Observation 18 19 : 40 Accuracy

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Y 1 0 : 1

ŷ (Model 1) 1.0263 0.1987 : 0.1509 100%

ŷ (Model 2) 0.9907 0.3032 : 0.1989 100%

2

A) There is no relevant outcome changingŷ to a binary variable.

B) By comparingŷ toy, the validation results are not able to be validated.

C) By comparingŷ toy, the accuracy of the model is 100%, but can change with a larger validation set.

D) By comparingŷ toy, the accuracy of the model is 100% and will not change based on a larger validation set.

8)

Which of the following is not a step for the holdout cross-validation method?

A) Partition the sample data into two parts, labeled training set and validation set.

B) Use the training set to estimate competing models.

C) Use these estimates to make predictions in the validation set.

D) Calculate the accuracy rates and select the model with the lowest value.

9)

Which of the following statements regarding odds isfalse?

A) Odds are the ratio of the probability of success and the probability of failure.

B) Odds =

wherep =P(y = 1) and the class designated as 1 is the target or positive

class.

C) Odds range between 0 and 1, whereas probability ranges between 0 and infinity.

D) Odds can be used to compute the probability of success asp =

10)

.

What is the probability of a soccer team winning with odds of 5 (i.e., 5 to 1 odds)?

A) 5:1 B) 0.20 C) 0.80 D) 0.83

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11)

What is the probability of a soccer team winning with odds of 9 (i.e., 9 to 1 odds)?

A) 0.20 B) 0.90 C) 9:1 D) 9:10

12)

What are the odds of a tennis player winning with a probability of 0.20?

A) 0.20 B) 0.25 C) 0.75 D) 0.80

13)

What are the odds of a tennis player winning with a probability of 0.80?

A) 0.20 B) 0.25 C) 4 D) 8

14) Given the following logistic regression model results, what is the percentage change in the odds when x2 increases by one unit holding x1 constant?

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A) −49.69 B) −35.65 C) 35.65 D) 49.69

15) Given the following logistic regression model results, calculate the odds when x1 = 10 and x2 = 7?

A) 0.079 B) 0.185 C) 1.580 D) 1.768

16) Which of the following goodness-of-fit measures assesses how well the model fits the data for binary choice models?

A) standard error of the estimate B) coefficient of determination R2 C) adjusted R2 D) accuracy rate

17) WhichR function do you use to compute the predicted probabilities of a binary choice model?

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A) glm B) lm C) predict D) summary

18) Elise computed the linear probability model and logistic regression model accuracy rates for 300 observations. The binary predictions match 73.8 percent of the time for the linear probability model and 238 times for the logistic regression model. Using the preferred model, calculate ŷ for x1 = 50 and x2 = 10.

Coefficients: Intercept x1 x2

Linear Logistic Probability Model Regression Model 0.5334 −1.0555 0.0164 0.1439 −0.0549 −0.4025

A) 58.22% B) 69.09% C) 80.44% D) 89.23%

19) Given a dataset labeled myData with 750 observations, how do you partition a training set (T) and validation set (V) in R to use 70% of the observations to train the data?

A) myData <- TData [1:525,]myData <- VData [526,750,] B) myData <- TData [1:225,]myData <- VData[226,750,] C) TData <- myData[1:225,]VData <- myData[226,750,] D) TData <- myData[1:525,]VData <- myData[526,750,]

20) Given the following accuracy rates for k-fold cross-validation with k = 4, which model will we choose to make predictions and what is the accuracy rate for that model?

Observations in the Validation Set

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Model 1

Model 2

6

301-400 201-300 101-200 1-100

69.5% 70.2% 71.8% 67.3%

72.2% 68.9% 71.5% 69.4%

A) Model 1; 69.7% B) Model 1; 71.8% C) Model 2; 70.5% D) Model 2; 72.2%

21)

Consider a binary response variable y and a predictor variable x that varies between 0 and

5. The linear model is estimated as = −2.90 + 0.65x. What is the estimated probability for x = 5?

A) 0.35 B) 6.15 C) 0.65 D) −6.15

22) How well the binary choice model predicts the nontarget class cases is called_____________.

A) Accuracy B) AdjustedR2 C) Sensitivity D) Specificity

23) Using a sample of 50, the following regression output is obtained from estimating the linear probability model y = β0 + β1x + ε. What is the predicted probability when x = 14?

Coefficients Intercept X

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4.03 −1.98

Standard Error 0.40 0.04

t Stat

P-value

1.23 −4.55

0.0001 0.0000

7

A) 4.42 B) -23.69 C) 3.86 D) 0.72

24) Using a sample of 50, the following regression output is obtained from estimating the linear probability model y = β0 + β1x + ε. What is the predicted probability when x = 14?

Coefficients Intercept X

4.14 −0.02

Standard Error 0.30 0.03

t Stat

P-value

1.45 −4.65

0.0001 0.0000

A) 4.42 B) 3.86 C) 8.34 D) 0.72

25) The following table contains the parameter estimates of the linear probability model and the logistic regression model. When considering a binary response variable y and two predictor variables, x1 and x2, what is the predicted probability implied by the logistic regression model for x1 = 2 with x2 = 15? (Hint: for logit, the model is ŷ = Variable Intercept x1 x2

Linear Probability −0.98 0.48 −0.02

)

Logistic −5.80 1.46 −0.26

A) 0.838 B) 0.001 C) 0.006 D) −5.160

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26) The following table contains the parameter estimates of the linear probability model and the logistic regression model. When considering a binary response variable y and two predictor variables, x1 and x2, what is the predicted probability implied by the logistic regression model for x1 = 2 with x2 = 15? (Hint: for logit, the model is ŷ = Variable Intercept x1 x2

Linear Probability −0.76 0.43 −0.02

)

Logistic −4.1 1.12 −0.22

A) 0.838 B) 0.006 C) −5.16 D) 0.005

27) The following table contains the parameter estimates of the linear probability model and the logistic regression model. When considering a binary response variable y and two predictor variables, x1 and x2, what is the estimated linear probability implied by the linear probability model for x1 = 3 with x2 = 9?

Variable Intercept x1 x2

Linear Probability −0.68 0.50 −0.02

Logistic −1.26 0.86 −0.10

A) 1.87 B) 0.35 C) 0.64 D) −0.87

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28) The following table contains the parameter estimates of the linear probability model and the logistic regression model. When considering a binary response variable y and two predictor variables, x1 and x2, what is the estimated linear probability implied by the linear probability model for x1 = 3 with x2 = 9?

Variable Intercept x1 x2

Linear Probability −0.76 0.43 −0.02

Logistic −1.12 0.85 −0.18

A) 1.87 B) −0.35 C) 0.35 D) −0.87

29)

Specificity and sensitivity are particularly relevant when_____________.

A) the response variable has many ones and a few zeros, or many zeros and a few ones B) the response variable has approximately the same number of ones and zeroes C) the less likely outcome is correctly classified highly accurately D) the response variable is based on the number of times heads is the correct answer

30) A linear regression model applied to a binary response variable is called a _____________.

A) logistic regression model B) log-transformed model C) log-log regression model D) linear probability model

31) Which interpretation of a linear probability model represents the estimate ^P = −40 + 0.05x?

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A) For each 1 unit increase in x, the predicted probability ^P decreases by 0.05.

B) For each 40 unit decrease in x, the predicted probability ^P increases by 40.

C) For each 1 unit increase in x, the predicted probability ^P increases by 0.05.

D) For each 0.05 unit increase in x, the predicted probability ^P decreases by 40.

32) Given the following accuracy, sensitivity, and specificity measures, which model is the preferred model?

Measure Accuracy Sensitivity Specificity

Model 1 69.5% 68.9% 71.8%

Model 2 72.2% 70.2% 71.5%

A) Model 1 because specificity is higher than Model 2.

B) Model 2 because accuracy and sensitivity are higher than Model 1.

C) Model 2 because accuracy is higher than Model 1.

D) Model 2 because the average of the measures is higher than Model 1.

33) Given the following accuracy, sensitivity, and specificity measures, which model is the preferred model?

Measure Accuracy Sensitivity Specificity

Model 1 79.5% 78.9% 81.3%

Model 2 77.2% 78.2% 81.3%

A) Model 1 because accuracy and sensitivity are higher than Model 2.

B) Model 1 because accuracy is higher than Model 2.

C) Model 1 because the average of the measures is higher than Model 2.

D) Model 2 because specificity is higher than Model 2.

34)

Dummy (binary) variables cannot be used as response variables. ⊚ true ⊚ false

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35) of xj.

In both linear probability and the logistic regression models, bj measures the partial effect ⊚ ⊚

true false

36)

Odds range from 0 to 1, whereas probability ranges from 0 to infinity. ⊚ true ⊚ false

37)

Logistic regression coefficients are easier to interpret using odds rather than probabilities. ⊚ true ⊚ false

38) There are no rules as to how the training and validation sets of the sample data should be partitioned but generally random draws are used. ⊚ true ⊚ false

39) In the linear probability regression model, the response variable y will equal 1 or 0 to represent the probability of success. ⊚ true ⊚ false

40) In the logistic regression model, estimates can be made with standard ordinary least squares procedures. ⊚ true ⊚ false

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41) To test for the accuracy rate in a binary choice model, the number of correct classification observations for both outcomes should be reported. ⊚ true ⊚ false

42) In the k-fold cross-validation method, essentially the holdout cross-validation method is used k times with the average of the performance measures being used for model selection. ⊚ true ⊚ false

43)

Specificity is the proportion of target class cases that are identified correctly. ⊚ true ⊚ false

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Answer Key Test name: ch 9 1) B 2) A 3) C 4) D 5) A 6) D 7) C 8) D 9) C 10) D 11) B 12) B 13) C 14) A 15) C 16) D 17) C 18) D 19) D 20) C 21) A 22) D 23) B 24) B 25) B 26) B Version 1

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27) C 28) C 29) A 30) D 31) C 32) B 33) A 34) FALSE 35) FALSE 36) FALSE 37) TRUE 38) TRUE 39) TRUE 40) FALSE 41) TRUE 42) TRUE 43) FALSE

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Student name:__________ 1) Sydney is evaluating monthly sales for her Etsy account. Based on the given data y1 = 4,136; y2 = 4,059; y3 = 4,108, what is her 3-period moving average?

A) 4,101 B) 4,108 C) 4,097 D) 4,122

2) Sydney is evaluating monthly sales for her Etsy account. Based on the given data y1 = 4,321; y2 = 3,876; y3 = 4,190, what is her 3-period moving average?

A) 4,129 B) 4,190 C) 4,098 D) 4,255

3) Mark is using a 3-period moving average to forecast the number of filters needed for the fourth quarter. Using the following data, what is the forecasted amount?

Quarter

Filters 1 2 3 4 1 2 3 4

43 32 35 46 41 35 39 ?

A) 40 Filters B) 37 Filters C) 38 Filters D) 39 Filters

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4) Mark is using a 3-period moving average to forecast the number of filters needed for the fourth quarter. Using the following data, what is the forecasted amount?

Quarter 1 2 3 4 1 2 3 4

Filters 43 32 38 37 41 35 40 ?

A) 37 Filters B) 38 Filters C) 39 Filters D) 40 Filters

5)

Using only the residual in the following table, calculate the overall MSE.

Quarter 4 1 2 3 Total

y 2,789 3,009 2,698 3,245

ŷ 2,684.00 2,956.33 2,924.67 2,921.00

e = y − ŷ 105 53 −227 324

e2 11,025 2,774 51,379 104,976 170,154

A) 2,871.50 B) 2,935.25 C) 42,538.50 D) 226.83

6)

Using only the residual in the following table, calculate the overall MSE.

Quarter

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y

ŷ

e = y − ŷ

e2

2

4 1 2 3

2,789 3,009 2,965 2,876

2,984.00 2,956.33 2,924.67 2,921.00

Total

−195.00 52.67 40.33 −45.00

38,025 2,774 1,627 2,025 44,451

A) 2,946.50 B) 2,909.75 C) 11,112.75 D) 210.83

7) Complete the simple exponentially smoothed series on the following table where α = 0.40 and L1 = y1.

Quarter 1 2 3

y 2,872 3,104 2,859

Lt 2,872 3,103 ?

A) 2,957.00 B) 3,005.40 C) 2,956.60 D) 2,864.20

8) Complete the simple exponentially smoothed series on the following table where α = 0.40 and L1 = y1.

Quarter 1 2 3

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y 2,872 3,104 2,976

Lt 2,872 2,964.8 ?

3

A) 3,027.20 B) 2,969.28 C) 2,971.52 D) 2,934.40

9) Consider the following estimated linear trend model and make a forecast for t = 18 and = 11.45 + 1.03t.

A) 0.50 B) 1.68 C) 29.45 D) 29.99

10) Consider the following estimated linear trend model and make a forecast for t = 18 and = 11.23 + 1.04t.

A) 0.68 B) 1.67 C) 29.23 D) 29.95

11) Consider the following quadratic trend model and make a forecast for t = 19 and ŷ = 10.69 + 1.01t + 0.04t2.

A) 31.40 B) 44.32 C) 28.36 D) 52.82

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12) Consider the following quadratic trend model and make a forecast for t = 20 and ŷ = 15.84 + 0.98t + 0.02t2.

A) 35.08 B) 43.44 C) 25.80 D) 45.10

13)

Consider the following exponential trend model and make a forecast for t = 21 and .

A) 40.86 B) 40.26 C) 40.80 D) 40.45

14)

Consider the following exponential trend model and make a forecast for t = 20 and .

A) 54.60 B) 54.33 C) 54.50 D) 54.05

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15) Based on the following plotted series of linear and exponential trends with an overlay of observation points, which model is the best fit for the observed data?

A) the exponential trend model B) the linear trend model C) neither model D) the quadratic model

16) The polynomial trend model that allows for two changes in the direction of a series is called the __________.

A) quadratic trend model B) cubic trend model C) linear trend model D) exponential trend model

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17) What is the polynomial trend model that allows for one change in direction of a series called?

A) quadratic trend model B) cubic trend model C) linear trend model D) exponential trend model

18)

Which option is not a description of polynomial trend models?

A) Higher level polynomials run the risk of overfitting.

B) MSE, MAD, MAPE cannot be used to compare polynomial trend models.

C) Adjusted R2 is used to compare polynomial models.

D) The coefficient β2 determines the direction of the linear trend line.

19) Using the following table of results, what is the estimated linear trend model for the sample?

Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations

0.918811476 0.845788529 0.849252529 0.8208249490 18 Coefficients

Intercept X Variable 1

12.735631580 0.864368421

A) ŷ = 0.91881 + 0.8644t B) ŷ = 12.7356 + 0.8644t C) ŷ = 0.84579 + 0.84925t D) ŷ = 0.84925 + 12.7356t

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20) Using the following table of results, what is the estimated linear trend model for the sample?

Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations

0.918811476 0.844214529 0.835559781 0.820824949 20 Coefficients

Intercept X Variable 1

11.76063158 0.314368421

A) ŷ = 0.91881 + 0.3144t B) ŷ = 11.7606 + 0.3144t C) ŷ = 0.84421 + 0.83556t D) ŷ = 0.83556 + 11.7606t

21) Using the following table of results, what is the estimated quadratic trend model for the sample?

Regression Statistics Multiple R 0.976507605 R Square 0.953567103 Adjusted R Square 0.954097409 Standard Error 0.46111766 Observations 20 Coefficients Intercept

12.3876316

Standard Error 0.343063

X Variable 1

0.838016651

0.075239

10.32438

X Variable 2

−0.01624939

0.00348

−6.32741

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t Stat 29.3388

p-value 5.3 E-16 9.68 E-09 7.58 E-06

8

A) ŷ = 12.3876 + 0.8380t − 6.3274t2 B) ŷ = 12.3876 + 0.9541t + 0.0034t2 C) ŷ = 12.3876 + 0.0034t + 0.0162t2 D) ŷ = 12.3876 + 0.8380t − 0.0162t2

22) Using the following table of results, what is the estimated quadratic trend model for the sample?

Regression Statistics Multiple R 0.976507605 R Square 0.953567103 Adjusted R 0.948104409 Square Standard Error 0.46111766 Observations 20 Coefficients Intercept X Variable 1 X Variable 2

10.0650614 0.776796651 −0.02202039

Standard Error 0.343063 0.075239 0.00348

t Stat 29.3388 10.32438 −6.32741

p-value 5.30E-16 9.68E-09 7.58E-06

A) ŷ = 10.0651 + 0.7768t − 6.3274t2 B) ŷ = 10.0651 + 0.9481t + 0.0034t2 C) ŷ = 10.0651 + 0.0034t + 0.0220t2 D) ŷ = 10.0651 + 0.7768t − 0.0220t2

23)

Based on the following results, which model is the best fit for the data?

Summary Output Linear Trend Model Regression Statistics Multiple R 0.918811476 R Square 0.844214529 Adjusted R Square 0.835559781 Standard Error 0.820824949 Observations 20 Summary Output Quadratic Trend Model

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Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations

0.976507605 0.953567103 0.948104409 0.46111766 20

A) The adjusted R2 for the quadratic model is higher, thus best fit for the data.

B) The adjusted R2 for the linear model is lower, thus best fit for the data.

C) The R2 for the quadratic model is higher, thus best fit for the data.

D) The R2 for the linear model is lower, thus best fit for the data.

24) Using the following table, estimate quadratic trend model with seasonal dummy variables.

Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations

0.935185002 0.874570988 0.855988912 0.054863436 32 Coefficients

Intercept X Variable 1 X Variable 2 X Variable 3 X Variable 4 X Variable 5

1.415457253 0.017428641 −0.073442595 −0.06362285 −0.004004323 0.000294365

A) = 0.9352 + 0.0174d1 − 0.0734d2 − 0.0636d3 − 0.0040t + 0.0003t2 B) =1.4155 + 0.0174d1 − 0.0734d2 − 0.0636d3 − 0.0040t + 0.0003t2 C) =0.8560 + 0.0174d1 − 0.0734d2 − 0.0636d3 − 0.0549t + 0.0003t2 D) =1.4155 + 0.9352d1 − 0.8746d2 − 0.8560d3 − 0.0549t + 0.0003t2

25)

Using the following results, estimate linear trend model with seasonal dummy variables.

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Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations

Intercept X Variable 1 X Variable 2 X Variable 3 X Variable 4

0.927460476 0.844214529 0.835559781 0.044863436 32 Coefficients 1.792609156 0.068818641 −0.064562865 −0.05775012 −0.01292147

A) = 1.7926 + 0.0688d1 + 0.0646d2 + 0.0578d3 + 0.0129t B) = 1.7926 − 0.0688d1 − 0.0646d2 − 0.0578d3 + 0.0129t C) = 1.7926 + 0.0688d1 − 0.0646d2 − 0.0578d3 − 0.0129t D) = 1.7926 − 0.0688d1 + 0.0646d2 + 0.0578d3 + 0.0129t

26)

Using the following results, estimate linear trend model with seasonal dummy variables.

Regression Statistics

Multiple R R Square Adjusted R Square Standard Error Observations

0.935185002 0.874570988 0.855988912 0.054863436 32 Coefficients

Intercept X Variable 1 X Variable 2 X Variable 3 X Variable 4

1.470209156 0.017428641 −0.072853865 −0.06303412 −0.01371837

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A) 1.4702 + 0.0174d1 + 0.0729d2 + 0.0630d3 + 0.0137t B) 1.4702 − 0.0174d1 − 0.0729d2 − 0.0630d3 + 0.0137t C) 1.4702 + 0.0174d1 − 0.0729d2 − 0.0630d3 − 0.0137t D) 1.4702 − 0.0174d1 + 0.0729d2 + 0.0630d3 + 0.0137t

27) Mary is determining the maximum quantity given the seasonality is constant. If the coefficients are 110t and −23.1955t2 in a quadratic model, what is the maximum quantity that can be reached?

A) 4.74 B) 0.105 C) 8.61 D) 2

28) Mary is determining the maximum quantity given the seasonality is constant. If the coefficients are 112t and −23.4502t2 in a quadratic model, what is the maximum quantity that can be reached?

A) 4.78 B) 0.015 C) 9.10 D) 2

29)

Which one of the following is not a step in cross-validation with time series?

A) Use both the training and validation set to re-estimate the preferred model.

B) Split data series into early and later periods, representing both training and validation

set.

C) Determine the proper forecast model based on R2 results.

D) Explore suitable forecasting models to compute MSE, MAD, and MAPE.

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30) In the __________ model, to estimate, the response variable is measured in natural logs, ln (yt), and then a regression is run of ln (yt) on t.

A) linear B) exponential C) cubed D) quadratic

31)

The following model yt =β0 + β1t + β2t2 + β3t3 + εt allows for what changes in a series?

A) allows for a curvature indicating the change of direction B) allows for one change in the direction of a series C) allows for two changes in the directions of a series D) allows for the increase in the time period and standard error of the estimate

32) Martin owns a food truck that for the past five years has frequented local festivals selling fried cheese curds. He has experienced a variation in sales for no known reason and wants to develop a forecast using the exponential smoothing method. After loading data from the previous 20 days, the following summary table was calculated. What is the MSE? Day

Curds 1

70

2 3 …

20 Total

69 67 …

61

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At

ŷt

et = yt − ŷt

e2

|e|

69.7 68.89 …

63.03

70 69.7 …

63.9

−1 −2.7 … −2.898

1 7.29 …

8.39682 212.412

1 2.7 …

2.8977 52.083

13

A) 10.62 B) 4.08 C) 11.18 D) 2.741

33) Martin owns a food truck that for the past five years has frequented local festivals selling fried cheese curds. He has experienced a variation in sales for no known reason and wants to develop a forecast using the exponential smoothing method. After loading data from the previous 20 days, the following summary table was calculated. What is the MSE? Day

Curds 1

70

2 3 …

20

69 67 …

61

At

ŷt

et = y1 − ŷt

69.7 68.89 …

63.03

70 69.7 …

63.9

−1 −2.7 … −2.898

Total

e2

1 7.29 …

8.39682 206.064

|e|

1 2.7 …

2.8977 51.211

A) 10.85 B) 4.02 C) 10.30 D) 2.695

34) Martin owns a food truck that for the past five years has frequented local festivals selling fried cheese curds. He has experienced a variation in sales for no known reason and wants to develop a forecast using the exponential smoothing method. After loading data from the previous 20 days, the following summary table was calculated. What is the MAD? Day

Curds 1

70

2 3

69 67

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At

ŷt

et = y1 − ŷt

e2

|e|

69.7 68.89

70 69.7

−1 −2.7

1 7.29

1 2.7

14

…

20 Total

…

61

…

63.03

…

63.9

… −2.898

…

8.39682 209.231

…

2.8977 52.143

A) 10.46 B) 4.01 C) 11.01 D) 2.744

35) Martin owns a food truck that for the past five years has frequented local festivals selling fried cheese curds. He has experienced a variation in sales for no known reason and wants to develop a forecast using the exponential smoothing method. After loading data from the previous 20 days, the following summary table was calculated. What is the MAD? Day

Curds 1

70

2 3 …

20

69 67 …

61

Total

At

ŷt

et = y1 − ŷt

69.7 68.89 …

63.03

70 69.7 …

63.9

−1 −2.7 … −2.898

e2

1 7.29 …

8.39682 206.064

|e|

1 2.7 …

2.8977 51.211

A) 10.30 B) 4.02 C) 10.85 D) 2.695

36) Martin is handed quarterly sales data from a small subsidiary. Prior to creating a strategy, he wants to forecast the Q4 results to have an idea of the full year potential sales. What is the most popular technique Martin can use to compute the Q4 sales estimate?

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A) quadratic trend model B) 3-period moving average technique C) polynomial trend model D) exponential smoothing model

37) Consider the following table of the derivations for the MSE, MSA, and MAPE in the validation set. Based on the results, which model is preferred and why?

Linear MSE MAD MAPE

144.64 11.06 10.4

Exponential 120.43 10.82 2.45

A) Exponential, because the MSE, MAD, and MAPE are consistently lower.

B) Both Linear and Exponential are preferred models because the results are positive.

C) Linear, because the MSE, MAD, and MAPE are consistently higher.

D) Neither, because an adjusted R2 is needed for determination.

38) Which method would be the best fit for a sample containing seasonality, but no trend, and is further divided into structures depending on the type of seasonality exhibited by the series?

A) the exponential method B) the Holt exponential smoothing method C) the Holt-Winters exponential smoothing method D) quadratic trend model

39) When constructing a quick review table in Excel, knowing the formula is essential in populating the cells correctly. Using the Holt exponential smoothing method, complete the following table for Year 3 where α = 0.1 and β = 0.2.

Year

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Lt

Tt

Ft

16

1

78,000

2

90,000

79,000

11,000

3

98,000

89,900

?

89,000

A) 6,400 B) 8,780 C) 7,180 D) 10,980

40) When constructing a quick review table in Excel, knowing the formula is essential in populating the cells correctly. Using the Holt exponential smoothing method, complete the following table for Year 3 where α = 0.1 and β = 0.2.

Year

Lt

Tt

Ft

1

78,000

2

89,000

78,000

11,000

3

98,000

89,900

?

89,000

A) 6,680 B) 8,980 C) 8,680 D) 11,180

41) When constructing a quick review table in Excel, knowing the formula is essential in populating the cells correctly. Using the Holts exponential smoothing method, complete the following table for Year 4 where α = 0.1 and β = 0.2.

Year

Lt

Tt

1

78,000

2

89,000

78,000

11,400

3

98,000

87,900

11,100

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Ft

89,000

17

4

98,000

?

11,000

89,000

A) 98,800 B) 98,900 C) 103,170.8 D) 109,332.8

42) When constructing a quick review table in Excel, knowing the formula is essential in populating the cells correctly. Using the Holts exponential smoothing method, complete the following table for Year 4 where α = 0.1 and β = 0.2.

Year

Lt

Tt

1

78,000

2

89,000

78,000

11,000

3 4

98,000 115,000

89,900 ?

11,180 11,458.40

Ft

89,000 101,080

A) 103,864 B) 102,472 C) 104,510.8 D) 114,625.6

43) When performing a cross-validation of regression model with R, in the forecast package, we use the __________ function to find the number of observations in the validation set.

A) length B) forecast C) tslm D) accuracy

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44) When using R to perform the Holt-Winters exponential smoothing method, in forecast, we use the __________ function with the model inputs of ‘AAA’ and ‘AAM’.

A) ts B) window C) ets D) fAdd

45) Of the smoothing methods, which one does the level Lt, as well as the trend Tt, adapt over time and is a best fit when the time series expresses no seasonality?

A) simple exponentially smoothing technique B) the moving average technique C) the Holt exponential smoothing method D) the Holt-Winters exponential smoothing method

46) Which of the following model performance measures gives you a sense of the magnitude of the errors by showing the error as a percentage of the actual value?

A) MAD B) MAPE C) MSE D) RMSE

47)

Which of the following is not a valid reason for using a simple smoothing technique?

A) To reduce the effect of random fluctuations B) To provide forecasts if short-term fluctuations represent random departures from the structure C) When forecasts of multiple variables need to be updated frequently D) When there are variations that can be explained due to trend or seasonality

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48) Which of the following models reflects a linear trend model with quarterly dummy variables?

A) y = β0 + β1 t + ε B) y = β0 + β1d1 + β2d2 + β3d3 + ε C) y = β0 + β1d1 + β2d2 + β3d3 + β4d4 + ε D) y = β0 + β1 d1 + β2d2 + β3d3 + β4t + ε

49)

<p>Forecast Q1 of year 11 revenues using the model ,

which was estimated based on 10 years of quarterly data.

A) $4,122,954.19 B) $5,649,516.36 C) $5,735,359.55 D) $6,028,932.37

50) Aimee’s bookstore had a 45% increase in profits on Wednesday, June 12th, over the previous year’s sales. Without the presence of a holiday, events in the area, or sale promotion, this business event is considered random. ⊚ true ⊚ false

51) The use of quantitative forecast can be criticized because biases in optimism and overconfidence may skew the results. ⊚ true ⊚ false

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52) In a 3-period moving average, when a new observation becomes available, the highest numerical observation is dropped. ⊚ true ⊚ false

53) When a time series is expected to grow by fixed amounts each time period, then the linear trend model should be used. ⊚ true ⊚ false

54) When visually inspecting data to confirm the existence of a trend, a scatterplot of the data with a superimposed linear trend line is advisable to view the series over time. ⊚ true ⊚ false

55) In reviewing stock growth in Amazon, the linear trend model would be best use for when an increase in the series happens over time. ⊚ true ⊚ false

56) If a time series reverses direction, then a quadratic trend model will allow for the curvature to be graphed. ⊚ true ⊚ false

57) By combining the validation and the training set, the sample is larger for estimation and includes the most recent validation set for predictions. ⊚ true ⊚ false

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58) When a time series exhibits seasonal variations, the Holt exponential smoothing method, or double exponential smoothing method, is appropriate to capture the upward and downward movement of the time series. ⊚ true ⊚ false

59) data.

The triple exponential smoothing method uses seasonality variations in the analysis of the ⊚ ⊚

true false

60) The functionality to perform the moving average or simple exponential smoothing techniques in Excel can be found on the Formulas ribbon. ⊚ true ⊚ false

61)

Causality can be determined using the model y = β0 + β1 d1 + β2d2 + β3d3 + β4t + ε. ⊚ true ⊚ false

62) The linear trend model with seasonality y = β0 + β1 d1 + β2d2 + β3d3 + β4t + ε can be modified to reflect an exponential trend by using the model ln(y) = β0 + β1 d1 + β2d2 + β3d3 + β4t + ε. ⊚ true ⊚ false

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Answer Key Test name: ch 10 1) A 2) A 3) C 4) C 5) C 6) C 7) B 8) B 9) D 10) D 11) B 12) B 13) A 14) A 15) A 16) B 17) A 18) D 19) B 20) B 21) D 22) D 23) A 24) B 25) C 26) C Version 1

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27) D 28) D 29) C 30) B 31) C 32) A 33) C 34) D 35) D 36) B 37) A 38) C 39) D 40) D 41) B 42) B 43) A 44) C 45) C 46) B 47) D 48) D 49) B 50) TRUE 51) FALSE 52) FALSE 53) TRUE 54) TRUE 55) FALSE 56) TRUE Version 1

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57) TRUE 58) FALSE 59) TRUE 60) FALSE 61) FALSE 62) TRUE

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Student name:__________ 1) Cross-Industry Standard Process for Data Mining (CRISP-DM) consists of six phases. Of the six, which one represents the phase where data wrangling occurs?

A) Deployment B) Modeling C) Data understanding D) Data preparation

2) Using the Manhattan distance between pairwise observations, which pairwise observation is most similar?

Observation 1 2 3

x1 2 5 12

x2 2 5 1

A) Observations 1 & 2 B) Observations 2 & 3 C) Observations 1 & 2 D) Both Observations 1 & 3 and 2 & 3

3) Using the Manhattan distance between pairwise observations, which pairwise observation is most similar?

Observation 1 2 3

x1 2 6 8

x2 3 4 2

A) Observations 2 & 3 B) Observations 1 & 3 C) Observations 1 & 2 D) Both Observations 2 & 3 and 1 & 3

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4) Using the Euclidean distance between pairwise observations, which pairwise observation is most dissimilar?

Observation 1 2 3

x1

x2 1 3 2

3 5 10

A) Observations 1 & 3 B) Observations 2 & 3 and 1 & 3 C) Observations 2 & 3 D) Observations 1 & 2 and 2 & 3

5) Using the Euclidean distance between pairwise observations, which pairwise observation is most dissimilar?

Observation 1 2 3

x1

x2 3 4 2

2 6 8

A) Observations 1 & 3 B) Observations 2 & 3 and 1 & 3 C) Observations 2 & 3 D) Observations 1 & 2 and 2 & 3

6) Consider the partial data set in the table represents online hours spent shopping by age and income. The average and standard deviation for the full data set is $47,649 and $14,226, respectively. Using z-scores to standardize the observations, what will be the average of standardized Income for the three provided?

ID 2201 2202 2203

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Income 75,000 49,000 59,000

Age 50 49 39

Online Hours 2 5 5

2

A) 1.923 B) 0.1040 C) 0.6660 D) 0.9387

7) Consider the partial data set in the table represents online hours spent shopping by age and income. The average and standard deviation for the full data set is $47,667 and $14,292, respectively. Using z-scores to standardize the observations, what will be the average of standardized Income for the three provided?

ID 2201 2202 2203

Income 62,000 58,000 53,000

Age 48 52 44

Online Hours 2 4 5

A) 1.003 B) 0.7320 C) 0.2410 D) 0.6997

8) Consider the partial data set in the table represents online hours spent shopping by age and income. What is the average min-max normalized values for Income shown in the table? Use the min-max transformation to normalize the observations for Income spent online.

ID 2,201 2,202 2,203 2,204 2,205 2,206

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Income 68,000 54,000 51,000 35,000 44,000 44,000

Age 49 41 48 39 25 34

Online Hours 2 5 4 8 6 5

3

A) 1.000 B) 0.4340 C) 0 D) 0.4920

9) Consider the partial data set in the table represents online hours spent shopping by age and income. What is the average min-max normalized values for Income shown in the table? Use the min-max transformation to normalize the observations for Income spent online.

ID 2201 2202 2203 2204 2205 2206

Income 62,000 58,000 53,000 22,000 43,000 48,000

Age 48 52 44 28 33 35

Online Hours 2 4 5 7 4 3

A) 1 B) 0.6417 C) 0 D) 0.6997

10) The following table is a segment of Loan Data from a bank for car loans. Compute the matching coefficient between Pairs 1 and 4.

Line 1 2 3 4

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Term 50 70 34 70

interest_rate 3.76% 3.72% 6.51% 3.76%

loan_amount 39,900 15,520 14,300 15,420

Sex F F F M

4

A) Matching coefficient is 0.25.

B) Matching coefficient is 0.13.

C) Matching coefficient is 0.88.

D) Matching coefficient is 0.37.

11) The following table is a segment of Loan Data from a bank for car loans. Compute the matching coefficient between Pairs 1 and 4.

Line 1 2 3 4

Term 60 60 36 60

interest_rate 3.58% 4.00% 6.71% 6.07%

loan_amount 35,000 15,350 12,500 15,350

Sex F F M F

A) Matching coefficient is 0.50.

B) Matching coefficient is 0.25.

C) Matching coefficient is 0.75.

D) Matching coefficient is 0.40.

12) Sandra began collecting transaction details to see if the same items were in each sales transaction. Compute the matching coefficient and Jaccard’s coefficient for pairwise transaction 1 & 2.

Transaction 1 2

Coffee No Yes

Tea Yes No

Scone Yes No

Muffin Yes Yes

Cookie Yes Yes

A) Matching Coefficient 0.40 and Jaccard's 0.40 B) Matching Coefficient 0.40 and Jaccard's 0.50 C) Matching Coefficient 0.40 and Jaccard's 0.75 D) Matching Coefficient 0.40 and Jaccard's 0.30

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13) Sandra began collecting transaction details to see if the same items were in each sales transaction. Compute the matching coefficient and Jaccard’s coefficient for pairwise transaction 1 & 2.

Transaction 1 2

Coffee Yes Yes

Tea No Yes

Scone Yes Yes

Muffin No No

Cookie Yes No

A) Matching Coefficient = 0.60 and Jaccard’s = 0.40 B) Matching Coefficient = 0.40 and Jaccard’s = 0.50 C) Matching Coefficient = 0.60 and Jaccard’s = 0.50 D) Matching Coefficient = 0.40 and Jaccard’s = 0.40

14) The process of dividing a data set into a training, a validation, and an optimal test data set is called _____________.

A) overfitting B) oversampling C) optional testing D) data partitioning

15) Cameron is performing a study on the IQ of groups in various areas. He has calculated that the average IQ of Group A is 131 with a standard deviation of 10. What is the z-score for someone with an IQ of 115?

A) 1.60 B) −1.60 C) 1.80 D) −0.80

16) Cameron is performing a study on the IQ of groups in various areas. He has calculated that the average IQ of Group A is 105 with a standard deviation of 10. What is the z-score for someone with an IQ of 98?

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A) 0.7 B) −0.7 C) 0.9 D) 0.1

17) Cameron is performing a study on the IQ of groups in various areas. He has calculated that the average IQ of Group B is 103 with a standard deviation of 10. What is the z-score for someone with an IQ of 110?

A) 1.10 B) −0.70 C) 0.22 D) 0.70

18) Cameron is performing a study on the IQ of groups in various areas. He has calculated that the average IQ of Group B is 118 with a standard deviation of 12. What is the z-score for someone with an IQ of 125?

A) 0.98 B) −0.58 C) 0.10 D) 0.58

19) When a predictive model is made overly complex to fit in the quirks of given sample data, it is called _____________.

A) oversampling B) overfitting C) partitioning D) distribution

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20) Molly e-mailed her clients offering a free 30-minute massage for referrals. In the following validation set of 100, Class 1 reflects the clients predicted to provide referrals and Class 0 reflects the clients predicted to not provide referrals. Based on the confusion matrix, what was the True Positive (TP) of current clients who provided referrals for a free massage?

Actual Class Class 1 Class 0

Predicted Class 1 Predicted Class 0 17 13 13 57

A) 30 B) 13 C) 17 D) 57

21) Molly e-mailed her clients offering a free 30-minute massage for referrals. In the following validation set of 100, Class 1 reflects the clients predicted to provide referrals and Class 0 reflects the clients predicted to not provide referrals. Based on the confusion matrix, what was the True Positive (TP) of current clients who provided referrals for a free massage?

Actual Class Class 1 Class 0

Predicted Class 1 Predicted Class 0 18 11 11 60

A) 29 B) 11 C) 18 D) 60

22) Molly e-mailed her clients offering a free 30-minute massage for referrals. In the following validation set of 100, Class 1 reflects the clients predicted to provide referrals and Class 0 reflects the clients predicted to not provide referrals. Based on the confusion matrix, what was the False Negative (FN) of current clients who provided referrals for a free message?

Actual Class Class 1 Class 0

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Predicted Class 1 Predicted Class 0 18 11 11 60

8

A) 29 B) 18 C) 11 D) 60

23)

The sensitivity, also called recall, is computed using which equation?

A) Sensitivity = TP ÷ (TP + FN).

B) Sensitivity = TN ÷ (TP + FN).

C) Sensitivity = (TP + TN) ÷ (TP + TN + FP + FN).

D) Sensitivity = TP ÷ (TP + FP).

24)

The precision, also called positive predictive value, is computed using which equation?

A) Precision = TP ÷ (TP + FN).

B) Precision = TN ÷ (TP + FN).

C) Precision = (TP + TN) ÷ (TP + TN + FP + FN).

D) Precision = TP ÷ (TP + FP).

25) Based on the following confusion matrix with a validation set of 100, Class 1 reflects the members targeted who purchased services and Class 0 reflects the non-targeted respondents who did not purchase services. Calculate the specificity rate.

Actual Class Class 1 Class 0

Predicted Class 1 Predicted Class 0 27 10 10 53

A) 73% B) 89% C) 84% D) 84.1%

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26) Based on the following confusion matrix with a validation set of 100, class 1 reflects the members targeted who purchased services and class 0 reflects the non-targeted respondents who did not purchase services. Calculate the specificity rate.

Actual Class Class 1 Class 0

Predicted Class 1 18 11

Predicted Class 0 11 60

A) 62% B) 78% C) 84% D) 84.5%

27) Based on the following confusion matrix with a validation set of 100, Class 1 reflects the members targeted who purchased services and Class 0 reflects the non-targeted respondents who did not purchase services. Calculate the sensitivity rate.

Actual Class Class 1 Class 0

Predicted Class 1 Predicted Class 0 21 10 10 59

A) 84% B) 85% C) 68% D) 38%

28) Based on the following confusion matrix with a validation set of 100, Class 1 reflects the members targeted who purchased services and Class 0 reflects the non-targeted respondents who did not purchase services. Calculate the sensitivity rate.

Actual Class Class 1 Class 0

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Predicted Class 1 Predicted Class 0 18 11 11 60

10

A) 78% B) 84% C) 62% D) 32%

29) The calculated average error measuring the average magnitude of errors in predictive performance measures is called _____________.

A) mean percentage error B) root mean square error C) mean error D) mean absolute deviation

30) The _____________ is the average absolute percentage error, shown as a percentage of the actual value, displaying the magnitude of the errors in performance measures.

A) RMSE B) MAD C) MAPE D) ME

31)

The root mean square error calculation is A) <p>= B) <p>= C) = y − D) <p>=

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× 100

11

32)

Which is the best-fit definition for the use of Principal Component Analysis (PCA)?

A) The elimination of the number of components in a data set to identify errors.

B) The transformation of a small set of correlated variables into larger uncorrelated subsets.

C) The transformation of a large number of correlated variables into a smaller number of uncorrelated variables.

D) The driving of pattern recognition in a supervised data mining set used to visualize data methods.

33) The following table displays the weights for computing the principal components and the data for two Observations. The mean and standard deviation for x1 are 4.10 and 1.80, respectively. What is the z-score of x1 for Observation 1?

Weights x1 x2

PC1 −0.73 −0.67 x1

PC2 0.52 0.79 x2

Observation 1 Observation 2

4.30 1.43

11.94 8.54

A) 0.11 B) 0.10 C) 0.66 D) 0.14

34) The following table displays the weights for computing the principal components and the data for two Observations. The mean and standard deviation for x1 are 4.2 and 1.4, respectively.

What is the z-score of x1 for Observation 1?

Weights x1 x2

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PC1 −0.78 −0.65 x1

PC2 0.51 0.82 x2

12

Observation 1 Observation 2

4.45 1.43

12.08 8.62

A) 0.18 B) 0.17 C) 0.73 D) 0.21

35) The following table displays the weights for computing the principal components and the data for two Observations. The mean and standard deviation for x1 are 4.4 and 1.5, respectively.

The mean and standard deviation for x2 are 6.6 and 4.4, respectively. Compute the first principal component score for Observation 1.

Weights x1 x2

Observation 1 Observation 2 Variance Variance Percentage

PC1 −0.65 −0.78 x1 4.60 4.71 3.77

x2 12.08 8.92

78.323233

A) −1.060 B) −0.611 C) 1.267 D) 0.930

36) The following table displays the weights for computing the principal components and the data for two Observations. The mean and standard deviation for x1 are 4.2 and 1.4, respectively.

The mean and standard deviation for x2 are 6.8 and 4.8, respectively. Compute the first principal component score for Observation 1.

Weights x1 x2

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PC1 −0.72 −0.85

13

Observation 1 Observation 2 Variance Variance Percentage

x1

x2

4.45 4.56 3.36

12.08 8.62

78.323221

A) −0.293 B) −1.065 C) 0.949 D) 0.612

37)

Of the following selections, which is not a descriptor of principal component analysis?

A) The first principal account is not suitable for analysis.

B) Principal components are uncorrelated variables.

C) The first principal accounts for most of the variability.

D) Principal component variables are weighted linear combinations of the original variables.

38)

Calculate the misclassification rate for the following confusion matrix.

Actual Class Class 1 Class 0

Predicted Class 1 Predicted Class 0 19 9 9 63

A) 0.68 B) 0.18 C) 0.74 D) 0.24

39)

Calculate the misclassification rate for the following confusion matrix.

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Actual Class Class 1 Class 0

Predicted Class 1 Predicted Class 0 18 11 11 60

A) 0.72 B) 0.22 C) 0.78 D) 0.28

40)

Calculate the accuracy rate for the following confusion matrix.

Actual Class Class 1 Class 0

Predicted Class 1 19 13

Predicted Class 0 13 55

A) 0.76 B) 0.26 C) 0.74 D) 0.32

41)

Calculate the accuracy rate for the following confusion matrix.

Actual Class Class 1 Class 0

Predicted Class 1 18 11

Predicted Class 0 11 60

A) 0.72 B) 0.22 C) 0.78 D) 0.28

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42) Tess is tasked with analyzing a data set with multiple variables with various scales. To reduce the difference in scale in variables, she is following a common process called _____________ to make the observations unit-free.

A) data mining B) Euclidean distance C) Manhattan distance D) standardizing

43) Which chart allows for a visual representation to determine a point where a model’s predictions become less useful?

A) ROC Curve B) Cumulative Lift Chart C) Decile-wise Lift Chart D) Sensitivity Measure

44) Which chart is a bar chart displayed in 10 equal-sized intervals, or every 10% of the observations?

A) ROC Curve B) Cumulative Lift Chart C) Decile-wise Lift Chart D) Sensitivity Measure

45)

When using PCA, all the following are disadvantages except A) PCA results are difficult to interpret clearly.

B) components are weighted linear combinations and abstract.

C) PCA only works with numerical data.

D) PCA significantly increases the dimension of the data.

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46) What is the first step to take in Excel to create a confusion matrix given a dataset that includes the actual class to which the observation belongs (ActualClass) and the probability of the target class (TargetProb)?

A) Compute values for False Positives and False Negatives B) Compute values for True Positives and True Negatives C) Derive the predicted class based on a cutoff of 0.5 D) Build the confusion matrix using a COUNTIF formula

47) Calculate the misclassification rate for the following 5 observations using a 0.25 cutoff value.

Observation 1 2 3 4 5

Actual Class 0 1 0 1 1

Class 1 Probability 0.11 0.24 0.76 0.37 0.07

A) 0.33 B) 0.40 C) 0.50 D) 0.60

48) Cross-Industry Standard Process for Data Mining (CRISP-DM) consists of six phases.

Which of the following is the first phase?

A) Business understanding B) Data preparation C) Data understanding D) Modeling

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49) Based on the following chart, how many components will need to be retained in order account for at least 90% of the total variance in the data?

Variance %

PC1

PC2

PC3

PC4

PC5

74.3890

12.5242

7.3262

4.7306

1.030

A) 2 B) 3 C) 4 D) 5

50) The process of applying a set of analytical techniques for the development of machine learning and artificial intelligence is called data mining. ⊚ true ⊚ false

51) The key distinction between supervised and unsupervised data mining is that the identification of the target variable is identified in supervised data mining. ⊚ true ⊚ false

52) Common applications of unsupervised learning include dimension reduction and prediction model. ⊚ true ⊚ false

53)

Normalization is the process that makes the numerical data independent of scale. ⊚ true ⊚ false

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54) The Jaccard’s coefficient is appropriate when it is more informative to match negative outcomes between observations. ⊚ true ⊚ false

55) Oversampling involves intentionally selecting more samples from one class than from other classes to adjust the class distribution of a data set. ⊚ true ⊚ false

56) A diagram that represents the information in equal-sized intervals, deciles, is called a cumulative lift chart. ⊚ true ⊚ false

57) In real-world situations, data sets contain many variables. If some variables are eliminated, valuable information may be lost. ⊚ true ⊚ false

58) The principal component analysis (PCA) is a dimension reduction technique used to reduce variables without removing variables. ⊚ true ⊚ false

59) In Excel, Analytic Solver only provides the covariance matrix for performing principal component analysis (PCA).

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⊚ ⊚

true false

60) Pairwise observations that have a large distance between observations are said to have a high degree of similarity. ⊚ true ⊚ false

61)

A bar or column chart is used to create a decile-wise lift performance chart in Excel. ⊚ true ⊚ false

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Answer Key Test name: ch 11 1) D 2) A 3) A 4) A 5) A 6) D 7) D 8) B 9) B 10) A 11) A 12) A 13) C 14) D 15) B 16) B 17) D 18) D 19) B 20) C 21) C 22) C 23) A 24) D 25) D 26) D Version 1

21

27) C 28) C 29) D 30) C 31) B 32) C 33) A 34) A 35) A 36) B 37) A 38) B 39) B 40) C 41) C 42) D 43) B 44) C 45) D 46) C 47) D 48) A 49) B 50) TRUE 51) TRUE 52) FALSE 53) TRUE 54) FALSE 55) TRUE 56) FALSE Version 1

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57) TRUE 58) TRUE 59) FALSE 60) FALSE 61) TRUE

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Student name:__________ 1)

Using the table below, find the k-nearest neighbor for record 4 using k = 3 for age.

Record 1 2 3 4 5 6

Age 33 27 30 32 44 53

Marital Married Other Other Married Other Other

Loan $ 4,700.00 $ 10,000.00 $ 14,000.00 $ 20,000.00 $ 16,000.00 $ 8,000.00

Risk Low High Low High High Low

A) 27, 30, & 33 B) 30, 33, & 44 C) 30, 32, & 33 D) 32, 33, & 44

2)

Using the table below, find the k-nearest neighbor for record 4 using k = 3 for age.

Record 1 2 3 4 5 6

Age 34 24 31 32 44 53

Marital Single Married Single Other Married Single

Loan $ 5,000.00 $ 15,000.00 $ 7,000.00 $ 10,000.00 $ 12,000.00 $ 9,000.00

Risk High Low Medium Medium High Low

A) 24, 31, & 34 B) 31, 34, & 44 C) 31, 32, & 34 D) 32, 34, & 44

3) A new applicant, age 32, is applying for a loan. Using the table below, what is the estimated probability the loan will default using k = 3.

Record 1 2

Version 1

Age 34 24

Marital Married Married

Loan $ 4,600.00 $ 15,000.00

Risk High Medium

Default Yes Yes

1

3 4 5 6

31 32 44 53

Other Single Other Married

$ 10,000.00 $ 8,000.00 $ 18,000.00 $ 10,000.00

High Low Low High

Yes No No No

A) 100% B) 33% C) 67% D) 0%

4) A new applicant, age 32, is applying for a loan. Using the table below, what is the estimated probability the loan will default using k = 3.

Record 1 2 3 4 5 6

Age 34 24 31 32 44 53

Marital Single Married Single Other Married Single

Loan $ 5,000.00 $ 15,000.00 $ 7,000.00 $ 10,000.00 $ 12,000.00 $ 9,000.00

Risk High Low Medium Medium High Low

Default No No Yes No Yes No

A) 100% B) 66% C) 33% D) 0%

5)

For a new observation of (0, 0, 0), what is the k-nearest neighbor when k= 1.

Observation 1 2 3 4 5 6

Version 1

Y Red Blue Blue Red Blue Blue

X1 1 −2 0 2 0 2

X2 −1 −1 2 −1 −2 −1

X3 0 0 0 1 −1 1

2

A) 0, -2, -1 B) -2, -1, 0 C) 2, -1, 1 D) 1, -1, 0

6)

For a new observation of (0, 0, 0), what is the k-nearest neighbor when k = 1.

Observation 1 2 3 4 5 6

Y Red Blue Blue Red Blue Blue

X1 0 −2 −1 0 1 0

X2 2 −1 0 1 1 3

X3 0 0 1 3 1 0

A) 1, 1, 1 B) −2, −1, 0 C) 0, 1, 3 D) −1, 0, 1

7) =3

What is the estimated probability that the cheese sample tested in NW will be Gouda? k Sample 1 2 3 4 5

Location SW SE NW SE NW

Cheese Cheddar Parmesan Gouda Gouda Cheddar

A) 40% B) 60% C) 67% D) 33%

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8) 3

What is the estimated probability that the cheese sample tested in NW will be Gouda? k = Sample 1 2 3 4 5

Location NW NW SW SE NW

Cheese Gouda Gouda Cheddar Gouda Parmesan

A) 40% B) 60% C) 34% D) 67%

9) A new applicant, age 45, is applying for a loan. Using the table below, what is the estimated probability the loan will be approved? k = 4.

Record 1 2 3 4 5 6

Age 34 24 49 32 44 53

Marital Other Married Single Married Single Single

Loan $ 5,100.00 $ 9,000.00 $ 5,000.00 $ 13,000.00 $ 13,000.00 $ 9,000.00

Risk Medium High High High High Low

Default Yes No No No No No

A) The probable default rate is 25%, the loan will be declined.

B) The probable success rate is 25%, the loan will be approved.

C) The probable success rate is 30%, the loan will be approved.

D) The probable default rate is 75%, the loan will be declined.

10) A new applicant, age 45, is applying for a loan. Using the table below, what is the estimated probability the loan will be approved? k = 4.

Record 1

Version 1

Age 34

Marital Single

Loan $ 5,000.00

Risk High

Default Yes

4

2 3 4 5 6

24 49 32 44 53

Married Single Other Married Single

$ 15,000.00 $ 7,000.00 $ 10,000.00 $ 12,000.00 $ 9,000.00

Low Medium Medium High Low

No Yes No Yes No

A) The probable default rate is 75%, the loan will be declined.

B) The probable success rate is 75%, the loan will be approved.

C) The probable success rate is 30%, the loan will be approved.

D) The probable default rate is 25%, the loan will be declined.

11)

Using the following table, which k should be used in the subsequent calculations?

k 1 2 3 4 5 6 7 8 9 10

% Misclassification 10.66666925 10.33333548 9.33333504 9.33333400 4.30 11.66666600 11.33333117 8.00 8.33333119 8.66666588

A) 8 B) 5 C) 6 D) None of the percentages should be used.

12)

Using the following table, which k should be used in the subsequent calculations?

k 1 2 3 4

Version 1

% Misclassification 10.66666667 10.33333333 9.33333333 9.33333333

5

5 6 7 8 9 10

7 11.66666667 11.33333333 10 8.33333333 8.66666666

A) 8 B) 5 C) 6 D) None of the percentages should be used.

13) If the performance measures from the training data are considerably higher than the values from the validation and test data, what could be the issue?

A) Proportion B) Sensitivity C) Duplication D) Overfitting

14) An issue with the naïve Bayes classifier is determining rare outcomes because the estimate is 0. To overcome this problem, the algorithm allows a replacement of zero probability with a nonzero value. This technique is called A) replacement.

B) smoothing.

C) discrete.

D) combinations.

15)

What is the Euclidean distance between Observation 1 and the origin point of (0, 0, 0)?

Observation 1

Version 1

Y Red

X1 0

X2 2

X3 0

6

2 3

Blue Blue

1 0

0 1

2 1

A) 2 B) 1 C) 0 D) 3

16)

What is the Euclidean distance between Observation 1 and the origin point of (0, 0, 0)?

Observation 1 2 3

Y Red Blue Blue

X1 0 2 1

X2 2 1 0

X3 0 0 1

A) 2 B) 1 C) 0 D) 3

17) The chart below is a summary of the main results of a test data set representing the population observed purchasing a virtual digital assistant. What does the accuracy rate indicate?

Metrics Metric Accuracy (#correct) Accuracy (%correct) Specificity Sensitivity (Recall) Precision F1 Score Success Class Success Probability

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Value 169 92.2 0.88222 0.6 0.82494 0.88201 1 0.5

7

A) 92.2% of the population has purchased a virtual assistant.

B) 60% of the observations are correctly classified.

C) 92.2% of the observations are correctly classified.

D) 7.8% of the observations are correctly classified.

18) The chart below is a summary of the main results of a test data set representing the population observed purchasing a virtual digital assistant. What does the accuracy rate indicate?

Metrics Metric Accuracy (#correct) Accuracy (%correct) Specificity Sensitivity (Recall) Precision F1 Score Success Class Success Probability

Value 212 88.5 0.88188 0.9 0.82453 0.88229 1 0.5

A) 88.5% of the population has purchased a virtual assistant.

B) 90% of the observations are correctly classified.

C) 88.5% of the observations are correctly classified.

D) 11.5% of the observations are correctly classified.

19) The chart below is a summary of the main results of a test data set representing the population observed purchasing a virtual digital assistant. What is the percent of the results that are incorrectly classified?

Metrics Metric Accuracy (#correct) Accuracy (%correct) Specificity Sensitivity (Recall) Precision F1 Score

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Value 187 78.3 0.88224 0.6 0.82467 0.88257

8

Success Class Success Probability Class 0 1 Overall

1 0.4 Error Report # Cases 120 120 240

# Errors 16 10 28

% Errors 13.05 12 21.70

A) 22% B) 12% C) 13.05% D) 21.70%

20) The chart below is a summary of the main results of a test data set representing the population observed purchasing a virtual digital assistant. What is the percent of the results that are incorrectly classified?

Metrics Metric Accuracy (#correct) Accuracy (%correct) Specificity Sensitivity (Recall) Precision F1 Score Success Class Success Probability Class 0 1 Overall

Value 212 88.5 0.88188 0.9 0.82453 0.88229 1 0.5 Error Report # Cases 140 100 240

# Errors 18 10 28

% Errors 12.85 10 11.5

A) 12% B) 10% C) 12.85% D) 11.5%

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21) Marta is partitioning her data set into 60% for training and 40% for validation. She is first specifying ‘Member’ as her target variable. What will she need to program to ensure consistency to fix a random seed?

A) myIndex B) trainSet C) createDataPartition D) set.seed

22) Which chart allows for the categorization of large data sets from high to low values, dividing sets of observations into an easy visual representation of the data.

A) Decile-wise chart B) Cumulative lift chart C) Scatterplot D) ROC Curve

23) This chart measures the effectiveness of a predictive model, containing both a baseline and a lift curve.

A) Decile-wise chart B) Cumulative lift chart C) Scatterplot D) ROC Curve

24)

This chart determines how well the model performs in terms of sensitivity and specificity.

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A) Decile-wise chart B) Cumulative lift chart C) Scatterplot D) ROC Curve

25) The following table is the count of observations in each class of the training data set on approvals and declines for a loan at a local bank. Using the naïve Bayes method, calculate the conditional probability of both the male and female being approved (declined) for the loan and indicate which one should be categorized with approved classification?

Approved (y = 1) Declined (y = 0)

Male (x = 1)

Female (x = 0)

23 18

35 76

A) 0.56 > 0.44 for male; 0.315 < 0.685 for female, providing male with the approved classification.

B) 0.56 > 0.44 for male; 0.40 < 0.60 for female, providing male with the approved classification.

C) 0.315 < 0.685 for male; 0.56 > 0.44 for female, providing female with the approved classification.

D) 0.315 < 0.685 for male; 0.50 >= 0.50 for female, providing female with the approved classification.

26) The following table is the count of observations in each class of the training data set on approvals and declines for a loan at a local bank. Using the naïve Bayes method, calculate the conditional probability of both the male and female being approved (declined) for the loan and indicate which one should be categorized with approved classification?

Approved (y = 1) Declined (y = 0)

Version 1

Male (x = 1)

Female (x = 0)

20 15

35 70

11

A) 0.57 > 0.43 for male; 0.333 < 0.667 for female, providing male with the approved classification.

B) 0.57 > 0.43 for male; 0.40 < 0.60 for female, providing male with the approved classification.

C) 0.333 < 0.667 for male; 0.57 > 0.43 for female, providing female with the approved classification.

D) 0.333 < 0.667 for male; 0.50 >= 0.50 for female, providing female with the approved classification.

27) A researcher is preparing data for a k-fold cross-validation. The number of groups the sample data is to be split into is 10. What would k equal in a 10-fold cross-validation?

A) k = 1 B) k = 5 C) k = 10 D) k cannot be determined until applied to a machine model.

28) The following table reflects the observations made on the color and type of vehicle, if a speeding ticket was received (1) or a warning (0), and if there was a prior driving violation (yes or no). Using the naïve Bayes calculation, predict the probability of receiving a ticket if a car is a red vehicle with a prior driving violation. Assume predictor variables are independent.

Prior Yes Yes Yes Yes No No No Yes No No

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Color Red Green Green Black Red Black Red Green Red Green

Type Sport SUV SUV Sport SUV Sport Sport SUV Sport Sport

Ticket 0 0 0 0 0 0 1 1 1 1

12

A) 0.62 B) 0.27 C) 0.14 D) 0.49

29) The following table reflects the observations made on the color and type of vehicle, if a speeding ticket was received (1) or a warning (0), and if there was a prior driving violation (yes or no). Using the naïve Bayes calculation, predict the probability of receiving a ticket if a car is a red vehicle with a prior driving violation. Assume predictor variables are independent.

Prior Yes No No No No No Yes Yes No Yes

Color Red Red Green Red Black Red Red Black Green Black

Type SUV Sport Sport SUV SUV Sport Sport Sport SUV SUV

Ticket 0 0 0 0 0 0 1 1 1 1

A) 0.88 B) 0.53 C) 0.40 D) 0.75

30) An R’s ROC curve with AUC = 0.9453 is presented below from an analysis on potential increased membership level from current basic members at Costco Wholesale. What does the AUC indicate on the prediction on increased membership enrollment among current base members? {MISSING IMAGE}

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A) The high AUC indicates an anomaly in that it requires data smoothing to match the baseline.

B) The high AUC indicates the KNN model performs well and better than the baseline model.

C) The high AUC indicates the KNN model is not predicting the level increase based on the baseline model.

D) The high AUC indicates there is a 0.05% probability the KNN model performs as predicted.

31) The marketing group and Rings Are Us is trying to predict if undergraduate or graduate students are more inclined to purchase (y = 1) or not purchase (y = 0) a class ring at graduation.

Using the following count on the training data set, calculate the conditional probability of both to determine which should be classified to the purchase group.

Purchase (y = 1) No Purchase (y = 0)

Undergraduate (x = 1)

Graduate (x = 0)

73 44

108 120

A) Undergraduate 0.62 > 0.272; Graduate 0.428 < 0.272. The undergraduate is assigned to the purchaser group.

B) Undergraduate 0.62 > 0.38; Graduate 0.474 < 0.526. The undergraduate is assigned to the purchaser group.

C) Undergraduate 0.303 > 0.272; Graduate 0.476 < 0.524. The undergraduate is assigned to the purchaser group.

D) Undergraduate 0.30 < 0.20; Graduate 0.524 > 0.476. The graduate is assigned to the purchaser group.

32) The marketing group and Rings Are Us is trying to predict if undergraduate or graduate students are more inclined to purchase (y = 1) or not purchase (y = 0) a class ring at graduation.

Using the following count on the training data set, calculate the conditional probability of both to determine which should be classified to the purchase group.

Purchase (y = 1) No Purchase (y = 0)

Version 1

Undergraduate (x = 1)

Graduate (x = 0)

70 30

100 110

14

A) Undergraduate 0.70 > 0.272; Graduate 0.428 < 0.272. The undergraduate is assigned to the purchaser group.

B) Undergraduate 0.70 > 0.30; Graduate 0.476 < 0.524. The undergraduate is assigned to the purchaser group.

C) Undergraduate 0.303 > 0.272; Graduate 0.476 < 0.524. The undergraduate is assigned to the purchaser group.

D) Undergraduate 0.30 < 0.20; Graduate 0.524 > 0.476. The graduate is assigned to the purchaser group.

33)

Specificity is A) TP ÷ (TP + FN).

B) TN ÷ (TN + FP).

C) TP ÷ (TN + FP).

D) 1 − TN ÷ (TN + FP).

34) To examine classification for k-fold cross-validation and naïve Bayes, two packages contain the necessary functions for partitioning the data. These are A) caret & klaR. B) caret & Crisp.

C) klarR & SEMMA. D) predictive & caret.

35) Mark is reviewing a partial summary of results from a test data set on a small health clinic. With an accuracy 82% (100 count), Sensitivity 55%, and Specificity 100%, can Mark correctly predict the true positive rate to identify those with the flu?

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A) Yes, because the specificity is 100% identifying healthy patients.

B) No, because the test set identified all patients with the flu.

C) No, because the sensitivity rate is only 55% in identifying those with the flu.

D) Yes, because there is a 82% accuracy rate with all healthy identified.

36) Mark is reviewing a partial summary of results from a test data set on a small health clinic. With an accuracy 75% (100 count), Sensitivity 50%, and Specificity 100%, can Mark correctly predict the true positive rate to identify those with the flu?

A) Yes, because the specificity is 100% identifying healthy patients.

B) No, because the test set identified all patients with the flu.

C) No, because the sensitivity rate is only 50% in identifying those with the flu.

D) Yes, because there is a 75% accuracy rate with all healthy identified.

37)

Of the following options, which does not represent the naïve Bayes method?

A) All predictor variables are categorical.

B) All predictor variables are independent.

C) Does not capture possible interactions between predictor variables.

D) Works best on a small data set.

38) Using the following table, what is the estimate of P(Color) = Black and what is the smoothed estimate of P(Color). k = 1.

Number 1 2 3 4 5 6 7

Version 1

Color Red Green Green Red Green Green Black

16

8 9 10

Black Red Black

A) P(Color = Black) = 0.70 and Smoothed 0.471 B) P(Color = Black) = 0.30 and Smoothed 0.380 C) P(Color = Black) = 0.3 and Smoothed 0.308 D) P(Color = Black) = 0.30 and Smoothed 0.471

39) Using the following table, what is the estimate of P(Color) = Black and what is the smoothed estimate of P(Color). k = 1.

Number 1 2 3 4 5 6 7 8 9 10

Color Black Red Green Red Black Black Red Red Green Red

A) P(Color = Black) = 0.70 and Smoothed 0.471 B) P(Color = Black) = 0.30 and Smoothed 0.380 C) P(Color = Black) = 0.30 and Smoothed 0.308 D) P(Color = Black) = 0.30 and Smoothed 0.471

40) In a decile-wise lift chart, what does the lift value of the leftmost bar imply? {MISSING IMAGE}

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A) The lift value is determined by the smoothing of the data.

B) The first 10% yields twice as many as a random selection of 10% would.

C) The bar represents 10% of the data cumulative score.

D) The first 10% is twice as prevalent as 20%.

41) To validate the model on the validation set, Mary calibrates the output of the model to examining all possible outcomes of the prediction (true positive, true negative, false positive, false negative). One way is to use a cutoff value and use functions such as the ifelse () function.

These statements are called A) prediction.

B) set.seed.

C) reference.

D) confusionMatrix.

42)

What is the Euclidean distance between Observation 2 and the origin point of (0, 0, 0)?

Observation 1 2 3

Y Red Blue Blue

X1

X2 0 2 1

X3 2 1 0

0 0 1

A) B) C) D)

43) Using the following table of the results of a paper towel study and selection, the XYZ company is making a new product with Durability = 3 and Feel = 4. Using the Euclidean distance, which Type is closest to the new observation?

Type

Version 1

Durability

Feel

Choice

18

1 2 3 4

4 2 1 3

2 3 4 3

Yes No No Yes

A) Type 2 B) Type 1 C) Type 4 D) Type 3

44) Using the following table of the results of a paper towel study and selection, the XYZ company is making a new product with Durability = 3 and Feel = 4. What is the k-nearest neighbors when k = 2?

Type 1 2 3 4

Durability 4 2 1 3

Feel 2 3 4 3

Choice No Yes No Yes

A) Types 2 & 4 B) Types 1 & 4 C) Types 2 & 3 D) Types 3 & 4

45) XYZ Streaming services wants to be able to provide programming recommendations to their customers. What type of supervised data mining technique is most used?

A) classification and regression trees B) k nearest neighbors method C) linear regression model D) Naïve Bayes classifiers

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46) Kurt wants to build a model that will allow him to predict whether it is a good day to go golfing. Using categorical weather conditions (e.g., outlook, humidity, windy, and temperature).

What type of supervised data mining technique would most likely be used?

A) classification and regression trees B) k nearest neighbors method C) linear regression model D) Naïve Bayes classifiers

47) The following table reflects the observations made on the weather outlook and temperature, if Kurt golfed (1) or did not golf (0), and if it was windy (yes or no). Using the Naïve Bayes calculation, what is the conditional probability of golfing on a sunny day given it is windy. Assume predictor variables are independent.

Windy Yes No Yes No No No Yes No No Yes

Outlook Sunny Rainy Cloudy Cloudy Sunny Sunny Sunny Cloudy Sunny Cloudy

Temp Hot Cold Mild Cold Hot Mild Hot Cold Hot Mild

Golf 0 0 0 0 1 1 1 1 1 1

A) 0.10 B) 0.38 C) 0.73 D) 0.95

48) The marketing group and Fabulous Flowers is trying to predict if the parents of undergraduate or graduate students are more inclined to purchase (y = 1) or not purchase (y = 0) flowers at graduation. Using the following count on the training data set, calculate the conditional probability of both to determine which should be classified to the purchase group.

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Purchase (y = 1) No Purchase (y = 0)

Undergraduate (x = 1)

Graduate (x = 0)

160 80

50 90

A) Undergraduate 0.667 > 0.333; Graduate 0.357 < 0.623. The undergraduate is assigned to the purchaser group.

B) Undergraduate 0.760 > 0.471; Graduate 0.529 > 0.238. The undergraduate is assigned to the purchaser group.

C) Undergraduate 0.333 < 0.667; Graduate 0.623 > 0.357. The graduate is assigned to the purchaser group.

D) Undergraduate 0.30 < 0.500; Graduate 0.529 > 0.500. The graduate is assigned to the purchaser group.

49)

What is the function used in R to create bins for a variable myData$x1?

A) breaks B) cut C) pnorm D) transform

50) The use of classifying or predicting the value to create an outcome is called scoring a record. ⊚ true ⊚ false

51) KNN is a simple data mining tool, known for developing personalized recommendations for many online company applications. ⊚ true ⊚ false

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52) Naïve Bayes classifiers are relatively simple, efficient, and assume dependency among predictors. ⊚ true ⊚ false

53)

KNN belongs to a category of mining techniques called computer-based-reasoning. ⊚ true ⊚ false

54) While k-nearest neighbors is effective as a classifier, it provides no information on predictor importance. ⊚ true ⊚ false

55) The naïve Bayes method is an unsupervised data mining technique that uses partitioning to assess model performance. ⊚ true ⊚ false

56)

When performing a naïve Bayes analysis, all predictor variables must be categorical. ⊚ true ⊚ false

57) Unlike the KNN method, the naïve Bayes method does not use the validation data set to optimize model complexity. ⊚ true ⊚ false

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58) To use the naïve Bayes method, numerical variables can be converted into discrete categories, through a process called binning, and then stored in a newly created categorical value. ⊚ true ⊚ false

59) Binning is a process where categorical data is transformed into numerical segments that can be appended back to the original data set to use a naïve Bayes method. ⊚ true ⊚ false

60) Anastasia should use a classification model to predict the starting salary of a university graduate. ⊚ true ⊚ false

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Answer Key Test name: ch 12 1) A 2) A 3) C 4) C 5) D 6) D 7) D 8) D 9) A 10) A 11) B 12) B 13) D 14) B 15) A 16) A 17) C 18) C 19) D 20) D 21) D 22) A 23) B 24) D 25) A 26) A Version 1

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27) C 28) B 29) B 30) B 31) B 32) B 33) B 34) A 35) C 36) C 37) D 38) C 39) C 40) B 41) D 42) C 43) C 44) A 45) B 46) D 47) C 48) A 49) B 50) TRUE 51) TRUE 52) FALSE 53) FALSE 54) TRUE 55) FALSE 56) TRUE Version 1

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57) TRUE 58) TRUE 59) FALSE 60) FALSE

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Student name:__________ 1) When a target variable is categorical, the CART algorithm produces a __________ tree to predict the class memberships of new cases.

A) classification B) regression C) minimum D) pruned

2)

Which tree is the least complex and contains the smallest validation error?

A) best-pruned tree B) full-grown tree C) minimum error tree D) categorical tree

3) Based on the following sorted 20 values for age, what are the possible split points? {20, 22, 24, 26, 28, 31, 32, 33, 35, 40, 41, 44, 45, 47, 49, 50, 52, 53, 55, 57} A) {20, 21, 23, 25, 27, 29.5, 31.5, 32.5, 34, 37.5, 40.5, 42.5, 44.5, 46, 48, 49.5, 51, 52.5, 54, 56} B) {21, 23, 25, 27, 29.5, 31.5, 32.5, 34, 37.5, 40.5, 42.5, 44.5, 46, 48, 49.5, 51, 52.5, 54, 56, 57} C) {0, 21, 23, 25, 27, 29.5, 31.5, 32.5, 34, 37.5, 40.5, 42.5, 44.5, 46, 48, 49.5, 51, 52.5, 54, 56} D) {21, 23, 25, 27, 29.5, 31.5, 32.5, 34, 37.5, 40.5, 42.5, 44.5, 46, 48, 49.5, 51, 52.5, 54, 56}

4) Based on the following sorted 20 values for age, what are the possible split points? {20, 22, 24, 26, 28, 31, 32, 33, 35, 40, 42, 43, 45, 47, 49, 50, 52, 53, 55, 57}

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A) {20, 21, 23, 25, 27, 29.5, 31.5, 32.5, 34, 37.5, 41, 42.5, 44, 46, 48, 49.5, 51, 52, 54, 56} B) {21, 23, 25, 27, 29.5, 31.5, 32.5, 34, 37.5, 41, 42.5, 44, 46, 48, 49, 51, 52.5, 54, 56, 57} C) {0, 21, 23, 25, 27, 29.5, 31.5, 32.5, 34, 37.5, 41, 42.5, 44, 46, 48, 49, 51, 52.5, 54, 56} D) {21, 23, 25, 27, 29.5, 31.5, 32.5, 34, 37.5, 41, 42.5, 44, 46, 48, 49.5, 51, 52.5, 54, 56}

5) Based on the following values for income, what are the possible split points? {12,665, 15,432, 22,653, 35,426, 41,967, 52,997} A) {14048.5, 19042.5, 29039.5, 38696.5, 47482, 52997} B) {12665, 14048.5, 19042.5, 29039.5, 38696.5, 47482} C) {14048.5, 19042.5, 29039.5, 38696.5, 47482} D) {14048, 19042, 29039, 38696, 47482}

6) Based on the following values for income, what are the possible split points? {12665, 15432, 28763, 34876, 41967, 52997} A) {14048.50, 22097.50, 31819.50, 38421.50, 47482, 52997} B) {12665, 14048.50, 22097.50, 31819.50, 38421.50, 47482} C) {14048.50, 22097.50, 31819.50, 38421.50, 47482} D) {14048, 22097, 31819, 38421, 47482}

7)

If 70% of the cases belong to Class 0 and 30% belong to Class 1, what is the Gini index?

A) 0.42 B) 0 C) 0.60 D) 0.14

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8)

If 80% of the cases belong to Class 0 and 20% belong to Class 1, what is the Gini index?

A) 0.32 B) 0 C) 0.40 D) 0.16

9) In reviewing the split of data, Maggie notes among the 13 cases, 2 belong to Class 1 and the remaining to Class 0. What is the Gini index for the cases and is it pure or impure?

A) 0.00 default because it is under 0.5 and pure.

B) 0.26 is closer to 0 implying relative purity.

C) 0.24 is at the halfway point is not considered pure.

D) 0.74 is over the 0.5 level and is impure.

10) In reviewing the split of data, Maggie notes among the 15 cases, 2 belong to Class 1 and the remaining to Class 0. What is the Gini index for the cases and is it pure or impure?

A) 0.00 default because it is under 0.5 and pure.

B) 0.23 is closer to 0 implying relative purity.

C) 0.27 is at the halfway point is not considered pure.

D) 0.77 is over the 0.5 level and is impure.

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11) Viewing the results in the following scatterplot, for the 11 cases to the left subset (Age < 40), two belong to Class 1 and nine belong to Class 0. In the right subset (Age ≥ 40) three belong to Class 1 and one belong to Class 0. What is the Index score for the two subsets?

A) (Age < 40) = 0.3636; (Age ≥ 40) = 0.50 B) (Age < 40) = 0.20; (Age ≥ 40) = 0.25 C) (Age < 40) = 0.298; (Age ≥ 40) = 0.375 D) (Age < 40) = 0.375; (Age ≥ 40) = 0.298

12) Robin wanted to know if the age partition chosen for her data was the best fit for her 30 case, 90% Class 1, 10% Class 0 partition. She completed the Gini impurity index with the results of (Age < 32) = 0.2235 and (Age ≥ 32) = 0.2899. What is the weighted combination and what did partition at Age 32 produce?

A) Robin was able to reduce the Gini index from 0.2899 to 0.2609 confirming the best split for age.

B) Robin was able to reduce the Gini index from 0.2899 to 0.20 confirming the best split for age.

C) Robin was able to reduce the Gini index from 0.2899 to 0.2301 confirming the best split for age.

D) Robin realized with the 0.2609 weighted average, the age split was not the best split for the age range.

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13) Robin wanted to know if the age partition chosen for her data was the best fit for her 30 case, 90% Class 1, 10% Class 0 partition. She completed the Gini impurity index with the results of (Age < 32) = 0.2034 and (Age ≥ 32) = 0.2786. What is the weighted combination and what did partition at Age 32 produce?

A) Robin was able to reduce the Gini index from 0.2786 to 0.2507, confirming the best split for age.

B) Robin was able to reduce the Gini index from 0.2786 to 0.20, confirming the best split for age.

C) Robin was able to reduce the Gini index from 0.2786 to 0.2109, confirming the best split for age.

D) Robin realized with the 0.2507 weighted average, the age split was not the best split for the age range.

14) A split at the $32,000 Income point creates a top and bottom partition. Compute the overall (weighted) Gini index given an income split of $32,000.

A) Ginisplit (Income=$32,000) = 0.2667 B) Ginisplit (Income=$32,000) = 0.0000 C) Ginisplit (Income=$32,000) = 0.4959 D) Ginisplit (Income=$32,000) = 0.3636

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15) Which description best fits the following tree structure for loan debt balance with a single age predictor?

A) The split points presented represent the MSE calculated points for Age = 35.

B) The MAD of the single age predictor is $42,964 and $32,980 respectfully.

C) The MSE split for Age = 35 is between the two partitions of $42,964 and $32,980, respectively.

D) The average loan debt balance of the two partitions are $42,964 and $32,980, respectively, when Age = 35.

16) In R, to determine the number of splits in the default classification tree, the rpart function uses what to determine when to stop growing the tree?

A) nsplit B) complexity parameter C) prune D) predict

17)

In a R complexity parameter table, the xerror column represents: A) the cross-validation errors associated with each candidate tree.

B) the recommended measure for the full tree.

C) the maximum error point for the first node split.

D) the root node type argument point.

18)

Using the following pruning table, what does the Rel Error represent?

Level 1

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CP 0.50

Num Splits 0

Rel Error 1.00

X Error 1.17

X Std Dev 0.050735

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2 3

0.44 0.00

1 2

0.50 0.06

0.73 0.10

0.061215 0.030551

A) Rel error is the calculated difference after the standard deviation is removed.

B) Rel error is the cross-validation error associated with each candidate tree.

C) Rel error is the error for predictions of the data that were used to estimate the model.

D) Rel error is the parameter associated with the candidate tree and complexity level.

19)

Using the following pruning table, which tree is the minimum error tree?

Level 1 2 3

CP 0.50 0.44 0.00

Num Splits 0 1 2

Rel Error 1.00 0.50 0.06

X Error 1.17 0.73 0.10

X Std Dev 0.050735 0.061215 0.030551

A) Level 3 B) Level 2 C) Level 1 D) Additional Levels needed to identify minimum error tree among candidate trees.

20)

Using the following chart for age and income, determine the split points for income.

Age 41 43 38 24 23 39 42 40

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Income 43,000 63,000 33,000 39,000 33,000 51,800 63,000 54,300

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A) {33000, 36000, 41000, 47400, 53050, 58650, 63000} B) {33000, 41000, 47400, 53050, 63000} C) {36000, 47400, 58650, 63000} D) {36000, 41000, 47400, 53050, 58650}

21)

Using the following chart for age and income, determine the split points for income.

Age 42 44 38 21 23 39 40 39

Income 43,000 63,000 33,000 39,000 33,000 51,000 63,000 54,000

A) {33000, 36000, 41000, 47000, 52500, 58500, 63,000} B) {33000, 41000, 47000, 52500, 63000} C) {36000, 47000, 58500, 63000} D) {36000, 41000, 47000, 52500, 58500}

22)

Which is not a purpose of running classification and regression trees (CART)?

A) To remove nodes that do not produce additional information B) To simplify and reduce complexity C) To identify the most diverse case set for the target variable D) To reduce the chances of overfitting

23) If the RMSE for the validation set is 49.95 and the RMSE for the test set is 48.45, then what range will the new data RMSE lie in?

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A) 48.45–49.95 range B) 48–49 range C) 48.45–50 range D) 48‐50 range

24) If the RMSE for the validation set is 58.78 and the RMSE for the test set is 57.12, then what range will the new data RMSE lie in?

A) 57.12–58.78 range B) 57–58 range C) 57.12–59 range D) 57–59 range

25) A regression tree was developed to predict customer spending for a hotel during football season. One of the leaf nodes consists of six cases in the training set with the following values: 312.00, 350.00, 285.00, 295.00, 397.00, 245.00. What is the predicted spending amount on a hotel for the night for a customer that falls into this leaf node?

A) 314.00 B) 315.40 C) 317.50 D) 313.80

26) A regression tree was developed to predict customer spending for a hotel during football season. One of the leaf nodes consists of six cases in the training set with the following values: 312.00, 350.00, 285.00, 295.00, 380.00, 220.00. What is the predicted spending amount on a hotel for the night for a customer that falls into this leaf node?

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A) 307.00 B) 308.40 C) 310.50 D) 306.80

27) When using the CART algorithm, the Gini index is used in the classification tree, however in a regression tree, __________ is used to measure impurity.

A) mean percentage error B) mean squared error C) mean absolute deviation D) mean absolute percentage error

28) Using the following sample of a regression prune log, the minimum error tree is decision node number 19 with a standard error of 4.689492 (not shown). Using the information provided, which decision node number represents the best-pruned tree?

Number Decision Nodes Cost Complexity 21 0 20 2.47933762 19 2.602975478 18 2.510227273 17 2.874095507 16 2.874095507 15 3.636959064 14 3.277857555 13 3.277857555 12 3.166379434 11 3.359372577 10 3.819333349 9 4.60470163 8 5.988329591 7 5.868890688 6 6.68214015 5 11.29753292

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Train. MSE 11.59784 11.79482 11.92497 12.16648 12.28455 12.42569 12.653 12.87976 13.01188 13.29182 14.02543 14.6908 14.93437 15.39484 17.27776 17.62498 18.57957

Validation MSE 22.37068 22.01144 21.99134 22.14245 22.12984 22.12984 22.69819 22.43582 22.43582 22.73964 22.74113 23.05767 23.38113 23.85401 23.74365 23.78791 24.99924

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4 3 2 1 0

17.4988252 17.11427713 23.45465646 33.43802325 32.671226

22.07933 29.89755 34.17612 50.89513 83.56636

29.80257 31.73721 27.43468 51.82928 86.50055

A) decision node number 21 B) decision node number 5 C) decision node number 4 D) decision node number 17

29) The following table reflects a partial Analytic Solver’s Performance measure for a hotel cost during an NFL game night. What is the MAD implying?

Performance Measure RMSE MAD

Value 316.64 55.98

A) The predicted mean absolute deviation is 0.56 of the mean absolute percentage error.

B) The predicted cost is relatively low, providing the need for full tree.

C) The predicted average cost is lesser than the standard error, thus impure.

D) The predicted cost on average differs from the actual cost by $55.98.

30) The following table reflects a partial Analytic Solver’s Performance measure for a hotel cost during an NFL game night. What is the MAD implying?

Performance Measure RMSE MAD

Value 310.12 50.56

A) The predicted mean absolute deviation is 0.51 of the mean absolute percentage error.

B) The predicted cost is relatively low, providing the need for full tree.

C) The predicted average cost is lesser than the standard error, thus impure.

D) The predicted cost on average differs from the actual cost by $50.56.

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31) When generating a single regression tree visually in R, the prp function is used. Based on the following example code, what does setting type = 1 mean? >prp(default_tree, type = 1, extra = 1, under = TRUE) A) type = 1 argument is the number of observations that fall into each node displayed.

B) type = 1 argument places the number of cases under each decision node in the diagram.

C) type = 1 argument allows for all nodes, except leaf nodes, to be labeled in the diagram.

D) type = 1 argument allows for the predicting variable to be displayed in root node.

32)

Using the following pruning table, which tree is the best-pruned tree?

Level 1 2 3

CP 0.50 0.44 0.00

Num Splits 0 1 2

Rel Error 1.00 0.50 0.06

X Error 1.17 0.73 0.10

X Std Dev 0.050735 0.061215 0.030551

A) Level 3 B) Level 2 C) Level 1 D) Additional Levels needed to identify best-pruned tree.

33) Which option is not one of the three common strategies used in creating ensemble models?

A) bagging B) boosting C) bootstrapping D) random Forest

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34) If the performance measures are based on a cutoff value of 0.5, then if we lower the cutoff value, more cases will be in the target class, resulting in different performance measurement values. What chart can be used to review the data that are independent of the cutoff value?

A) cumulative lift chart B) decile-wise lift chart C) ROC curve D) All options are independent of the cutoff value.

35) If predictor variables are highly correlated, then repeated sampling of the training data and a random selection of features are used to construct trees. This is an example of which strategy?

A) random forest B) bagging C) boosting D) banking

36) In a random forest model, as a guideline the user needs to select a number of the random features for each tree. If there are 225 predictor variables in the data, each tree will randomly select how many features to be included in the tree?

A) 5 B) 18 C) 225 D) 15

37) In a random forest model, as a guideline the user needs to select a number of the random features for each tree. If there are 9 predictor variables in the data, each tree will randomly select how many features to be included in the tree?

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A) 4 B) 1 C) 9 D) 3

38) When constructing the argument for a bagging tree strategy, the varImpPlot function displays feature importance graphically. For this we set the type argument to either equal 1 or 2.

If type = 2, then what does this command?

A) to show the average decrease in the predictive variable mean in a percentage form B) that R will use the average decrease in the Gini impurity index to compare the feature importance C) to show the feature importance as the average decrease in overall accuracy D) that R will use the average increase in the Gini impurity index to compare future importance

39) Ensemble tree models combine multiple single-tree models to reduce the variation in prediction error. Of the strategies, which may lead to overfitting?

A) boosting B) random Forest C) bagging D) banking

40)

In the following tree, how many leaf nodes are there?

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A) six B) seven C) two D) four

41)

In the following tree, the arrow is pointing to the __________ node.

A) exterior B) interior C) leaf D) root

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42)

In the following tree, the arrow is pointing to the __________ node.

A) exterior B) interior C) leaf D) root

43) A data file contains four predictor variables x1, x2, x3, and x4 and a numerical target variable y. Which of the following MSE split points should be selected for as the root node?

A) MSEsplit(x1=4) = 400.57 B) MSEsplit(x2=42) = 385.29 C) MSEsplit(x3=225) = 40.79 D) MSEsplit(x4=16) = 210.17

44) In which of the following situations is using an MSE to determine the split point appropriate?

A) predictor variable is categorical B) predictor variable is numerical C) target variable is categorical D) target variable is numerical

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45) The regression tree below relates credit score to number of defaults (NUM_DEF), revolving balance (REV_BAL), and years of credit history (YRS_HIST). Predict the score for an individual with two defaults, a revolving balance of $12,800, and 14 years of credit history.

A) 535 B) 588 C) 672 D) 740

46) A pure subset contains leaf nodes where cases have contradicting values to the target variable, to enhance the variable case outcomes and allow for further splits. ⊚ true ⊚ false

47) Decision trees produced by the CART algorithm are binary, meaning that there are two branches for each decision node. ⊚ true ⊚ false

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48) The best-pruned tree is the smallest set, least complex tree, with the smallest validation error. ⊚ true ⊚ false

49) Small changes in the training set, while using the CART algorithm, will result in drastically different trees. ⊚ true ⊚ false

50) A subset with the highest degree of impurity is when a 50% and 50% split occur between classes. ⊚ true ⊚ false

51) Based on the Gini index, 0.10 implies a higher degree of purity because it is closer to 0 than 0.5. ⊚ true ⊚ false

52) In a decision tree, the recursive process of partitions continues and only terminates when the Gini index reaches 0.5. ⊚ true ⊚ false

53)

To measure impurity in a regression tree, mean square error (MSE) is used. ⊚ true ⊚ false

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54) The overall MSE split for Age = 27 is $22,987,333.29 and for Age = 24 is $21,983,723.40. Of the two presented, Age = 27 is slightly higher and has a lower level of impurity for constructing a regression tree. ⊚ true ⊚ false

55) The overall MSE split for Age = 25 is $22,987,111.29 and for Age = 23 is $21,983,723.40. Of the two presented, Age = 25 is slightly higher and has a lower level of impurity for constructing a regression tree. ⊚ true ⊚ false

56) Before constructing a decision tree, one of the first steps is identifying possible splits of the predictor variable. ⊚ true ⊚ false

57) Boosting is an ensemble modeling strategy that uses the bootstrap aggrega-tion technique to create multiple training data sets by repeatedly sampling the original data with replacement. ⊚ true ⊚ false

58) The random forest technique is particularly useful if the predictor variables are not highly correlated. ⊚ true ⊚ false

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Answer Key Test name: ch 13 1) A 2) C 3) D 4) D 5) C 6) C 7) A 8) A 9) B 10) B 11) C 12) C 13) C 14) D 15) D 16) B 17) A 18) C 19) A 20) D 21) D 22) C 23) B 24) B 25) A 26) A Version 1

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27) B 28) B 29) D 30) D 31) C 32) A 33) C 34) D 35) A 36) D 37) D 38) B 39) A 40) D 41) B 42) D 43) C 44) D 45) A 46) FALSE 47) TRUE 48) FALSE 49) TRUE 50) TRUE 51) TRUE 52) FALSE 53) TRUE 54) FALSE 55) FALSE 56) TRUE Version 1

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57) FALSE 58) FALSE

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Student name:__________ 1) Which method uses the farthest distance between a pair of observations that do not belong to the same cluster?

A) The single linkage method B) The complete linkage method C) The centroid method D) The average linkage method

2) In the following AGNES algorithm, what type of linkage distance method is being displayed?

A) The centroid method B) The single linkage method C) The complete linkage method D) No linkage method is shown

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3) When using R for Agglomerative Clustering, the plot function is used to create the dendrogram as well as a banner plot. What function is used to split these results into distinct clusters?

A) aResult B) data.frame C) cutree D) view

4) The following results are a subset of a study on the demographics of a city population.

Participants were asked to respond if male (1) or female (0), current annual salary, and if they were raised in a suburb (1) or in a city (0). Based on the hierarchical clustering results, which of the following is not a valid observation that can be made?

Cluster 1 (N = 21) 2 (N = 68) 3 (N = 41) 4 (N = 34)

Salary 58,980 60,873 72,390 82,080

Gender 0.4782 0.4102 0.5142 0.5801

City 0.4657 0.568 0.2351 0.5041

A) Cluster 3 has the most observations of participants that grew up in the city.

B) Cluster 1 has a 48% male population with 47% being raised in the suburbs.

C) Cluster 4 has the highest average salary, with 58% of participants being female.

D) Cluster 2 has the most participants in the cluster with over half being raised in a suburb.

5) The following results are a subset of a study on the demographics of a city population.

Participants were asked to respond if male (1) or female (0), current annual salary, and if they were raised in a suburb (1) or in a city (0). Based on the hierarchical clustering results, which of the following is not a valid observation that can be made?

Cluster 1 (N = 21) 2 (N = 68) 3 (N = 41) 4 (N = 34)

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Salary 58,980 60,873 72,390 82,080

Gender 0.4782 0.4102 0.5142 0.5301

City 0.4877 0.568 0.2351 0.5041

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A) Cluster 3 has the most observations of participants that grew up in the city.

B) Cluster 1 has a 48% male population with 49% being raised in the suburbs.

C) Cluster 4 has the highest average salary, with 53% of participants being female.

D) Cluster 2 has the most participants in the cluster with over half being raised in a suburb.

6) In the k-Means Clustering Method, there is a general process of how k-means clustering algorithm can be classified. Which one of the following is not one of the general processes?

A) Specify the k value B) Randomly assign k observations to its nearest cluster center C) Calculate the cluster centroids D) Reassign each observation to the nearest observation point

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7) Using the following dendrogram created by using the k-means method, identify the number k was given to form the clusters present.

A) 5 B) 8 C) 9 D) 10

8) The marketing department is examining the data pulled from the retail stores over the month of December. In this time period, three items are of interest, Sound Bars, LED under counter lights, and shelving units. In researching if two of the items are purchased, if the third will be also, the following confidence level was calculated at 0.545, with an expected confidence of 0.10. Calculate the lift ratio.

A) 0.0550 B) 5.450 C) 0.1840 D) 0.445

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9) The marketing department is examining the data pulled from the retail stores over the month of December. In this time period, three items are of interest, Sound Bars, LED under counter lights, and shelving units. In researching if two of the items are purchased, if the third will be also, the following confidence level was calculated at 0.575, with an expected confidence of 0.10. Calculate the lift ratio.

A) 0.0575 B) 5.75 C) 0.6389 D) 0.475

10) Of 23,000 grocery store transactions, 1,695 have been identified as having coffee, ice cream, and chips as part of the same transaction. Calculate the support of the association rule.

A) 13.569 B) 0.0737 C) 16.95 D) 1.695

11) Of 10,000 grocery store transactions, 895 have been identified as having coffee, ice cream, and chips as part of the same transaction. Calculate the support of the association rule.

A) 11.173 B) 0.0895 C) 8.95 D) 0.895

12) Martin wants to understand the strength of association among toilet paper, milk, and eggs. Of 15,000 transactions, the number of transactions including antecedent is 2,210 whereas the number of transactions including both antecedent and consequent transactions is 890.

Calculate the confidence.

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A) 0.2067 B) 0.1473 C) 0.4027 D) 0.0593

13) Martin wants to understand the strength of association among toilet paper, milk, and eggs. Of 5,000 transactions, the number of transactions including antecedent is 1,520 whereas the number of transactions including both antecedent and consequent transactions is 690.

Calculate the confidence.

A) 0.4420 B) 0.3040 C) 0.4539 D) 0.1380

14) Of 4,900 total transactions, 1,440 transactions are the number of consequents, confidence equals 0.5289. Calculate the expected confidence.

A) 0.29 B) 2.94 C) 2,592 D) 0.7061

15) Of 5,000 total transactions, 1,400 transactions are the number of consequents, confidence equals 0.5203. Calculate the expected confidence.

A) 0.28 B) 2.80 C) 2,601 D) 0.6919

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16)

Using the following transactions, what is the frequency distribution?

Transaction 001 002 003 004 005 006 007 008 009 010

Item Latte, Scone, Muffin Coffee, Muffin Espresso, Egg, Fruit Cup Coffee, Egg, Muffin Scone, Latte, Muffin Latte, Scone, Fruit Cup Latte, Muffin, Egg Coffee, Muffin, Fruit Cup Egg, Scone, Cookie Fruit Cup, Muffin, Cookie

A) Latte-3%; Scone-4%; Muffin-7%; Egg-4%; Espresso-1%; Coffee-2%; Fruit Cup-4%; Cookie-2% B) Latte-1/5; Scone-1/4; Muffin-1/7; Egg-1/4; Espresso-1/1; Coffee-1/3; Fruit Cup-1/4; Cookie-1/4 C) Latte-1; Scone-2; Muffin-4; Egg-3; Espresso-5; Coffee-6; Fruit Cup-7; Cookie-22 D) Latte-4; Scone-4; Muffin-7; Egg-4; Espresso-1; Coffee-3; Fruit Cup-4; Cookie-2

17)

Using the following transactions, what is the frequency distribution?

Transaction 001 002 003 004 005 006 007 008 009 010

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Item Latte, Scone, Muffin Coffee, Muffin Espresso, Egg, Fruit Cup Coffee, Egg, Muffin Scone, Latte, Muffin Latte, Scone, Fruit Cup Latte, Muffin, Egg Coffee, Muffin, Fruit Cup Espresso, Scone, Cookie Latte, Muffin, Cookie

7

A) Latte-4%; Scone-4%; Muffin-7%; Egg-3%; Espresso-2%; Coffee-2%; Fruit Cup-3%; Cookie-2% B) Latte-1/6; Scone-1/4; Muffin-1/7; Egg-1/3; Espresso-1/2; Coffee-1/2; Fruit Cup-1/3; Cookie-1/2 C) Latte-1; Scone-2; Muffin-4; Egg-3; Espresso-5; Coffee-6; Fruit Cup-7; Cookie-8 D) Latte-5; Scone-4; Muffin-7; Egg-3; Espresso-2; Coffee-3; Fruit Cup-3; Cookie-2

18) Use the proportion of the following transactions that contain both Latte and Scone and calculate the Support of the association rule.

Transaction 001 002 003 004 005 006 007 008 009 010

Item Latte, Scone, Muffin Coffee, Muffin Espresso, Egg, Fruit Cup Coffee, Egg, Muffin Scone, Latte, Muffin Latte, Scone, Fruit Cup Scone, Muffin, Egg Espresso, Muffin, Fruit Cup Egg, Scone, Cookie Latte, Scone, Cookie, Muffin

A) 0.35 B) 0.60 C) 0.40 D) 0.65

19) Use the proportion of the following transactions that contain both Latte and Scone and calculate the Support of the association rule.

Transaction 001 002 003 004 005

Version 1

Item Latte, Scone, Muffin Coffee, Muffin Espresso, Egg, Fruit Cup Coffee, Egg, Muffin Scone, Latte, Muffin

8

006 007 008 009 010

Latte, Scone, Fruit Cup Latte, Muffin, Egg Coffee, Muffin, Fruit Cup Espresso, Scone, Cookie Latte, Muffin, Cookie

A) 0.25 B) 0.70 C) 0.30 D) 0.75

20) Carmen pulled transactions from the month of March to see if there is an association between coffee and breakfast sandwich purchases. After running through confidence and expected confidence calculations, she calculated the lift ratio at 1.52. What association does a calculated lift ratio of 1.52 reflect?

A) A lift ratio of 1.52 is over 1, indicating a weak association between coffee and breakfast sandwiches.

B) 1.52 implies that identifying a person who purchases coffee and a breakfast sandwich is 52% better than guessing that a random person will purchase a breakfast sandwich.

C) 1.52 implies that 52% of consumers will purchase coffee and not a breakfast sandwich.

D) The lift ratio cannot determine the association between purchases.

21)

Using R, which function is used to conduct association rule analysis?

A) itemFrequency B) apriori C) inspect D) read.transaction

22) Eva is trying to figure out the total number of possible combinations for 85 inventory items. Calculate the number for Eva. Version 1

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A) 1.28926E + 40 possible combinations B) 1.29007E + 81 possible combinations C) 3.59175E + 40 possible combinations D) 1.66428E + 165 possible combinations

23) Eva is trying to figure out the total number of possible combinations for 50 inventory items. Calculate the number for Eva. A) 2.57689E + 23 possible combinations B) 5.15378E + 47 possible combinations C) 7.17898E + 23 possible combinations D) 2.65614E + 98 possible combinations

24) Martin wants to use Gower’s coefficient to compute the distance for each variable and to convert it into a [0,1] scale. What analysis package will he use to run the analysis?

A) Analytic Solver only B) Excel and R C) R D) Excel

25)

Of the following options, which is not accurate for clustering?

A) Euclidean distance or Manhattan distance measures for numerical variables and matching.

B) AGNES takes each observation in the data initially and forms its own cluster.

C) Hierarchical clustering commonly follows agglomerative and divisive clustering.

D) Cluster analysis is where small amounts of data are organized against larger statistical sets.

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26) The use of error sum of squares to measure the loss of information that occurs when observations are clustered describes which method?

A) The centroid method B) The complete linkage method C) The average linkage method D) Ward’s method

27)

k-means clustering algorithm can be summarized as all of the following except for A) can be either numerical or character variables.

B) specify the k value.

C) randomly assign k observations as cluster centers.

D) assign each observation to its nearest cluster center.

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28) Tosh Marketing Group wants to identify customers who are likely to purchase high-end appliances for a new marketing campaign. After collecting data from recent customers, the following plot was created showing age and spend variables with 3 distinct clusters. Which cluster offers the best target with the highest spend for marketing materials?

A) The lower-left cluster reflects the younger age range and should be the target of the marketing.

B) The upper-middle cluster reflects a higher spend for appliances versus the lower-left and far-right clusters.

C) The far-right cluster is in the middle and the highest spend for appliances.

D) Both the upper-middle cluster and the lower-left cluster are the highest spends for appliances.

29) In reviewing purchases at Costco on a given Saturday, 750 transactions out of 1,900 included toilet paper, detergent, and clothing or {toilet paper, detergent} => {clothing}.

Calculate the support of the association rule.

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A) 0.395 B) 1.395 C) 1,900 D) 7.50

30) In reviewing purchases at Costco on a given Saturday, 385 transactions out of 1,000 included toilet paper, detergent, and clothing or {toilet paper, detergent} => {clothing}.

Calculate the support of the association rule.

A) 0.385 B) 1.385 C) 1,000 D) 3.85

31) Toilet paper and detergent are the antecedent, where 1,770 of the 15,000 transactions include both items. Of the overall 15,000 transactions, 610 include toilet paper, detergent, and the consequent of clothing. Using the association rule, what is the confidence?

A) 0.0407 B) 71.980 C) 0.118 D) 0.3446

32) Toilet paper and detergent are the antecedent, where 890 of the 10,000 transactions include both items. Of the overall 10,000 transactions, 234 include toilet paper, detergent, and the consequent of clothing. Using the association rule, what is the confidence?

A) 0.0234 B) 20.826 C) 0.089 D) 0.2629

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33) Costco is known for lower prices for bulk items. After calculating the support and confidence values for {toilet paper} => {avocados}, there appears to be a strong association because a large percentage of customers purchase these items. However, to avoid assuming the strength of association, what option should be done to confirm the strength of the association?

A) Review the transactions for accuracy B) Rerun the confidence C) Calculate the lift ratio D) If the support and confidence are strong then no need to run anything more

34) Based on the following table, what is the frequency distribution of the most purchased item?

Transaction 001 002 003 004 005

Item bread, yogurt, cheese milk, yogurt yogurt, cheese yogurt, bread milk, cheese, bagels

A) bagels B) milk C) yogurt D) bread

35) Based on the following table, what is the frequency distribution of the most purchased item?

Transaction 001 002 003 004 005

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Item bread, milk, cheese milk, yogurt bread, cheese milk, bread milk, cheese, bagels

14

A) bread B) cheese C) milk D) yogurt

36) Based on the following table, what is the proportion of the transactions that include both milk and bread?

Transaction 001 002 003 004 005

Item bread, milk, bagels milk, yogurt bagels, cheese bagels, bread milk, bagels, bagels

A) 0.80 B) 0.60 C) 0.40 D) 0.20

37) Based on the following table, what is the proportion of the transactions that include both milk and bread?

Transaction 001 002 003 004 005

Item bread, milk, cheese milk, yogurt bread, cheese milk, bread milk, cheese, bagels

A) 0.80 B) 0.60 C) 0.20 D) 0.40

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38) The Corner Market is using 2,100 transactions on item purchases for analysis. Based on initial results, the manager noticed eggs and potato chips were frequently in the same transactions. After calculating the confidence and the expected confidence on the data, 0.54 and 0.71 respectively, they want to run a lift ratio to ensure there is a positive association. Calculate the lift ratio and determine if the association is positive or negative.

A) 1.315, positive number, positive association B) 0.761, under one, negative association C) 1.315, over one, positive association D) −1.315, negative number, negative association

39) The Corner Market is using 2,500 transactions on item purchases for analysis. Based on initial results, the manager noticed eggs and potato chips were frequently in the same transactions. After calculating the confidence and the expected confidence on the data, 0.54 and 0.62 respectively, they want to run a lift ratio to ensure there is a positive association. Calculate the lift ratio and determine if the association is positive or negative.

A) 0.871, positive number, positive association B) 0.871, under one, negative association C) 1.148, over one, positive association D) −1.148, negative number, negative association

40) Sara is a marketing analysis manager for a top cereal producer. Part of her job is to review product sales to grocery chains and if the contracted product shelf placement is the optimal location to maximize sales. Because of the large data sets, she is only interested in creating a small number of clusters to view the results. What type of clustering method would work best?

A) hierarchical clustering B) agglomerative clustering C) divisive clustering D) k-means clustering

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41) Amazon uses searches and items purchased to create future product marketing recommendations. Additionally, demographics drive additional potential products to be recommended. To do this, what type of market basket analysis is used?

A) Information Rule B) Supervised Data Analysis C) Association Rule D) k-mean

42)

Using R, what function is used to view the rules by their lift ratios?

A) lookup B) apriori C) rules D) sort

43) In cluster analysis, measures are used to form clusters. However, when large data sets are imported into R, sometimes the variables do not share the same format. To overcome this, you standardize the data using the __________ function.

A) score B) scale C) standard D) dist

44) When using R for Agglomerative Clustering, the cutree function is used to split results into distinct clusters. What function is used to create the dendrogram as well as a banner plot?

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A) aResult B) data.frame C) plot D) view

45) What type of (dis)similarity measure should you use when your variables result in observations with mostly 0s as the values for binary variables?

A) Euclidean distance B) Jaccard’s coefficient C) Manhattan distance D) matching coefficient

46) To what cluster should Record 2 be assigned, given the following distances to the cluster centroids?

Record ID 1 2 3

Dist.Cluster-1 Dist.Cluster-2 4.0503384 1.9593924 3.8503278

3.830521 4.235910 1.230822

Dist.Cluster-3 Dist.Cluster-4 1.793345 3.394509 2.977934

4.166309 3.899235 2.781135

A) Cluster 1 B) Cluster 2 C) Cluster 3 D) Cluster 4

47) To what cluster should Record 3 be assigned, given the following distances to the cluster centroids?

Record ID 1 2

Version 1

Dist.Cluster-1 Dist.Cluster-2 4.0503384 1.9593924

3.830521 4.235910

Dist.Cluster-3 Dist.Cluster-4 1.793345 3.394509

4.166309 3.899235

18

3

3.8503278

1.230822

2.977934

2.781135

A) Cluster 1 B) Cluster 2 C) Cluster 3 D) Cluster 4

48) The forming of groups into internally homogeneous groups where each has a unique characteristic, different from other groups, is called cluster analysis. ⊚ true ⊚ false

49)

The most commonly used approach for hierarchical clustering is divisive clustering. ⊚ true ⊚ false

50) When using k-means clustering, the number of clusters are specified at the end of the analysis to remove overlapping clusters. ⊚ true ⊚ false

51) When evaluating large data sets, it is customary to cluster large data sets using the kmeans to reduce the computation of measures during each iteration compared to hierarchical clustering methods. ⊚ true ⊚ false

52) When using R, after the data is imported, set.seed function is used to set the random seed and the k function sets the k parameters to preselect the number of clusters.

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⊚ ⊚

true false

53)

In understanding the association rules, it is best to think of them as an If-Then statement. ⊚ true ⊚ false

54)

Under the association rule, a lift ratio between 0 and 1 indicates a positive association. ⊚ true ⊚ false

55) If-Then logical statements are constructed with the If portion being the consequent and the Then being the antecedent. ⊚ true ⊚ false

56) The Ward’s method is the use of a different algorithm to minimize the dissimilarity within clusters by using error sum of squares. ⊚ true ⊚ false

57)

A dendrogram allows for a visual inspection of the clustering results. ⊚ true ⊚ false

58) Because clustering is essentially an unsupervised technique for data exploration, the appropriate technique would be the one that makes the fewest clusters.

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⊚ ⊚

true false

59) Gower’s coefficient is commonly used to measure the distance between two observations when using agglomerative hierarchical clustering to analyze mixed data. ⊚ true ⊚ false

60) Hierarchical clustering methods are more efficient than k-means clustering methods for large data sets. ⊚ true ⊚ false

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Answer Key Test name: ch 14 1) B 2) B 3) C 4) C 5) C 6) D 7) A 8) B 9) B 10) B 11) B 12) C 13) C 14) A 15) A 16) D 17) D 18) C 19) C 20) B 21) B 22) C 23) C 24) C 25) D 26) D Version 1

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27) A 28) B 29) A 30) A 31) D 32) D 33) C 34) C 35) C 36) D 37) D 38) B 39) B 40) D 41) C 42) D 43) B 44) C 45) D 46) A 47) B 48) TRUE 49) FALSE 50) FALSE 51) TRUE 52) FALSE 53) TRUE 54) FALSE 55) FALSE 56) TRUE Version 1

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57) TRUE 58) FALSE 59) TRUE 60) FALSE

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Student name:__________ 1)

What are the two types of decision models?

A) deterministic and stochastic B) dynamic and static C) deterministic and static D) dynamic and stochastic

2) Which one of the following isnot one of the three primary components in a decision model?

A) input variables B) processes C) target outputs D) test variables

3) What type of cell references is Sophia using in the following Excel formula: =IF(D4=“Management”, B$12, C$12)?

A) absolute and relative B) absolute and mixed C) relative and mixed D) relative and absolute

4) What type of cell references is Sophia using in the following Excel formula: =IF($B3=“Non-Management”, $B$12, $C$12)?

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A) absolute and relative B) absolute and mixed C) relative and mixed D) relative and absolute

5) The following partially completed Excel spreadsheet contains information for computing an annual budget. Assuming salary and bonus values are net of taxes, what is the formula for calculating the estimated annual savings (cell B11) for Mr. Ramirez this year? Note: Consider the columns as labeled "A" and "B" and rows as labeled "1-12". Use these labels to determine the correct formula.

A 1

B

Ramirez Monthly Budget

2 3

Variables:

4 5 6 7 8 9

Annual salary One-time bonus Fixed expenses Salary savings Bonus savings

10

Annual Estimates

11

Savings

12

Spending money

$ 54,900.00 $ 10,000.00 50% 10% 50%

A) =B4*B7 B) =(B4+B5)*(B5+B8) C) =B4*B7 + B5*B8 D) =64900*0.60

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6) The following partially completed Excel spreadsheet contains information for computing an annual budget. Assume salary and bonus values are net of taxes and fixed expenses are based on annual salary. What is the formula for calculating the estimated annual spending money (cell B12) for Mr. Ramirez this year? Consider the columns as labeled "A" and "B" and rows as labeled "1-12". Use these labels to determine the correct formula.

A 1

B

Ramirez Monthly Budget

2 3

Variables:

4 5 6 7 8 9

Annual salary One-time bonus Fixed expenses Salary savings Bonus savings

10

Annual Estimates

11

Savings

12

Spending money

$ 54,900.00 $ 10,000.00 50% 10% 50%

A) =B4 − B4*B6 B) =B4*B6 + B5*B8 C) =B4 + B5 − B4*B7 − B5*B8 D) =B4 + B5 − B4*B6 − B11

7) The following partially completed spreadsheet contains information for computing an annual budget. Is this a deterministic or stochastic model and why?

A 1

B

Ramirez Monthly Budget

2

Version 1

3

3

Variables:

4 5 6 7 8 9

Annual salary One-time bonus Fixed expenses Salary savings Bonus savings

10

Annual Estimates

11

Savings

12

Spending money

$ 54,900.00 $ 10,000.00 50% 10% 50%

A) deterministic because all model parameters are known with certainty, and the output of the model is fully determined by the model parameters B) deterministic because some randomness is incorporated in the model; therefore, the model will produce different outputs even with the same inputs C) stochastic because all model parameters are known with certainty, and the output of the model is fully determined by the model parameters D) stochastic because some randomness is incorporated in the model; therefore, the model will produce different outputs even with the same inputs

8) Which of the following statements best describes the key elements of an influence diagram?

A) It is made up of nodes (decision variables) and arrows, starting with the final target output variable and decomposes the decision to show how variables are influenced by input variables at each level.

B) It starts with input variables at the top and builds its way down to intermediate, and then, final output variables (nodes) using nodes (decision variables) and arrows.

C) It creates all of the input and output formulas that will be used in the spreadsheet model where each formula should each be placed.

D) None of the statements accurately describe the key elements of an influence diagram.

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9) Below is an influence diagram for a basic make versus buy manufacturing decision. What is the final target output variable?

A) buy cost per unit B) cost difference C) fixed costs D) units

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10) Below is an influence diagram for a basic make versus buy manufacturing decision. What of the following is not an intermediate output variable?

A) total buy costs B) total make cost C) total variable costs D) variable cost per unit

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11) Mad Merna’s Halloween Emporium is planning for this year’s busiest time and is debating between buying or making this year’s limited edition witch’s hat. Merna estimates that she will need 1,000 units. Her normal vendor charges $45.75 per hat including shipping. To make the hat, Merna will incur $15,000 in fixed costs and an additional $30.25 per hat in variable costs. Using the influence diagram below, create a spreadsheet model and determine 1) whether Merna should make or buy this year’s limited edition witch’s hat and 2) how much she will save.

A) buy; $250 B) buy; $500 C) make; $250 D) make; $500

12) Bindle-Featherston Company has a new product. It estimates that first year sales will result in 150,000 units sold and a growth rate of 12% per year. The sales price is $25 per unit and will increase by 5% per year. The fixed costs are $200,000 per every 100,000 units and variable costs are $10 per unit. Variable costs will increase by 3% per year due to increased material and labor costs. What is the total profit expected over the next five years?

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A) $11,893,906.56 B) $14,031,510.54 C) $14,525,656.33 D) $15,093,381.95

13) Bindle-Featherston Company has a new product. It estimates that first year sales will result in 150,000 units sold and a growth rate of 12% per year. The sales price is $25 per unit and will increase by 5% per year. The fixed costs are $200,000 per every 100,000 units and variable costs are $10 per unit. Variable costs will increase by 3% per year due to increased material and labor costs. What is the profit expected in year three?

A) $2,279,600.00 B) $2,422,400.00 C) $2,789,970.56 D) $2,963,150.56

14) Bindle-Featherston Company has a new product. It estimates that first year sales will result in 150,000 units sold and a growth rate of 12% per year. The sales price is $25 per unit and will increase by 5% per year. The fixed costs are $200,000 per every 100,000 units and variable costs are $10 per unit. Variable costs will increase by 3% per year due to increased material and labor costs. What is the total profit expected over the next five years if the sales growth rate is 15% instead?

A) $11,893,906.56 B) $14,031,510.54 C) $14,525,656.33 D) $15,093,381.95

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15) Use the Excel template below for a vertical one-way data table that shows loan amounts in $2,500 increments starting from $10,000 and their monthly payment amounts, assuming the loan term and annual interest rate are 5 years and 3.9%, respectively. What is the formula for the total loan amount in cell A10 if we want to be able to copy the formula to the remainder of the cells? Consider the columns as labeled "A" and "B" and rows as labeled "1-13". Use these labels to determine the correct formula.

A 1

B

Loan Payment Calculator

2 3 4 5 6

Loan amount Loan term (in years) Annual interest rate Monthly payment

$ 10,000.00 5 3.9%

7 8 9 10

Loan Amount $ 10,000.00

Monthly Payment ($183.71)

11 12 13

A) = A9 + 2500 B) = $A$9 + 2500 C) = A$9 + 2500 D) = $B$3 + 2500

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16) Use the Excel template below for a vertical one-way data table that shows loan amounts in $2,500 increments starting from $10,000 and their monthly payment amounts, assuming the loan term and annual interest rate are 5 years and 3.9%, respectively. What is the formula for the monthly payment of this loan in cell B9 if we want to be able to copy the formula to the remainder of the cells? Consider the columns as labeled "A" and "B" and rows as labeled "1-13".

Use these labels to determine the correct formula.

A 1

B

Loan Payment Calculator

2 3 4 5 6

Loan amount Loan term (in years) Annual interest rate

$ 10,000.00 5 3.9%

7 8 9 10

Loan Amount $ 10,000.00

Monthly Payment ($183.71)

11 12 13

A) =PMT($B$5,$B$4,A9) B) =PMT($B$5/12,$B$4*12,A9) C) =PMT(A9, $B$4*12,$B$5/12) D) =PMT(B5/12,B4*12,A9)

17) Use the Excel template below to create a vertical one-way data table that shows loan amounts in $2,500 increments starting from $10,000 and their monthly payment amounts, assuming the loan term and annual interest rate are 5 years and 3.9%, respectively. What is the monthly payment for a loan of $20,000? Consider the columns as labeled "A" and "B" and rows as labeled "1-13". Use these labels to determine the correct formula.

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A 1

B

Loan Payment Calculator

2 3 4 5 6

Loan amount Loan term (in years) Annual interest rate

$ 10,000.00 5 3.9%

7 8 9 10

Loan Amount $ 10,000.00

Monthly Payment ($183.71)

11 12 13

A) $367.43 B) $413.36 C) $867.35 D) $4,479.93

18) Bindle-Featherston Company has a new product. It estimates that first year sales will result in 150,000 units sold and a growth rate of 12% per year. The sales price is $25 per unit and will increase by 5% per year. The fixed costs are $200,000 per every 100,000 units and variable costs are $10 per unit. Variable costs will increase by 3% per year due to increased material and labor costs. Calculate the breakeven point using Excel’s Goal Seek in year one.

A) 8,000 B) 12,333 C) 13,334 D) 20,000

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19) Bindle-Featherston Company has a new product. It estimates that first year sales will result in 150,000 units sold and a growth rate of 12% per year. The sales price is $25 per unit and will increase by 5% per year. The fixed costs are $200,000 per every 100,000 units and variable costs are $10 per unit. Variable costs will increase by 3% per year due to increased material and labor costs. Using Excel’s Goal Seek calculate the number of units that will need to be sold in year one to earn a profit of $100,000.

A) 8,000 B) 12,333 C) 13,334 D) 20,000

20) The following partially completed Excel spreadsheet contains information for computing an annual budget. Assume salary and bonus values are net of taxes and fixed expenses are based on annual salary. Use Excel’s Goal Seek to determine what percentage will Ramirez need to save monthly to have $20,000 in his savings account by the end of the year.

A 1

B

Ramirez Monthly Budget

2 3

Variables:

4 5 6 7 8 9

Annual salary One-time bonus Fixed expenses Monthly savings Bonus savings

10

Annual Estimates

11

Savings

12

Spending money

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$ 54,900.00 $ 10,000.00 50% 10% 50%

12

13

Fixed expenses

A) 10.00% B) 13.85% C) 22.39% D) 27.32%

21) The benefit of using Excel’s Scenario Manager over data tables and Excel’s Goal Seek is that it allows you to create and compare__________.

A) one variable at a time B) multiple possible scenarios based on multiple input variables C) conceptual models of the business problem D) randomness that models deterministic models have

22) Trulovia Manufacturing is looking to purchase a machine that will increase its efficiency in their manufacturing process. Trulovia wants to use Scenario Manager to evaluate the following four scenarios based on possible purchase prices and interest rates for a 10-year loan.

Which option results in the lowest monthly payment?

Scenario 1 2 3 4

Equipment Cost $ 145,000 $ 147,500 $ 150,000 $ 155,000

Loan Term 9 years 10 years 11 years 9 years

Interest Rate 7% 5% 4% 2%

A) 1 B) 2 C) 3 D) 4

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23) Trulovia Manufacturing is looking to purchase a machine that will increase its efficiency in their manufacturing process. Trulovia wants to use Scenario Manager to evaluate the following four scenarios based on possible purchase prices and interest rates for a 10-year loan.

Which option results in the highest monthly payment?

Scenario 1 2 3 4

Equipment Cost $ 145,000 $ 147,500 $ 150,000 $ 155,000

Loan Term 9 years 10 years 11 years 9 years

Interest Rate 7% 5% 4% 2%

A) 1 B) 2 C) 3 D) 4

24) Trulovia Manufacturing is looking to purchase a machine that will increase its efficiency in their manufacturing process. Trulovia wants to use Scenario Manager to evaluate the following four scenarios based on possible purchase prices and interest rates for a 10-year loan.

What is the monthly payment for Scenario 2?

Scenario 1 2 3 4

Equipment Cost $ 145,000 $ 147,500 $ 150,000 $ 155,000

Loan Term 9 years 10 years 11 years 9 years

Interest Rate 7% 5% 4% 2%

A) $1,161.74 B) $1,260.41 C) $1,385.72 D) $1,448.26

25) The following spreadsheet and formula results in what type of common spreadsheet error?

A 1

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Total Employee Salaries

14

2 3 4 5 6 7 8

Employee Name Fletcher Davis Simon Jennings Ainsley Smart Casey Manning Moira Fowler

Salary $ 54,900.00 $ 35,000.00 $ 22,500.00 $ 85,000.00 $ 49,000.00

9

Grand Total

=SUM(B4:B9)

A) circular reference B) data validation C) missing data D) mixed reference

26) Which Excel feature will let you enforce a rule where the user can only enter whole numbers in a particular cell?

A) data validation B) error alert C) new rule D) what-if analysis

27) Which option in Data Validation would you choose if you want to make sure only certain options can be selected for a particular cell?

A) any value B) date C) list D) whole number

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28)

Which of the following isnot one of the model auditing tools available in Excel?

A) data validation B) show formulas C) trace dependents D) trace precedents

29) Jeremy Valdez found three #N/A formula errors in his spreadsheet. Thinking an error in one cell is causing errors in the following cells, he decided to use the__________ formula auditing tool.

A) show formulas B) trace dependents C) trace precedents D) watch window

30) Jeremy Valdez thinks he has found the source of a series of #N/A errors in his spreadsheet. He is ready to begin correcting the errors but would like a way to monitor how the results change when the formulas are modified. What model auditing tool should he use?

A) show formulas B) trace dependents C) trace precedents D) watch window

31)

There are two types of decision models, deterministic and stochastic. ⊚ ⊚

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true false

16

32) Stallion Airlines knows for certain what they can charge per seat, as well as the labor, fuel, and plane maintenance costs for its flight from Minneapolis, MN to Sioux Falls, SD. Stallion Airlines would likely use a stochastic model to compute total profit for a full flight. ⊚ ⊚

true false

33) When entering a formula, careful consideration should be given to the choice of absolute, relative, and mixed references so that the formula can be copied to other cells that require a similar calculation. ⊚ ⊚

true false

34) The textbook recommends using an influence diagram to avoid critical design flaws or bugs. ⊚ ⊚

true false

35) Spreadsheet modeling is the process of designing, constructing, and testing spread-sheet models. ⊚ ⊚

true false

36) What-if analysis, also called sensitivity analysis, helps the decision maker predict how the target outputs of a spreadsheet model would change if one or more input variables are changed.

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⊚ ⊚

true false

37) In a two-way data table, you can compare how different values of one input variable influence the return on invested capital by holding the remaining input variables constant. ⊚ ⊚

true false

38) Excel’s Goal Seek is the what-if analysis that can determine the exact value of the target outcome variable based on a desired input. ⊚ ⊚

true false

39) A formula that refers to its own cell, either directly or indirectly, is called a circular reference. ⊚ ⊚

true false

40) Trace Dependents allows you to identify cells that contain data that are part of the formula in the active cell. Trace Precedents, on the other hand, allows you to identify cells whose values are affected by the active cell. ⊚ ⊚

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Answer Key Test name: ch 15 1) A 2) D 3) C 4) B 5) C 6) D 7) A 8) A 9) B 10) D 11) D 12) B 13) C 14) D 15) A 16) B 17) A 18) C 19) D 20) D 21) B 22) C 23) D 24) B 25) A 26) A Version 1

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27) C 28) A 29) B 30) D 31) TRUE 32) FALSE 33) TRUE 34) TRUE 35) FALSE 36) TRUE 37) FALSE 38) FALSE 39) TRUE 40) FALSE

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Student name:__________ 1) When using Excel to model uncertainty, the RAND function and BINOM.INV functions are used. What key is used to redraw new observations, changing the output?

A) Return key B) ALT4 C) F4 D) F9

2) A CNBC study in 2017 determined only 32% of adults have zero to only a couple of hundred dollars in a savings account. With n = 7 and p = 0.32, using the binomial probability distribution, what is the mean and the standard deviation of the population? Hint: σ =

A) 7 and 1.52 respectively B) 2.24 and 1.2342 respectively C) 3.5 and 1.82 respectively D) 1.8 and 1.5232 respectively

3) A CNBC study in 2017 determined only 35% of adults have zero to only a couple of hundred dollars in a savings account. With n = 5 and p = 0.35, using the binomial probability distribution, what is the mean and the standard deviation of the population? Hint: σ =

A) 5 and 1.13 respectively B) 1.75 and 1.0665 respectively C) 2.50 and 1.1705 respectively D) 1.55 and 1.1375 respectively

4) _____________ analytics refers to using simulation and optimization algorithms to provide advice on ‘what we should do.’

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A) Descriptive B) Diagnostic C) Predictive D) Prescriptive

5) Serena only wants to run risk analyses on situations with uncertainty, which of the following scenarios does she not need to run a risk analysis on?

A) A certificate of deposit at a bank B) An investment in shares of stock C) The level of inventory at any point in time D) The ideal interior size of vehicle prototype

6) Consider the function: y = (x1 + x2)x3. In the most likely scenario, the values of x1, x2, and x3 are 4, 21, and 4, respectively. In the worst-case scenario, the values of the three variables are 1, 10, and 2, respectively. In the best-case scenario, the values of the vari-ables are 8, 32, and 5, respectively. What is the range of y?

A) 13 – 200 B) 22 – 100 C) 22 – 200 D) 100 – 200

7) Consider the function: y = (x1 + x2)x3. In the most likely scenario, the values of x1, x2, and x3 are 4, 21, and 4, respectively. In the worst-case scenario, the values of the three variables are 1, 10, and 2, respectively. In the best-case scenario, the values of the variables are 8, 32, and 5, respectively. What is the most likely value of y?

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A) 22 B) 88 C) 100 D) 200

8)

Which of the following is an example of a discrete random variable?

A) number of bankruptcies B) amount of data usage C) inches of rainfall D) increase in stock price

9)

Which of the following is an example of a continuous random variable?

A) number of bankruptcies B) amount of data usage C) number of students D) total t-shirts

10)

Which of the following is an example of a discrete random variable?

A) height of a person B) amount of data usage C) number of students D) increase in stock price

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11) Black Beauty Cosmetics estimates that 20% of its customers will make a purchase. With n = 15 and p = 0.2, using the binomial probability distribution, what is the mean and the standard deviation of the population? Hint: σ = A) 15 and 2, respectively B) 0.30 and 0.0864 respectively C) 3 and 1.5492 respectively D) 1.75 and 1.0665 respectively

12) How many defects should Black Beauty Cosmetics expect out of 20 mascara tubes if they usually identify 3 defects in every 15?

A) 3 B) 4 C) 5 D) 6

13)

The Monte Carlo simulation is also known as A) stochastic & probabilistic.

B) stochastic & deterministic.

C) deterministic.

D) probabilistic & deterministic.

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14) Below is a scatterplot of a Monte Carlo simulation of 100 profit distribution scenarios based on employing 3 tailors or 4 tailors. What would you recommend to the manager if she is looking to minimize variability?

A) Four tailors because profitability is higher.

B) Four tailors because risk is lower.

C) Three tailors because profitability has a lower standard deviation.

D) Three tailors because risk is lower.

15) Below is a scatterplot of a Monte Carlo simulation of 100 profit distribution scenarios based on employing 3 tailors or 4 tailors. What would you recommend to the manager if she is looking to maximize profit?

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A) Four tailors because profitability is higher.

B) Four tailors because risk is lower.

C) Three tailors because profitability has a lower standard deviation.

D) Three tailors because risk is lower.

16) Black Beauty Cosmetics wants to model demand (normal distribution with μ = 95 units and σ = 10.67) and a production rate (uniform distribution between 23 units and 31 units) for producing mascara tubes. Using R, what formulas are needed to generate 100 random observations for demand and production rate, respectively?

A) output1 <- sample(95, 100, replace = TRUE); output2 <- sample(23:31, 100, replace = TRUE) B) output1 <- rnorm(100, 95, 10.67); output2 <- rnorm(100, 23, 31) C) output1 <- rnorm(23:31, 100, replace = TRUE); output2 <- sample(100, 95, 10.67) D) output1 <- rnorm(100, 95, 10.67); output2 <- sample(23:31, 100, replace = TRUE)

17)

Deterministic process is A) created by random variable selection.

B) created by constraint identification.

C) a Monte Carlo simulation.

D) a precise estimation based on known variables.

18) What type of probability distribution will Elva need to use to generate random observations for delivery times so that she can set up the drivers’ schedules?

A) Continuous B) Exponential C) Normal D) Triangular

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19) What type of probability distribution will Evelyn need to use to generate random observations for the expected amount of time between customer purchases?

A) Continuous B) Exponential C) Normal D) Triangular

20) What type of probability distribution will Axel need to use to generate random observations for weekly demand for ventilators needed at a hospital?

A) Continuous B) Empirical C) Exponential D) Triangular

21) What type of probability distribution will Herman need to use to generate random observations when he only knows his best, worst, and most likely scenarios?

A) Continuous B) Empirical C) Exponential D) Triangular

22) Javier needs to project how many vases to produce each month for his new customer. The new customer only provided the minimum, maximum, and average number of vases the flower shop goes through each month. Which type of probability distribution should Javier use to generate random observations?

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A) Continuous B) Empirical C) Triangular D) Variable

23) What type of data is used with the continuous uniform distribution when generating random observations?

A) data with unknown distributions B) data generated from symmetrical distributions C) data generated from nonsymmetrical distributions D) data with constant probability within a specified range

24) What type of data is used with the exponential uniform distribution when generating random observations?

A) data with unknown distributions B) data generated from symmetrical distributions C) data generated from nonsymmetrical distributions D) data with constant probability within a specified range

25) What type of data is used with the empirical distribution when generating random observations?

A) continuous data generated from symmetrical distributions B) continuous data generated from nonsymmetrical distributions C) historical data with discrete outcomes D) data with constant probability within a specified range

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26) What type of data is needed to use the triangular distribution when generating random observations?

A) minimum, maximum, and mode values B) worst, best, and most likely case scenarios C) either the min, max, and mode or the worst, best, and most likely case scenarios D) neither the min, max, and mode or the worst, best, and most likely case scenarios

27) Finn has historical data but does not know the distribution pattern of the data. What type of graph or chart could they use to plot the data to identify potential patterns?

A) box plots B) histogram C) line chart D) scatterplot

28)

Which formulas are used for an empirical probability distribution?

A) and B) y = (−1/λ) × ln(1 − x) C) min +

D) μ =

29)

and

and

Which formulas are used for a triangular probability distribution?

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A) and B) y = (−1/λ) × ln(1 − x) C)

D)

and

and

30) What function is used in R to generate random observations from the continuous uniform distribution?

A) cumsum B) rexp C) runif D) ifelse

31) What function is used in R to generate random observations from the continuous uniform distribution?

A) rexp B) runif C) ifelse D) cumsum

32) Prescriptive analytics is the process of using decision analysis tools to improve decision making. ⊚ true ⊚ false

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33) The Monte Carlo simulation relies solely on average values to capture risk and uncertainty. ⊚ true ⊚ false

34) Binomial and Poisson distributions are the two most relevant probability distributions for many business and nonbusiness applications. ⊚ true ⊚ false

35) Continuous uniform distribution, also known as the rectangular distribution, captures the time that elapses between occurrences. ⊚ true ⊚ false

36) Risk can be defined as the possibility or probability that an undesirable outcome may occur. ⊚ true ⊚ false

37) In Excel, random observations can be generated, however random seed cannot be set, requiring values to be cut and pasted. ⊚ true ⊚ false

38) Risk analysis is a process of examining and assessing different scenarios to better understand the possibility of both intended and undesired outcomes. ⊚ true ⊚ false

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39) Katerina’s team used best practices when they only ran a best case scenario risk analysis before debuting a new electric car. ⊚ true ⊚ false

40) Many real-life events and the data obtained from these events may not conform to any theoretical probability distributions. In these situa-tions, we may construct a triangular distribution. ⊚ true ⊚ false

41) A Bayesian model is used to run simulations and analyze risk and uncertainty by recreating a real-world process. . ⊚ true ⊚ false

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Answer Key Test name: ch 16 1) D 2) B 3) B 4) D 5) A 6) C 7) C 8) A 9) B 10) C 11) C 12) B 13) A 14) C 15) A 16) D 17) D 18) A 19) B 20) B 21) D 22) C 23) D 24) C 25) C 26) A Version 1

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27) B 28) D 29) C 30) C 31) A 32) TRUE 33) FALSE 34) TRUE 35) FALSE 36) TRUE 37) TRUE 38) TRUE 39) FALSE 40) FALSE 41) FALSE

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Student name:__________ 1)

Which of the following is not an example of where linear programming can be used?

A) choosing a combination of products to maximize profits B) developing a mix of investment options to maximize return C) using available resources to minimize delivery costs D) using a simulation to analyze demand for a particular product

2)

What special case in linear programming does the following graph depict?

A) infeasibility B) multiple optimal solutions C) objectivity D) unboundedness

3) Lane Accessories has a growing demand for custom Apple watch designer bands. The current manufacturing costs are $120 per hour to operate, for each hour of operation, 210 black band designs and 180 multi-color designs are completed. However, Lane found a new larger manufacturing space that will cost $160 per hour and produce 325 black design bands and 289 multi-color bands per hour. Lane has newly placed orders to restock other retail outlets nationwide for 6,000 black band designs and 5,000 multi-color bands. Because Lane is out of inventory, she needs to decide how many hours to operate each facility to fulfill orders while minimizing cost. Formulate the minimization function for production costs.

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A) Production Costs = 120x1 + 160x2 B) Production Costs = (120x1 + 390) + (160x2 + 614) C) Production Costs = 390x1 + 614x2 D) Production Costs = (120x1 + 6,000) + (160x2 + 5,000)

4)

Which one of the following is not an essential component of linear programming?

A) random B) an objective function C) decision variables D) constraints

5) Lane Accessories has a growing demand for custom Apple watch designer bands. The current manufacturing costs are $120 per hour to operate, for each hour of operation, 210 black band designs and 180 multi-color designs are completed. However, Lane found a new larger manufacturing space that will cost $160 per hour and produce 325 black design bands and 289 multi-color bands per hour. Lane has newly placed orders to restock other retail outlets nationwide for 6,000 black band designs and 5,000 multi-color bands. Because Lane is out of inventory, she needs to decide how many hours to operate each facility to fulfill orders while minimizing cost. Formulate the orders for black design bands.

A) 210x1 + 325x2 ≥ 6,000x3 B) 210x1 + 325x2 ≥ 6,000 C) 180x1 + 210x2 ≥ 6,000 D) 180x1 + 325x2 ≥ 5,000

6) Sampson Limited produces two products that can be produced on either of two machines.

Each month, only 500 hours of time are available on each machine. The time required to produce each item by hour and machine is: Machine 1 Product 1

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Machine 2 3 2

Product 2

5 4 Month 1 Demand Month 2 Demand Month 1 Price Month 2 Price

Product 1 Product 2

100 120

160 110

$ 45 $ 65

$ 10 $ 35

The demand and price point for each product that customers are willing to pay are above. The company goal is to maximize revenue from sales from the next two months. Based on the provided information, how many constraints does this problem have excluding the nonnegativity constraints?

A) 2 total constraints B) 4 total constraints C) 6 total constraints D) 8 total constraints

7) By viewing the following Excel snapshot, in Excel Solver what would be entered to formulate the Objective Function for E3?

A) =SUM(B5:D11,C8:D11) B) =SUMPRODUCT(B5*C5) + (B8*C8) C) =SUMPRODUCT(B5+B8) * (C5+C8) D) =SUMPRODUCT(B5*B8) + (C5*C8)

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8) By viewing the following Excel snapshot, in Excel Solver what would be entered to formulate B11, Material (units) for Quantity?

A) =SUMPRODUCT(B2+B8) * (C2+C8) B) =SUMPRODUCT(B2*B8) + (C2*C8) C) =SUMPRODUCT(B2*D15) + (C2*D16) D) =SUMPRODUCT(B2*D15) * (C2*D16)

9) Based on the following sensitivity results on constraints, what is the optimal linear programming solution for Products 1 and 2?

Constraints Cell Name $B$11 Material (units) Quantity $B$12 Machine (hours) Quantity $B$13 Labor (hours)

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Final Value 1,050

Shadow Constraint Price R.H. Side 0 1,300

Allowable Increase 1 E+30

Allowable Decrease 250

525

0

750

1

E+30

225

800

0

1,400

1

E+30

500

4

Quantity $B$15 Number of Product 1 Units Produced $B$16 Number of Product 2 Units Produced

380

5

300

125

300

140

8

150

83.33333

150

A) Product 1: 380 units and Product 2: 234 units B) Product 1: 425 units and Product 2: 140 units C) Product 1: 380 units and Product 2: 140 units D) Product 1: 125 units and Product 2: 84 units

10) Based on the following sensitivity results on constraints, what is the optimal linear programming solution for Products 1 and 2?

Constraints Cell Name $B$11 Material (units) Quantity $B$12 Machine (hours) Quantity $B$13 Labor (hours) Quantity $B$15 Number of Product 1 Units Produced $B$16 Number of Product 2 Units Produced

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Final Value 1,050

Shadow Constraint Price R.H. Side 0 1,300

Allowable Increase 1E+30

Allowable Decrease 250

525

0

750

1E+30

225

900

0

1,400

1E+30

500

300

5

300

125

300

150

8

150

83.33333333

150

5

A) Product 1: 300 units and Product 2: 234 units B) Product 1: 425 units and Product 2: 150 units C) Product 1: 300 units and Product 2: 150 units D) Product 1: 125 units and Product 2: 84 units

11) Based on the following sensitivity results on constraints, how would an increase in machine hours impact the linear programming solution?

Constraints Cell Name $B$11 Material (units) Quantity $B$12 Machine (hours) Quantity $B$13 Labor (hours) Quantity $B$15 Number of Product 1 Units Produced $B$16 Number of Product 2 Units Produced

Final Value 1,050

Shadow Constraint Price R.H. Side 0 1,300

Allowable Increase 1E+30

Allowable Decrease 250

525

0

750

1E+30

225

900

0

1,400

1E+30

500

300

5

300

125

300

150

8

150

83.33333333

150

A) The hours can have an infinite increase without altering the solution.

B) The hours can only decrease without altering the solution.

C) The hours can have an increase by 750 units before price decreases.

D) The hours cannot be predicted with the information presented.

12) Based on the following Variable cells segment of a Solver Sensitivity Report, what range does Product 1 per-unit profit fall between at 390 units?

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Variable cells Cell Name $B$8 Decision Variable (to produce) Product 1 $C$8 Decision Variable (to produce) Product 2

Final Value 390

Reduced Objective Allowable Allowable Cost Coefficient Increase Decrease 0 6 0.76 0.884

180

0

8

1

0.7

A) The range is between 0.884 and 0.76.

B) The range is between 0.884 and 6.76.

C) The range is between 5.116 and 6.76.

D) The range can only be determined with more details from the sensitivity report.

13) Based on the following Variable cells segment of a Solver Sensitivity Report, what range does Product 1 per-unit profit fall between at 300 units?

Variable cells Cell Name $B$8 Decision Variable (to produce) Product 1 $C$8 Decision Variable (to produce) Product 2

Final Reduced Objective Allowable Allowable Value Cost Coefficient Increase Decrease 300 0 5 0.75 0.889

150

0

8

1

0.5

A) The range is between 0.889 and 0.75.

B) The range is between 0.889 and 5.75.

C) The range is between 4.111 and 5.75.

D) The range can only be determined with more details from the sensitivity report.

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14)

What special case in linear programming does the following graph depict?

A) infeasibility B) multiple optimal solutions C) objectivity D) unboundedness

15) In a linear programming model, the parameter values in an objective function are referred to as the A) objective function coefficients.

B) parameter function.

C) constraint coefficient.

D) quantitative function.

16) At Taste of Thyme coffee shop a dirty chai latte creates a profit point of $2.93 for a small and $3.26 for a large. In a month, 225 small lattes were sold and 285 large lattes. As a fast growing demand item with fall approaching, the demand is estimated at 430 and 420 per month.

The amount of machine time needed to produce the lattes is 5 minutes and 7 minutes each or for a month, 16.67 hours a month for a small and 33.25 hours a month for a large. What is the maximization function for profit?

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A) Profit = 430x1 + 420x2 B) Profit = 2.93x1 + 3.26x2 C) Profit = 16.67x1 + 33.25x2 D) Profit = 2.58x1 + 2.96x2

17) At Taste of Thyme coffee shop a dirty chai latte creates a profit point of $2.85 for a small and $3.80 for a large. In a month, 200 small lattes were sold and 285 large lattes. As a fast growing demand item with fall approaching, the demand is estimated at 400 and 420 per month.

The amount of machine time needed to produce the lattes is 5 minutes and 7 minutes each or for a month, 16.67 hours a month for a small and 33.25 hours a month for a large. What is the maximization function for profit?

A) Profit = 400x1 + 420x2 B) Profit = 2.85x1 + 3.80x2 C) Profit = 16.67x1 + 33.25x2 D) Profit = 2.50x1 + 3.50x2

18) At Taste of Thyme coffee shop a dirty chai latte creates a profit point of $2.90 for a small and $3.29 for a large. In a month, 240 small lattes were sold and 285 large lattes. As a fast growing demand item with fall approaching, the demand is estimated at 450 and 420 per month.

The allotted machine time for both lattes is 100 hours. The amount of machine time needed to produce the lattes is 5 minutes and 9 minutes each or for a month, 16.67 hours a month for a small and 34.57 hours a month for a large. What is the corresponding parameters formulation for machine time?

A) 5x1 + 9x2 ≤ 100 B) 16.67x1 + 34.57x2 ≥ 100 C) 2.90x1 + 3.34x2 ≤ 100 D) 16.67x1 + 34.57x2 ≤ 100

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19) At Taste of Thyme coffee shop a dirty chai latte creates a profit point of $2.85 for a small and $3.80 for a large. In a month, 200 small lattes were sold and 285 large lattes. As a fast growing demand item with fall approaching, the demand is estimated at 400 and 420 per month.

The allotted machine time for both lattes is 90 hours. The amount of machine time needed to produce the lattes is 5 minutes and 7 minutes each or for a month, 16.67 hours a month for a small and 33.25 hours a month for a large. What is the corresponding parameters formulation for machine time?

A) 5x1 + 7x2 ≤ 90 B) 16.67x1 + 33.25x2 ≥ 90 C) 2.85x1 + 3.85x2 ≤ 90 D) 16.67x1 + 33.25x2 ≤ 90

20) Which of the following statements is false regarding Excel’s Solver and R's capabilities regarding multiple optimal solutions in linear programming?

A) Solver and R can only show one optimal solution.

B) Solver and R can provide a simple way to find the other optimal solutions.

C) If the allowable increase of a decision variable is zero, the linear programming problem may have multiple optimal solutions.

D) If the allowable decrease of a decision variable is zero, the linear programming problem may have multiple optimal solutions.

21)

The first step in performing linear programming is A) to generate random numbers.

B) to formulate a problem into a series of mathematical expressions.

C) to create intervals.

D) to analyze the file for patterns.

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22) Smith Industries has a growing demand for custom athleisure wear. The current manufacturing costs are $90 per hour to operate, for each hour of operation, 110 black yoga pants and 150 multi-color yoga pants per hour are completed. However, Smith found a new larger space that will cost $125 per hour and produce 175 black yoga pants and 140 multi-color yoga pants per hour. Smith has newly placed orders to restock retail outlets for 4,000 black pants and 3,500 multi-color pants. Because Smith is out of inventory, she needs to decide how many hours to operate each facility to fulfill orders while minimizing cost. Formulate the minimization function for production costs.

A) Production Costs = (90x1 + 260) + (125x2 + 315) B) Production Costs = 215x1 + 575x2 C) Production Costs = 90x1 + 125x2 D) Production Costs = (90x1 + 5,000) + (125x2 + 3,500)

23) Smith Industries has a growing demand for athleisure wear. The current manufacturing costs are $90 per hour to operate, for each hour of operation, 110 black yoga pants and 150 multi-color yoga pants are completed. However, Smith found a new larger manufacturing space that will cost $120 per hour and produce 175 black yoga pants and 140 multi-color yoga pants per hour. Smith has newly placed orders to restock other retail outlets nationwide for 5,000 black yoga pants and 3,500 multi-color yoga pants. Because Smith is out of inventory, she needs to decide how many hours to operate each facility to fulfill orders while minimizing cost.

Formulate the orders for multi-color yoga pants.

A) 250x1 + 190x2 ≥ 8,500 B) 150x1 + 140x2 ≥ 5,000 C) 150x1 + 140x2 ≥ 3,500 D) 110x1 + 175x2 ≥ 3,500

24) The range of objective function coefficients between the allowable increase and decrease, within which the optimal solution for an LP problem remains unchanged is called the _____________.

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A) shadow price B) binding constraint C) range of feasibility D) range of optimality

25) The range of values of a binding constraint, within which the shadow price remains the same is called the _____________.

A) shadow price B) binding constraint C) range of feasibility D) range of optimality

26) Hwang Manufacturing has a growing demand for high quality carving knives. The current manufacturing costs are $150 per hour to operate, for each hour of operation, 210 straight blades and 175 curved blades are completed. However, CEO Kelly Hwang found a new larger space that will cost $175 per hour and produce 250 straight blades and 190 curved blades per hour. Kelly has newly placed orders to restock retail outlets for 6,000 straight blades and 4,500 curved blades. Because Kelly is out of inventory, she needs to decide how many hours to operate each facility to fulfill orders while minimizing cost. Formulate the minimization function for production costs.

A) Production Costs = 150x1 + 175x2 B) Production Costs = 325x1 + 825x2 C) Production Costs = (150x1 + 460) + (175x2 + 365) D) Production Costs = (150x1 + 6,000) + (175x2 + 4,500)

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27) Hwang Manufacturing has a growing demand for high quality carving knives. The current manufacturing costs are $150 per hour to operate, for each hour of operation, 210 straight blades and 175 curved blades are completed. However, CEO Kelly Hwang found a new larger space that will cost $175 per hour and produce 250 straight blades and 190 curved blades per hour. Kelly has newly placed orders to restock retail outlets for 6,000 straight blades and 4,500 curved blades. Because Kelly is out of inventory, she needs to decide how many hours to operate each facility to fulfill orders while minimizing cost. Formulate the orders for straight blades.

A) 460x1 + 325x2 ≥ 10,500 B) 210x1 + 250x2 ≥ 6,000 C) 175x1 + 190x2 ≥ 4,500 D) 150x1 + 175x2 ≥ 6,000

28) Serena Limited produces two products that can be produced on either of two machines.

Each month, only 320 hours of time are available on each machine. The time required to produce each item by hour and machine is: Machine 1 Product 1 Product 2

Machine 2

2 2 4 3 Month 1 Demand Month 2 Demand

Product 1 Product 2

125 130

150 120

Month 1 Price $ 45 $ 65

Month 2 Price $ 30 $ 55

The demand and price point for each product that customers are willing to pay are above. The company goal is to maximize revenue from sales from the next two months. Based on the provided information, how many constraints does this problem have excluding the nonnegativity constraints?

A) 2 total constraints B) 4 total constraints C) 6 total constraints D) 8 total constraints

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29) In a linear programming solution, constraints with slack are called _____________ constraints.

A) binding B) nonbinding C) surplus D) overage

30)

If x1 and x2 are outside of the feasibility region, what does that mean?

A) The first constraint is a valid solution to be considered.

B) Solutions outside of the feasibility region can be considered as viable.

C) The farthest outside the feasibility region is the optimal solution.

D) Solutions outside of the feasibility region cannot be considered.

31) As a manager, Mike uses linear programming to formulate a problem into a series of mathematical expressions. _____________ refers to the choices or alternatives Mike selects to minimize or maximize the value of his goals.

A) Objective function B) Decision variables C) Optimization D) Feasible region

32) Numerical values that are associated with objective function, decision variables, and constraints are called _____________.

A) parameters B) binary C) value D) assumptions

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33) Which of the following is not one of the “special cases” in linear programming that was discussed in this chapter?

A) Interminableness B) Infeasibility C) Multiple optimal solutions D) Unboundedness

34) At Tasty Sweets ice cream shop, a banana split creates a profit point of $1.75 for a small and $2.50 for a large. Last month, 100 small banana splits and 185 large banana splits were sold.

However, with summer approaching, the demand is estimated to increase. The maximum allotted labor for both banana splits sizes is 50 hours for the month. Based on last month’s sales the amount of labor needed to produce one small and large banana split is 3 minutes and 5 minutes, respectively for a total of 5 hours a month for a small and 15.416 hours a month for a large. What is the corresponding constraint for monthly labor?

A) 3x1 + 5x2 ≤ 50 B) 5x1 + 15.416x2 ≤ 50 C) 5x1 + 15.416x2 ≥ 50 D) 1.75x1 + 2.50x2 ≤ 50

35) A minimization linear programming model will usually have at least one constraint with the ≤ sign. ⊚ true ⊚ false

36) The constraints in the minimi-zation problem usually describe maximum requirements that must be met. ⊚ true ⊚ false

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37) Sometimes, in real-world applications, a linear programming model may yield multiple optimal solutions that have the same maximized or minimized value of the objective function. ⊚ true ⊚ false

38) A linear programming problem is infeasible when there is no solution for which all constraints in the LP model are satisfied. ⊚ true ⊚ false

39) Linear programming problems are bounded when the objective function can attain an infinite value without violating any constraints. ⊚ true ⊚ false

40) Linear programming is an optimization technique in which an exponential function is maximized or minimized when subjected to resource constraints. ⊚ true ⊚ false

41)

The objective function is a mathematical representation of an objective. ⊚ true ⊚ false

42) Constraints with slack or surplus in the linear programming solutions are called binding constraints.

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⊚ ⊚

true false

43) The shadow price, or dual price, of a constraint is an indication of the change in the optimized value of an objective function in a one unit change in a binding constraint. ⊚ true ⊚ false

44) The optimal solution to a linear programming problem can be found at the boundary or corner of the feasibility region in a graphic model. ⊚ true ⊚ false

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Answer Key Test name: ch 17 1) D 2) A 3) A 4) A 5) B 6) C 7) D 8) B 9) C 10) C 11) A 12) C 13) C 14) D 15) A 16) B 17) B 18) D 19) D 20) B 21) B 22) C 23) C 24) D 25) C 26) A Version 1

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27) B 28) C 29) B 30) D 31) B 32) A 33) A 34) B 35) FALSE 36) FALSE 37) TRUE 38) TRUE 39) FALSE 40) FALSE 41) TRUE 42) FALSE 43) TRUE 44) TRUE

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Student name:__________ 1)

Which of the following isnot an example of a linear or integer programming application?

A) capital budgeting B) distribution of electricity C) transportation D) workforce

2)

Which of the following is not an example of a nonlinear programming application?

A) assignment of employees to tasks B) distribution of electricity C) investment volatility or risk D) vehicle traffic in busy city

3) When using R, how do you ensure all decision variables are integers when solving an integer programming problem?

A) all.int = FALSE B) all.int = TRUE C) all.num = INT D) all.num = TRUE

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4)

What type of programming problem is this graph depicting?

A) global programming B) integer programming C) linear programming D) nonlinear programming

5) What type of nonlinear programming algorithm would you use in your software program, if your nonlinear function is smooth and differentiable?

A) derivative-based B) derivative-free C) evolutionary D) simplex

6) Williams LLC wants to run advertisements to promote their digital game, Fractions are Fun! during the month of March in celebration of Pi Day. They have four options, Facebook, Instagram, TikTok, and Twitter. They have decided they can afford to fund at least two but no more than three platforms. What formula should they add to reflect this constraint?

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A)

and

B)

and

C)

and

D)

and

7) Creighton Medical Supplies manufactures medical gowns and scrubs for hospitals and medical offices. Each medical gown uses about 30 square feet of material and uses about 0.8 hours of machine time. Each set of scrubs uses about 50 square feet of material and uses about 1.3 hours of machine time. For this production cycle the company secured 1,500 square feet of material and has up to 80 hours available. Given the following template, what constraint(s) would be set in Solver add-in?

A) $B$13:$B$14 <= $D$13:$D$14 B) $B$13:$B$14 >= $D$13:$D$14 C) $B$10:$C$10 = integer; $B$13:$B$14 <= $D$13:$D$14 D) $B$10:$C$10 = integer; $B$13:$B$14 >= $D$13:$D$14

8) Pumpkin Enterprises is evaluating 4 projects for potential capital funding. However, they only have $75,000 for Year one, and $60,000 for the remaining years to invest. Unfortunately, they cannot receive any additional funds unspent from year to year. Based on the following data, what is the constraint for the Year 1 integer programming formulation?

Cash Investment Year 1

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Project 1 50

Project 2 45

Project 3 30

Project 4 40

3

Year 2 Year 3 Year 4 Expected Return

10 10 0 350,000

10 10 10 400,000

10 10 0 385,000

10 10 10 450,000

A) B) C) D)

9) Old Orchard Brewing Company has three warehouses that service ten locations for Good Eats Grocery. The first warehouse supplies up to 210 pallets a week, the second warehouse supplies 480 pallets a week, and the third warehouse supplies 175 pallets a week. The orders received weekly from Good Eats are 40 pallets for the1st five locations and 50 pallets for locations 6 through 10. Formulate the objective function for the total shipping costs.

A) Maximize: Total Shipping Cost = B) Minimize: Total Shipping Cost = C) Maximize: Total Shipping Cost = D) Maximize: Total Shipping Cost =

10) Old Orchard Brewing Company has two warehouses that service three locations for Good Eats Grocery. The first warehouse supplies up to 210 pallets a week and the second warehouse supplies 480 pallets a week. The orders received weekly from Good Eats are 240 pallets for the location 1 and 2 and 350 pallets for location 3. Formulate the warehouse capacity constraint(s).

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A) Capacity all warehouses = B) Capacity all warehouses = C) Capacity of warehouse 1

; Capacity of warehouse 2

D) Capacity of warehouse 1

; Capacity of warehouse 2

11) A kitchen cabinet shop with four cabinet makers receives a contract to work on a project that requires four different tasks: milling, sanding, building, and staining. The project needs to be completed as soon as possible. The owner wants to assign each of the four tasks to an employee with an overall goal of minimizing the project’s completion time and has recorded the amount of time each employee has taken to complete the four tasks from previous projects. The accompa-nying table shows the employees’ average completion time (in hours) for each task.

Define the design variables.

Employees Kristy Michelle Mike Ronaldo

Milling 5.65 6.05 5.19 5.48

Cutting 4.05 3.91 5.27 5.99

Finishing 4.73 4.20 4.55 4.62

Assembly 3.41 5.57 6.16 4.82

A) xij representing employeei being assigned to taskj B) xij representing taski being assigned to employeej C) wij representing average completion time for employeei, taskj D) wij representing average completion time for taski, employeej

12) A kitchen cabinet shop with four cabinet makers receives a contract to work on a project that requires four different tasks: milling, sanding, building, and staining. The project needs to be completed as soon as possible. The owner wants to assign each of the four tasks to an employee with an overall goal of minimizing the project’s completion time and has recorded the amount of time each employee has taken to complete the four tasks from previous projects. The accompa-nying table shows the employees’ average completion time (in hours) for each task.

Define the objective function.

Employees

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Milling

Cutting

Finishing

Assembly

5

Kristy Michelle Mike Ronaldo

5.65 6.05 5.19 5.48

4.05 3.91 5.27 5.99

4.73 4.20 4.55 4.62

3.41 5.57 6.16 4.82

A) Maximize: Total Completion Time = B) Maximize: Total Completion Time = C) Minimize: Total Completion Time = D) Minimize: Total Completion Time =

13) A department chair at Sassafrass University has four remaining classes to staff (intro accounting, advanced accounting, finance, and data analytics) and three instructors available to teach those classes. The department chair would like to assign each of the three classes to an instructor with an overall goal of maximizing overall instructor ratings. The accompa-nying table shows the instructors’ average course ratings for each course. Define the objective function assuming that each instructor is only available to teach one class. Using Solver, who will teach the finance class?

Instructors Ingrid Mark Mary Uday

Intro Accounting 4.65 4.05 3.89 4.62

Advanced Accounting 4.05 3.91 4.24 4.77

Finance 4.65 4.67 4.73 4.50

Data Analytics 4.63 4.85 4.06 4.25

A) Ingrid B) Mark C) Mary D) Uday

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14) A department chair at Sassafrass University has four remaining classes to staff (intro accounting, advanced accounting, finance, and data analytics) and three instructors available to teach those classes. The department chair would like to assign each of the three classes to an instructor with an overall goal of maximizing overall instructor ratings. The accompanying table shows the instructors’ average course ratings for each course. Define the objective function assuming that each instructor is only available to teach one class. Using Solver, what will be the total and average evaluation scores?

Instructors Ingrid Mark Mary Uday

Intro Accounting 4.65 4.05 3.89 4.62

Advanced Accounting 4.05 3.91 4.24 4.77

Finance 4.65 4.67 4.73 4.50

Data Analytics 4.63 4.85 4.06 4.25

A) 16.66; 4.17 B) 17.33; 4.33 C) 18.25; 4.64 D) 19; 4.75

15) The owners of CaroKat Café need to create a new schedule for its staff. The café is open from 6 am to 10 pm seven days a week. All of the café’s employees are full time and work a schedule of five 8-hour days followed by two days off. Recently the owners have been busier on Fridays and Saturdays due to music acts. Caro and Kat like that all of their employees work full time, what is the objective function?

Days Monday Tuesday Wednesday Thursday Friday Saturday Sunday

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Workers Needed 6 6 5 8 10 10 5

7

A) Maximize: Total number of employees B) Maximize: Total number of employees C) Minimize: Total number of employees D) Minimize: Total number of employees

16) The owners of CaroKat Café need to create a new schedule for its staff. The café is open from 6 am to 10 pm seven days a week. All of the café’s employees are full time and work a schedule of five 8-hour days followed by two days off. Recently the owners have been busier on Fridays and Saturdays due to music acts. Caro and Kat like that all of their employees work full time, what is the constraint for Wednesday if Monday = x1?

Days Monday Tuesday Wednesday Thursday Friday Saturday Sunday

Workers Needed 6 6 5 8 10 10 5

A) x1 + x2 + x3 + x4 + x5 ≥ 5 B) x1 + x2 + x3 + x6 + x7 ≥ 5 C) x3 + x4 + x5 + x6 + x7 ≥ 5 D) x1 + x4 + x5 + x6 + x7 ≥ 5

17) The owners of CaroKat Café need to create a new schedule for its staff. The café is open from 6 am to 10 pm seven days a week. All of the café’s employees are full time and work a schedule of five 8-hour days followed by two days off. Recently the owners have been busier on Fridays and Saturdays due to music acts. Caro and Kat like that all of their employees work full time, what is the minimum number of employees needed to be able to fully staff the café?

Days Monday

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Workers Needed 6

8

Tuesday Wednesday Thursday Friday Saturday Sunday

6 5 8 10 10 5

A) 8 B) 9 C) 10 D) 11

18) Martin is evaluating 4 projects for potential capital funding. However, he only has $145,000 for Year one, and $35,000 for the remaining years to invest. Unfortunately, he cannot receive any additional funds unspent from year to year. Based on the following data, what is the constraint for the Year 1 integer programming formulation?

Cash Investment Year 1 Year 2 Year 3 Year 4 Expected Return

Project 1 50 10 10 0 350,000

Project 2 45 10 10 10 400,000

Project 3 30 10 10 0 385,000

Project 4 40 10 10 10 450,000

A) = ∑i=14 cixi B) = ∑i=1 4ai1xi ≤ 145,000 C) = ∑i=14 aixi ≤ 35,000 D) = ∑i=14 ai1xi ≤ 165,000

19) Martin is evaluating 4 projects for potential capital funding. However, he only has $90,000 for Year one, and $50,000 for the remaining years to invest. Unfortunately, he cannot receive any additional funds unspent from year to year. Based on the following data, what is the constraint for the Year 1 integer programming formulation?

Cash Investment Year 1 Year 2

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Project 1 50 10

Project 2 45 10

Project 3 30 10

Project 4 40 10

9

Year 3 Year 4 Expected Return

10 0 350,000

10 10 400,000

10 0 385,000

10 10 450,000

A) <p>= B) <p>=

≤ 90,000

C) <p>=

≤ 50,000

D) <p>=

≤ 165,000

20) Martin is evaluating 4 projects for potential capital funding. However, he only has $50,000 for Year one, and $40,000 for the remaining three years to invest. Each of the four projects is projected to generate an expected return of $350,000, $400,000, $385,000, and $450,000, respectively. Based on the summary information provided, what is the Expected Return objective function?

A) Expected Return = ∑i=1 4cixi B) Expected Return = 50x1 + 40x2 C) Expected Return = ∑i=x14cixi D) Expected Return = ∑i=1 ai1xi6i

21) Martin is evaluating 4 projects for potential capital funding. However, he only has $90,000 for Year one, and $50,000 for the remaining three years to invest. Each of the four projects is projected to generate an expected return of $350,000, $400,000, $385,000, and $450,000, respectively. Based on the summary information provided, what is the Expected Return objective function?

A) <p>Expected Return = B) Expected Return = 90x1 + 50x2 C) <p>Expected Return = D) <p>Expected Return =

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22) The following Excel Solver Results show capital projects selected for investment. When constructing the Solver parameters, the “subject to the constraints” need to be set. What two constraints are required?

A) $G$8 AND B10:E10 = binary B) B8:E8 = binary AND F2:F5 <= G2:G5 C) B10:E10 = binary AND F2:F5 => G2:G5 D) B10:E10 = binary AND F2:F5 <= G2:G5

23) In Excel, the worksheet needs to be prepared prior to running the Solver. As such, the total (total return), cell G8, shows the total of the expected return for approved projects. What is the formula that must be set in G8 to capture the results accurately?

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A) =SUM(B8:E8 * B10:E10) B) =SUMPRODUCT(B8:E8, B10:E10) C) =SUMPRODUCT(B8:E8 * B10:E10) D) =SUMPRODUCT(B8:E8, G2:G5)

24) Be it a capital project selection or a transportation, a manager’s goals are what drives the projects. In integer programming the manager’s goal translates to the __________ function in programming.

A) objective B) fractional C) matrix D) transport

25) Watkins Trucking has two warehouses that service four retail locations for Harmons Hardware. The first warehouse supplies up to 180 pallets a week and the second warehouse supplies 300 pallets a week. The orders received weekly from Harmons are 75 pallets for the 1st location and 50 pallets for locations 2, 3, & 4. Formulate the objective function for the total shipping costs.

Watkins 1 Watkins 2

Harmons 1

Harmons 2

Harmons 3

Harmons 4

2.85 3.10

3.32 2.90

4.85 3.48

6.25 4.90

A) <p>Minimize: Total Shipping Cost = B) <p>Minimize: Total Shipping Cost = C) <p>Maximize: Total Shipping Cost = D) <p>Maximize: Total Shipping Cost =

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26)

When using R, how do you store shipping costs?

Watkins 1 Watkins 2

Harmons 1

Harmons 2

2.90 4.20

3.46 2.97

A) >unit.costs <- matrix(2.90, 3.46, 4.20, 2.97) B) >unit.costs <- unit.costs(2.90, 3.46, 4.20, 2.97) C) >unit.costs <- matrix(c(2.90, 3.46, 4.20, 2.97), nrow=2, byrow=TRUE) D) >unit.costs <- unit.costs(2.90, 3.46, 4.20, 2.97), nrow=2, byrow=TRUE)

27)

When using R, how do you store shipping costs?

Watkins 1 Watkins 2

Harmons 1

Harmons 2

2.85 3.10

3.32 2.90

A) >unit.costs <- matrix(2.85, 3.32, 3.10, 2.90) B) >unit.costs <- unit.costs(2.85, 3.32, 3.10, 2.90) C) >unit.costs <- matrix(c(2.85, 3.32, 3.10, 2.90), nrow=2, byrow=TRUE) D) >unit.costs <- unit.costs(2.85, 3.32, 3.10, 2.90), nrow=2, byrow=TRUE)

28) The owners of CaroKat Café need to create a new schedule for its staff. The café is open from 6 am to 10 pm seven days a week. All of the café’s employees are full time and work a schedule of five 8-hour days followed by two days off. Recently the owners have been busier on Fridays and Saturdays due to music acts. Caro and Kat like that all of their employees work full time, how many employees start their work week on Wednesday?

Days Monday Tuesday Wednesday Thursday Friday Saturday Sunday

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Workers Needed 6 6 5 8 10 10 5

13

A) 0 B) 1 C) 2 D) 3

29) Chasing Windmills, manufacturer of wind turbines, is looking to open one or more new plants to service the Midwest region. They want to keep the distance between the plant and five metropolitan areas to less than 300 miles. Using Solver add-in, what is the minimum number of plants needed to service the Midwest?

From/To Chicago Des Moines Indianapolis Omaha St. Louis

Chicago 0 333 184 467 296

Des Moines Indianapolis 333 184 0 477 477 0 134 611 349 242

Omaha 467 134 611 0 432

St. Louis 296 349 242 432 0

A) 0 B) 1 C) 2 D) 3

30) Chasing Windmills, manufacturer of wind turbines, is looking to open one or more new plants to service the Midwest region. They want to keep the distance between the plant and five metropolitan areas to less than 300 miles. What is the objective function?

From/To Chicago Des Moines Indianapolis Omaha St. Louis

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Chicago 0 333 184 467 296

Des Moines Indianapolis 333 184 0 477 477 0 134 611 349 242

Omaha 467 134 611 0 432

St. Louis 296 349 242 432 0

14

A) Minimize: Total number of plant sites B) Minimize: Total number of plant sites C) Maximize: Total number of plant sites D) Maximize: Total number of plant sites

31) Chasing Windmills, manufacturer of wind turbines, is looking to open one or more new plants to service the Midwest region. They want to keep the distance between the plant and five metropolitan areas to less than 300 miles. What is the constraint for Indianapolis if Chicago = x1 where xi is 0 or 1?

From/To Chicago Des Moines Indianapolis Omaha St. Louis

Chicago 0 333 184 467 296

Des Moines Indianapolis 333 184 0 477 477 0 134 611 349 242

Omaha 467 134 611 0 432

St. Louis 296 349 242 432 0

A) x1 + x3 + x5 ≥ 1 B) x1 + x3 + x5 ≤ 1 C) x2 + x4 ≥ 1 D) x2 + x4 ≤ 1

32) An integer programming model which involves selection of investment is classified as what type of problem?

A) clustering problem B) transportation problem C) integer problem D) capital budgeting problem

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33) In a transportation model, when constructing the constraints, the points of demand are classified as __________.

A) transportation B) destinations C) origin D) goal

34) The general process for modeling an integer programming optimization problem is similar to that of linear programming, including the four essential components: an objective function, decision variables, constraints, and parameters. ⊚ ⊚

true false

35) Using linear programming on an integer programming problem and then rounding the optimal fractions to the nearest integer results in an optimal solution. ⊚ ⊚

true false

36) Linear and integer programming techniques can be used to solve many real-world problems. ⊚ true ⊚ false

37)

Capital budgeting is common linear programming application. ⊚ ⊚

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38) Integer Programming, like Linear Programming, requires the analyst to round to the nearest integer for optimization. ⊚ true ⊚ false

39) Theoretically, Shad can use linear or integer programming techniques to minimize the transportation costs related to shipping goods from his company’s warehouse to various retailers across the nation if the demand and supply both have integer values. ⊚ ⊚

40)

true false

An assignment problem can be formulated as a maximization or minimization problem. ⊚ ⊚

true false

41) Renita runs a local food bank and needs to schedule staff for the month ahead, but her integer programming model will result in one and only one optimal solution. ⊚ ⊚

true false

42) A facility location problem usually seeks to minimize the number of facilities and therefore the total cost, while satisfying the needs of all constituents. ⊚ true ⊚ false

43) Unlike a linear programming model, where the optimal solution is found at the corner or bound-ary of the feasibility region, the optimal solution to a nonlinear programming problem can be found any-where, making a nonlinear programming model easier to solve.

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⊚ ⊚

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true false

18

Answer Key Test name: ch 18 1) B 2) A 3) B 4) D 5) A 6) D 7) C 8) A 9) B 10) C 11) A 12) D 13) C 14) D 15) C 16) B 17) D 18) B 19) B 20) A 21) A 22) D 23) B 24) A 25) A 26) C Version 1

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27) C 28) C 29) C 30) A 31) A 32) D 33) B 34) TRUE 35) FALSE 36) TRUE 37) FALSE 38) FALSE 39) TRUE 40) TRUE 41) FALSE 42) TRUE 43) FALSE

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