Test Bank for Data Science For All, 1st edition by Brennan Davis Hunter Glanz

Data Science for All, 1e (Glanz/Davis) Chapter 1 What is Data Science?

1.1

Introduction to Data Science

1

Define terms relating to data science.

1) Which of the following best describes the interdisciplinary nature of data science?

A) Data science combines statistics, computer science, and domain expertise to extract knowledge from data.

B) Data science is solely focused on statistical analysis.

C) Data science is primarily about data visualization.

D) Data science only involves machine learning.

Answer: A 2) What are the three iterative components of the data science life cycle?

A) Data preparation, analysis, and storytelling B) Data collection, visualization, and deletion C) Data extraction, transformation, and archiving D) Data acquisition, modeling, and disposal Answer: A 2

Identify the scope of data science including its interdisciplinary nature or life cycle.

1) Which term is defined as the process of collecting, cleaning, transforming, integrating, and managing data for effective analysis and storytelling?

A) Data preparation B) Data visualization C) Data modeling D) Data archiving Answer: A 2) In the context of data science, what does "data storytelling" refer to?

A) Communicating data insights through summaries, visualizations, and narratives.

B) Collecting and cleaning raw data.

C) Analyzing data to find patterns.

D) Storing data in a structured format.

Answer: A

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3

Answer questions relating to the interdisciplinary nature or life cycle of data science.

1) Why is data science considered interdisciplinary?

A) It combines statistics, computer science, and domain expertise.

B) It focuses solely on statistical analysis.

C) It is limited to computer science applications.

D) It only involves data visualization.

Answer: A 2) What process involves cleaning, transforming, and integrating data to make them useful for analysis and storytelling?

A) Data wrangling B) Data visualization C) Data modeling D) Data archiving Answer: A 1.2

Data in Tables

1

Answer questions about the relationship of observations and variables.

1) What is a Boolean variable type in data tables?

A) A variable that contains only two possible values, TRUE or FALSE B) A variable that takes on only numerical values C) A variable that can take on any text D) A variable that describes a category Answer: A 2) What is the relationship between an observational unit and a variable?

A) An observational unit is an entity about which data are recorded, and a variable is a recorded characteristic of that entity.

B) A variable is an entity about which data are recorded, and an observational unit is a recorded characteristic of that entity.

C) An observational unit is a subset of variables in a dataset.

D) A variable is always a numerical value associated with an observational unit.

Answer: A 2

Answer questions about tidying messy data.

1) Considering the numbers 0, -2, 3.5, and 10, which one could be converted to a Boolean value in a data analysis context?

A) 0 B) -2 C) 3.5 D) 10 Answer: A 2 Copyright © 2026 Pearson Education, Inc.

2) Which of the following scenarios best illustrates tidy data?

A) A table where each column is a variable, each row is an observation, and each cell contains a single value B) A table where each column is an observation, each row is a variable, and each cell contains multiple values C) A table where each cell contains a summary of multiple observations D) A table where each row is a dataset, and each column is a variable Answer: A 3

Identify different variables value and data types.

1) Looking at the numbers -3, 1.4, 1.7, and 1.9, which is most likely to be stored as an integer in a data structure?

A) -3 B) 1.4 C) 1.7 D) 1.9 Answer: A 2) Consider the values -2, 1, 3, and 4.0. Which of these could be stored as an integer or converted to a Boolean variable?

A) 1 B) -2 C) 3 D) 4.0 Answer: A 4

Describe metadata and its purpose.

1) Which of the following is NOT a characteristic that would be included in metadata?

A) Description of the analysis process B) Variable names C) Data set authorship D) The coding scheme used for variables Answer: A 2) Often, data are accompanied by a description of the data itself. Because it is data about data, what is this data description called?

A) metadata B) keydata C) index data D) exclusive data Answer: A

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1.3

Data Preparation

1

Define or describe the data wrangling process.

1) Which is not a part of the data preparation process?

A) Data storytelling B) Data collection C) Data wrangling D) Data management Answer: A 2) Which of the following activities is NOT part of the data wrangling process?

A) Developing machine learning models B) Cleaning incomplete or inconsistent data C) Transforming data into a structured format D) Integrating multiple data sources Answer: A 2

Identify and use the steps for data wrangling.

1) Which of the following is an example of raw data?

A) Data in the form originally collected B) A processed spreadsheet ready for analysis C) Data that has been cleaned and structured D) A visual representation of analyzed data Answer: A 2) What step in the data wrangling process involves merging multiple data sources?

A) Integrating data B) Cleaning data C) Transforming data D) Visualizing data Answer: A 1.4

Data Analysis and Storytelling

1

Describe or define the different types of data analysis.

1) Good data communication is necessary for what purpose?

A) Making information accessible to a broader audience B) The transformation of raw data C) The integration of different data sets D) Collecting high-quality data Answer: A

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2) Which of the following is NOT a category of data analysis?

A) Restrictive B) Descriptive C) Diagnostic D) Prescriptive Answer: A 2

Describe or define the different types of data storytelling.

1) Data storytelling is essential for what purpose?

A) Communicating complex data findings in an understandable way B) Cleaning and preparing raw data C) Storing and managing large data sets D) Collecting new data from various sources Answer: A 2) What does visualization refer to, in the context of data science?

A) The presentation of data in a graphical format B) The prediction of future data trends C) The descriptive analysis of data D) The storytelling aspect of data analysis Answer: A 1.5

Data Science in Society and Industry

1

Answer questions relating to data science in society and industry.

1) How does data science contribute to the healthcare industry?

A) By enabling personalized medicine through the analysis of patient data B) By automating all medical procedures C) By eliminating the need for healthcare professionals D) By making all healthcare data public Answer: A 2) In what way has data science impacted the retail industry?

A) By optimizing inventory management through predictive analytics B) By completely eliminating physical stores C) By reducing the need for customer service representatives D) By making all transactions paper-based Answer: A

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2

Answer questions about the importance of data science.

1) The text states that data science values include: A) Curiosity, creativity, strategy, and insight B) Mathematics, programming, and data storage C) Speed, efficiency, and automation D) Confidentiality, security, and privacy Answer: A 2) How does data science contribute to innovation in technology?

A) By enabling the development of new products and services through data-driven insights B) By eliminating the need for research and development C) By making all technology self-sustaining D) By focusing only on hardware improvements Answer: A

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Data Science for All, 1e (Glanz/Davis) Chapter 2 Data Wrangling: Preprocessing 2.1

What is Data Wrangling?

1

Answer conceptual questions involving data wrangling.

1) What is not an example of data wrangling?

A) Collecting the data B) Cleaning the data C) Transforming the data D) Integrating the data Answer: A 2) What is not a key component of data wrangling?

A) Visualizing the data B) Cleaning the data C) Transforming the data D) Integrating the data Answer: A 2.2

Cleaning Missing Data

1

Answer questions involving cleaning missing data.

1) Which of the following is NOT a common issue that may need to be addressed when cleaning data?

A) Bad axis labels on visualizations B) Implausible values C) Missing data D) Duplicate records Answer: A 2) In data cleaning, what might be done with text values in a quantitative variable like age?

A) Convert to numeric equivalent B) Delete them entirely C) Set to the variable mean D) Leave as is Answer: A

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2

Remove observations with missing values.

1) What is one advantage of removing observations with missing values?

A) It simplifies the dataset.

B) It retains all possible data.

C) It avoids the need for imputation.

D) It ensures no data is lost.

Answer: A 2) Which of the following is a potential risk of removing observations with missing values?

A) It may lead to a loss of valuable information.

B) It ensures a more accurate dataset.

C) It simplifies data visualization.

D) It eliminates all data inconsistencies.

Answer: A 3

Impute missing values from internal data.

1) Why is sorting the columns of a dataset an early step in data cleaning?

A) To observe the range of values for each variable B) To put the columns in alphabetical order C) To identify columns with many missing values D) To move columns with errors to the end Answer: A 2) What is a downside of imputing missing values?

A) It makes assumptions and modifies raw values.

B) It ensures data accuracy.

C) It simplifies data analysis.

D) It increases dataset size.

Answer: A 4

Identify missing data.

1) Which of the following is an initial step in the process of identifying missing data?

A) Sorting the dataset B) Visualizing the data C) Normalizing the data D) Transforming the data Answer: A 2) What tool can be used to identify missing data in a dataset?

A) Spreadsheet software like Excel B) Visualization software like Tableau C) Database management system like SQL D) Programming language like Python Answer: A 2 Copyright © 2026 Pearson Education, Inc.

2.3

Cleaning Anomalous Values

1

Answer questions involving cleaning anomalous values.

1) What might be done with a quantitative variable that has some values far outside the normal range?

A) Check against raw data source B) Set to the maximum value C) Set to a Boolean value D) Delete them without further thought Answer: A 2) Which of the following methods can be used to address extreme data values?

A) Removing the extreme values B) Transforming the data C) Replacing the extreme values with more plausible values D) All of the above Answer: D 2

Identify extreme data values

1) What defines an extreme value in a dataset?

A) A value significantly different from the majority of values B) A value that is incorrect C) A value that is duplicated D) A value that is missing Answer: A 2) Which of the following is a possible consequence of not addressing extreme values in a dataset?

A) Biased statistical results B) More accurate data analysis C) No impact on data analysis D) Improved data quality Answer: A 3

Clean incorrect formats.

1) Which of the following is a common issue caused by incorrect formats in a dataset?

A) Inaccurate analysis results B) Improved data quality C) Enhanced data visualization D) Increased data size Answer: A

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2) What is a potential consequence of not cleaning incorrect formats in a dataset?

A) Biased results and misinterpretation B) Improved data accuracy C) Enhanced data consistency D) Increased dataset size Answer: A 2.4

Transforming Quantitative Variables

1

Answer conceptual questions involving transforming quantitative variables.

1) Why might a data scientist choose to transform a quantitative variable?

A) To make the data easier to visualize and analyze B) To remove anomalies from the dataset C) To integrate multiple datasets D) To store data securely Answer: A 2) What transformation technique involves creating new variables from an existing date variable?

A) Extracting month and year from a full date B) Sorting dates in chronological order C) Removing invalid date entries D) Combining date data from multiple sources Answer: A 2

Transform a quantitative variable using scaling.

1) Which mathematical operation is commonly used in scaling a quantitative variable?

A) Division B) Subtraction C) Addition D) Exponentiation Answer: A 2) Which of the following scenarios would most likely require scaling of a quantitative variable?

A) When the variable has a wide range of values B) When the variable has no missing values C) When the variable is categorical D) When the dataset is small Answer: A

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3

Use two or more quantitative variables to compute a new quantitative variable.

1) What is a common use of combining quantitative variables in data analysis?

A) Calculating per capita values B) Converting numerical data to text C) Removing duplicate records D) Replacing missing values Answer: A 2) Create a new variable, BMI in the final column, using the formula, BMI=(Weight(lb.)/[Height(in.)]^2)*703.

Student A B C D E

Weight (lb.)

149 138 104 105 150

Height (in.)

62 63 60 61 67

BMI

What is the mean BMI for the dataset to one decimal place?

A) 19.2 B) 19.0 C) 3.8 D) 22.8 Answer: A 4

Transform a quantitative variable using categorization.

1) What is the purpose of creating a categorical variable from a quantitative variable?

A) To simplify data for specific analyses B) To increase data complexity C) To remove duplicate records D) To store data in a database Answer: A 2) What method would you use to create a categorical variable from a continuous variable like income?

A) Setting income ranges (e.g., low, medium, high) B) Calculating the total income C) Sorting income data in descending order D) Removing outliers from the income data Answer: A

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2.5

Transforming Categorical Variables

1

Answer questions involving transforming categorical variables.

1) Which is most likely to be an example of a Boolean variable?

A) Pass_or_fail B) Academic_major C) Graduation_year D) Date_of_birth Answer: A 2) What is a common method for transforming categorical variables?

A) Encoding categories as numerical values B) Sorting categories alphabetically C) Removing invalid categories D) Combining categories from multiple datasets Answer: A 2

Split a categorical variable into multiple variables.

1) What is the purpose of splitting a categorical variable into multiple variables?

A) To capture more detailed information B) To reduce the dataset size C) To eliminate missing values D) To correct data entry errors Answer: A 2) Why might a data scientist split a categorical variable into multiple variables?

A) To improve the granularity of the data B) To reduce the number of variables C) To eliminate extreme values D) To correct data entry errors Answer: A 3

Transform a variable using encoding.

1) What is the primary purpose of encoding a categorical variable?

A) To convert it into a numerical format suitable for analysis B) To remove duplicate records C) To replace missing values with the mean D) To sort the dataset Answer: A

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2) Why might a data scientist choose to use encoding for a categorical variable?

A) To prepare the data for machine learning algorithms B) To reduce the number of variables C) To eliminate extreme values D) To correct data entry errors Answer: A 4

Transform a variable using Boolean encoding.

1) What is the purpose of Boolean encoding a categorical variable?

A) To convert it into binary format for analysis B) To remove duplicate records C) To replace missing values with the mean D) To sort the dataset Answer: A 2) What is a potential drawback of Boolean encoding?

A) It can increase the number of variables B) It can introduce new missing values C) It can reduce the dataset size D) It can eliminate outliers Answer: A 2.6

Reshaping a Data Set

1

Answer questions involving reshaping a dataset.

1) Which of the following best describes a "wide format" dataset?

A) A dataset with fewer rows but more columns B) A dataset with more rows but fewer columns C) A dataset with an equal number of rows and columns D) A dataset with only one column Answer: A 2) What is the primary benefit of reshaping a dataset from long format to wide format?

A) It can make the data easier to read and interpret.

B) It eliminates extreme values.

C) It reduces the dataset size.

D) It corrects data entry errors.

Answer: A

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2.7

Combining Data Sets

1

Answer questions involving combining datasets.

1) Which of the following describes a situation where combining datasets is beneficial?

A) When analyzing data from multiple sources to draw comprehensive insights B) When data is already clean and ready for analysis C) When data visualization is the only goal D) When data storage is the primary concern Answer: A 2) Which of the following is a common use case for combining datasets?

A) Enhancing data analysis by integrating multiple sources B) Removing duplicate records C) Replacing missing values with the mean D) Sorting the dataset Answer: A 2

Augment datasets.

1) What does augmenting datasets involve?

A) Combining datasets horizontally by adding new variables from one dataset to another B) Combining datasets vertically by stacking observations C) Removing duplicate records from a dataset D) Sorting data in ascending order Answer: A 2) Why might a data scientist choose to augment datasets?

A) To enrich the dataset with additional relevant information B) To remove anomalies from the dataset C) To visualize data trends D) To store data securely Answer: A 3

Stack datasets.

1) What does stacking datasets involve?

A) Combining datasets vertically by adding new observations from one dataset to another B) Combining datasets horizontally by adding new variables C) Removing duplicate records from a dataset D) Sorting data in descending order Answer: A

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2) Dataset A with 25 rows and 10 columns is stacked with Dataset B which has 35 rows and 10 columns. How many rows and columns will the combined dataset have?

A) 60 rows and 10 columns B) 25 rows and 20 columns C) 35 rows and 20 columns D) 60 rows and 20 columns Answer: A 4

Merge datasets.

1) A university has two datasets: Student info: Student ID 1001 1002 1003

Name Alex Jamie Jordan

Student grades: Student ID Course 1001 Math 101 1001 English 101 1002 CS 300 1003 Physics 209

Major Year Business Junior Computer Science Senior Engineering Sophomore Grade B+ A AC+

If the university merges these datasets using the Student ID field, how many unique Student ID values would exist in the merged dataset?

A) 3 B) 4 C) 7 D) 5 Answer: A

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2) A university has two datasets: Student info: Student ID 1001 1002 1003

Name Alex Jamie Jordan

Student grades: Student ID Course 1001 Math 101 1001 English 101 1002 CS 300 1003 Physics 209

Major Year Business Junior Computer Science Senior Engineering Sophomore Grade B+ A AC+

How many rows would the merged dataset contain?

A) 4 B) 3 C) 7 D) 2 Answer: A

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Data Science for All, 1e (Glanz/Davis) Chapter 3 Making Sense of Data through Visualization 3.1

The Grammar of Graphics

1 Define terms and describe characteristics related to grammar of graphics and data visualization.

1) Which of the following is an essential component of most data visualizations according to the grammar of graphics?

A) A source dataset B) Size of effects C) Interactive elements D) A legend Answer: A 2) What element is NOT typically part of the grammar of graphics framework?

A) Animation B) Color C) Geometry D) Axes Answer: A 3.2

Visualizations with One Quantitative Variable

1 Describe, compare, and interpret characteristics of visualizations with one quantitative variable.

1) Which of these is a correct interpretation of a box plot?

A) The dots outside the whiskers represent potential outliers.

B) The ends of the whiskers represent the mean and mode.

C) The top and bottom of the box represent the range of the data.

D) The dots outside the whiskers indicate the median and quartiles.

Answer: A 2) A ________ displays one quantitative variable (like a histogram) with a smooth continuous line to show the distribution.

A) density plot B) violin plot C) box plot D) scatterplot Answer: A

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2

Define statistics and identify visual techniques.

1) Which of the following is true regarding a right-skewed histogram?

A) It has a long tail on the right side of the peak.

B) The mean is less than the median.

C) It suggests most data points are clustered at lower values.

D) It indicates that the data points are spread evenly.

Answer: A 2) The nth ________ measures the data point that is larger than n% of all the data points.

A) percentile B) outlier C) treemap D) box plot Answer: A

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3

Solve applications involving visualizations with one quantitative variable.

1) How many bins have a frequency greater than 15 in Figure 3.6.1's histogram of random data?

Figure 3.6.1.

A) 3 B) 5 C) 2 D) 7 Answer: A

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2) What is the min value of Group 3 in Figure 3.6.2?

Figure 3.6.2.

A) 6 B) 4 C) 8 D) 5 Answer: A 3.3

Visualizations with One Categorical Variable

1

Identify chart types having given characteristics.

1) Which of the following is an inappropriate use of a bar chart?

A) To depict the average speed of a car over time B) To compare the number of students in different classes C) To show the amount of rainfall in different months D) To illustrate sales figures for various products Answer: A 2) What is a word cloud?

A) A visual depiction of text data, where the size of each word indicates its frequency or importance B) A mapping of words onto a bubble chart C) A graphical representation of word frequency D) A plot that uses the x and y axes to represent word count Answer: A 4 Copyright © 2026 Pearson Education, Inc.

2

Describe characteristics of visualizations with one categorical variable.

1) What is a primary characteristic of visualizations with one categorical variable?

A) They display the frequency of each category.

B) They require a secondary variable for context.

C) They use continuous data.

D) They show relationships between two variables.

Answer: A 2) Which of the following is NOT a characteristic of a bar chart?

A) Used for continuous data B) Shows frequency distribution C) Consists of rectangular bars D) Can be oriented vertically or horizontally Answer: A 3

Solve applications involving visualizations with one categorical variable.

The bar graph shows the expenditures of one city government in a recent year.

1) Name the agency whose spending for the year was between $4 million and $5 million and estimate its spending.

A) Fire; $4.3 million B) Fire; $5.4 million C) Health; $4.3 million D) Health; $5.4 million Answer: A

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The bar graph shows the number of tickets sold each week by the garden club for their annual flower show.

2) How many tickets were sold during week 3?

A) 30 tickets B) 59 tickets C) 40 tickets D) 19 tickets Answer: A 4

Interpret visualizations with one categorical variable.

1) How can you interpret the data from a bar chart showing the favorite ice cream flavors of students?

A) Identify the flavor with the longest or tallest bar as the most popular.

B) Identify the flavor with the shortest bar as the most popular.

C) Identify the flavor with the longest or tallest bar as the least popular.

D) Identify the flavor with the shortest bar as the least popular.

Answer: A 2) When interpreting a bar chart showing survey results, what should you look for to identify the most common response?

A) The longest or tallest bar B) The shortest bar C) Always the first bar D) Always the last bar Answer: A

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3.4

Visualizations with Two Variables

1

Describe characteristics of visualizations with two variables.

1) Which aspect is not typically represented in a scatterplot?

A) Hierarchical structure of data categories B) Relationship direction between two variables C) Relationship strength between two variables D) Relationship shape between two variables Answer: A 2) What visualization is best suited for showing the relationship between two quantitative variables?

A) Scatterplot B) Bar Chart C) Line Chart D) Pie Chart Answer: A 2

Identify visualizations with two variables given a graph.

1) A ________ chart displays data as a series of connected points that show how a value changes over time.

A) line B) prediction C) dashboard D) bubble Answer: A 2) What visualization is depicted by a graph that shows data points as bubbles, with the size of each bubble representing a third variable?

A) Bubble Chart B) Scatterplot C) Line Chart D) Bar Chart Answer: A 3

Solve applications involving visualizations with two variables.

1) How would you visualize the relationship between students' study hours and their test scores?

A) Scatterplot B) Bar Chart C) Line Chart D) Pie Chart Answer: A

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2) What type of chart would you use to compare the sales performance of different products over several months?

A) Line Chart B) Scatterplot C) Bar Chart D) Pie Chart Answer: A 3.5

Visualizations with Three or More Variables

1

Identify visualizations with three or more variables having given characteristics.

1) Which of the following visualizations is best suited for representing the relationship between three quantitative variables?

A) Bubble chart B) Scatterplot C) Box plot D) Line chart Answer: A 2) What visualization technique is most effective for showing the relationship between three quantitative variables and one categorical variable?

A) Bubble chart B) Scatterplot C) Box plot D) Violin plot Answer: A 2

Describe and compare characteristics of visualizations with three or more variables.

1) In which case would you most likely not use a treemap?

A) To illustrate the trend of a variable over time B) To display nested levels of data C) To show part-to-whole relationships D) To compare different categories based on size Answer: A 2) Which of the following is a disadvantage of using a bubble chart?

A) Can be difficult to interpret when bubbles overlap B) Does not show relationships between variables C) Not suitable for categorical data D) Limited to two variables Answer: A

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3

Solve applications involving visualizations with three or more variables.

1) Consider the Titanic visualization design illustrating the relation of age, fare, and passenger class to survival. How many 3rd class passengers survived who were 20 to 30 years old, paying a fare of $200 - $300?

Figure 3.83. A bubble chart.

A) 4 B) 3 C) 6 D) 7 Answer: A 2) How can you use a bubble chart to interpret the relationship between three variables?

A) Analyze the position and size of the bubbles.

B) Count the number of bubbles.

C) Identify the color of each bubble.

D) Identify the proportion of each category within a whole.

Answer: A

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3.6

The Dangers of Visual Misrepresentations

1

Describe characteristics of visual misrepresentations.

1) A ________ occurs when data and visuals are altered in some way to communicate a message that is misleading or inaccurate.

A) visual misrepresentation B) dashboard C) prediction D) bubble plot Answer: A 2) A form of visual misrepresentation is ________ axes, where the scale of the axes are manipulated to exaggerate small differences.

A) truncated B) graphic deception C) rounded D) infographic Answer: A 2

Solve applications involving visual misrepresentations.

1) What is a recommended action to mitigate the risk of visual misrepresentation?

A) Using appropriate axis scales B) Using truncated axes C) Cherry-picking data points D) Using misleading labels Answer: A 2) What is a recommended practice to ensure ethical data visualization?

A) Using appropriate scales and labels B) Using truncated axes C) Cherry-picking data points D) Using misleading colors Answer: A 3.7

Data Visualization Guidelines

1

Explain concepts involving data visualization guidelines.

1) What is the primary goal of data visualization guidelines?

A) To enhance the clarity and effectiveness of data communication B) To increase the complexity of data representation C) To obscure the underlying data D) To make the data visualization inaccessible Answer: A 10 Copyright © 2026 Pearson Education, Inc.

2) What is a key benefit of using interactive elements in data visualizations?

A) It engages the audience and allows for exploration.

B) It simplifies the data.

C) It removes the need for legends.

D) It ensures data accuracy.

Answer: A 2

Identify characteristics of effective data visualizations.

1) ________ is the reporting of information to society using large datasets.

A) Data journalism B) Hierarchical C) Data storytelling D) Natural language processing Answer: A 2) Which of the following is a characteristic of effective data visualizations?

A) They are accessible to all users.

B) They are overly complex.

C) They obscure the data.

D) They use misleading colors.

Answer: A

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Data Science for All, 1e (Glanz/Davis) Chapter 4 Exploratory Data Analysis 4.1

Central Tendency

1

Answer conceptual and vocabulary questions involving measures of central tendency.

1) Which of the following statistics is used to find the center or typical value in a dataset?

A) Mean B) Variance C) Range D) Standard deviation Answer: A 2) What does the mode refer to?

A) The value that occurs most frequently in the dataset B) The middle value after sorting the data C) The value that divides the dataset into two halves D) The average of all the values Answer: A 2

Solve applications involving measures of central tendency.

1) The following are the ages of participants in a study: {22, 24, 34, 31, 27, 29, 27, 29, 38, 25, 25, 31, 26, 38, 38, 35, 25, 26, 45, 50}. What is the 90% trimmed average number of the ages in the study?

A) 30.61 B) 29 C) 27.55 D) 25 Answer: A 2) A real estate site assesses the value for ten homes within a city (in thousands of dollars) {515, 900, 675, 560, 435, 725, 485, 675, 780, 660}. What is the median value of the ten homes?

A) $322,778 B) $675,000 C) $290,500 D) $580,00 Answer: A

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3

Determine the appropriate measure of central tendency given a scenario.

1) What is the best measure of central tendency for categorical data?

A) Mode B) Mean C) Median D) 90% Trimmed Mean Answer: A 2) In a highly skewed distribution of household incomes, what measure of central tendency should be used to represent the typical income?

A) Median B) Mean C) Mode D) 90% Trimmed Mean Answer: A 4.2

Variability

1

Answer conceptual and vocabulary questions involving measures of variability.

1) The variance gives us: A) The average squared distance from the mean B) The distance between the first and third quartiles C) The most frequent value in the dataset D) The distance between the mean and the mode Answer: A 2) At a climate change workshop, environmental scientists highlighted the importance of the ________ to understand data variability.

A) standard deviation B) mode C) trimmed mean D) median Answer: A 2

Solve applications involving measures of variability.

1) During a seminar on urban planning, city planners discussed ________ as the typical deviation of building heights from the average building height in the city's central district.

A) standard deviation B) median C) range D) mean Answer: A

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2) Given the data set: 5, 8, 12, 14, 20, calculate the variance.

A) 34.5 B) 32.5 C) 30.5 D) 28.5 Answer: A 4.3

Shape

1

Answer conceptual and vocabulary questions involving shape.

1) How is a bimodal distribution identified?

A) Having two peaks B) Being perfectly symmetrical C) Having a single, central peak D) Having a long tail on one side Answer: A 2) Jake, a music lover, presented a ________ to show the distribution of song lengths on his favorite artist's new album.

A) histogram B) stacked column chart C) scatterplot D) line graph Answer: A 2

Solve applications involving the shape of a quantitative variable.

1) How would you describe the shape of a histogram that shows the distribution of car prices, where most cars are priced low but a few are priced very high?

A) Right-skewed B) Symmetric C) Left-skewed D) Bimodal Answer: A 2) A data set of exam scores has a mean of 75, a median of 80, and a mode of 85. What is the likely shape of the distribution?

A) Left-skewed B) Symmetric C) Right-skewed D) Bimodal Answer: A

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4.4

Resistant Central Tendency and Variability

1 Answer conceptual and vocabulary questions involving resistant central tendency and variability.

1) Which of the following statistics is considered non-resistant?

A) Range B) Median C) Mode D) All of the above Answer: A 2) Sophia, a literature major, used ________ analytics to understand the main themes in classic novels.

A) descriptive B) inferential C) quantitative D) prescriptive Answer: A 2

Calculate new values of the statistics given an outlier added to a data set.

1) A data set has a mean of 50 and a standard deviation of 5. If an outlier with a value of 100 is added, what will happen to the mean and standard deviation?

A) Both will increase.

B) Both will decrease.

C) The mean will increase, but the standard deviation will decrease.

D) The mean will decrease, but the standard deviation will increase.

Answer: A 2) A data set has a median of 60. If an outlier with a value of 10 is added, what will happen to the median?

A) It will remain approximately the same.

B) It will significantly decrease.

C) It will significantly increase.

D) It will become equal to the outlier.

Answer: A

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4.5

Data Associations

1

Answer conceptual and vocabulary questions involving data associations.

1) During a healthcare seminar, Dr. Patel highlighted the importance of ________ to understand the association between blood sugar and BMI A) scatterplots B) bar charts C) box plots D) pie charts Answer: A 2) In her biology research, Dr. Martinez used ________ statistics to describe her data and variables with basic numbers.

A) summary B) qualitative C) exploratory D) quantitative Answer: A 2

Determine the appropriate visualization method or type of table given a scenario.

1) In a sports analytics lecture, the coach used a ________ graph to showcase the relationship between player height and average field throw percentage.

A) Quantitative-by-Quantitative B) Quantitative-by-Categorical C) Categorical-by-Categorical D) bar Answer: A 2) During a nutrition seminar, researchers displayed a ________ chart to depict the link between food group and average calorie content.

A) Quantitative-by-Categorical B) Quantitative-by-Quantitative C) Categorical-by-Categorical D) Percentile Answer: A 3

Solve applications involving data associations.

1) Which of the following scenarios best illustrates a quantitative-by-categorical relationship?

A) Comparing average incomes across different education levels B) Analyzing the correlation between height and weight C) Examining the frequency of different car brands in a survey D) Assessing the distribution of test scores in a class Answer: A 5 Copyright © 2026 Pearson Education, Inc.

2) How would you investigate the relationship between car prices and car types (e.g., SUV, Sedan, Truck)?

A) Use side-by-side box plots to compare prices across car types.

B) Create a scatterplot of car prices and car types.

C) Calculate the correlation coefficient between car prices and car types.

D) Create a histogram of car prices for each car type.

Answer: A 4.6

Identifying Outliers

1

Determine the appropriate visualization of data given a scenario.

1) In the economics department, Professor Yang emphasized the importance of understanding the ________ as a summary statistic for the variability in the data.

A) interquartile range B) mean C) median D) frequency Answer: A 2) In his biology research, Dr. Lee should use a ________ to visually identify potential outliers in a single variable representing plant growth A) box plot B) bar chart C) line graph D) scatterplot Answer: A 2

Answer conceptual and vocabulary questions involving outliers.

1) Which method uses the quartiles and interquartile range (IQR) to identify outliers?

A) The Quartile Method B) The Median Method C) The Mean Method D) The Mode Method Answer: A 2) When analyzing the distribution of a quantitative variable, what visualization would best highlight the presence of outliers?

A) Box plot B) Line graph C) Stacked bar chart D) Pie chart Answer: A

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3

Solve applications involving identifying outliers.

1) Given a data set with the following values: {10, 12, 14, 15, 16, 18, 100}, identify the outlier using the Quartile Method.

A) 100 B) 10 C) 15 D) 18 Answer: A 2) Which of the following values is a mild outlier in the data set {20, 21, 22, 23, 24, 25, 40} using the Quartile Method?

A) 40 B) 20 C) 25 D) 22 Answer: A

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Data Science for All, 1e (Glanz/Davis) Chapter 5 Data Management 5.1

Asking Questions of Data

1

Explain critical concepts involved in data management.

1) What could be the outcome of a table without a unique key for each observation?

A) Possibility of the same student listed multiple times B) More efficient data queries C) Faster data entry D) Lower storage requirements Answer: A 2) What is the role of a foreign key in data management?

A) To reference the primary key in another table, forming a relationship between tables B) To uniquely identify observations within a table C) To store large datasets D) To encrypt data Answer: A 5.2

Selecting Variables

1

Describe the purpose of selecting variables.

1) Why might one access distinct values of a variable in a table?

A) To know the unique entries within a variable B) To print all observations C) To duplicate the table D) To calculate the mean of the variable across similar values Answer: A 2) What tool is commonly used to select variables in a dataset?

A) SQL B) SELECT C) DELETE D) UPDATE Answer: A

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2

Identify values of variables using queries.

1) Which SQL query would you use to identify the values of the "Country" variable in a "Customers" table?

A) SELECT Country FROM Customers; B) SELECT DISTINCT Country FROM Customers; C) SELECT * FROM Customers; D) SELECT Country, City FROM Customers; Answer: A 2) What SQL query would you use to retrieve the "OrderDate" values from an "Orders" table?

A) SELECT OrderDate FROM Orders; B) SELECT DISTINCT OrderDate FROM Orders; C) SELECT * FROM Orders; D) SELECT OrderDate, CustomerID FROM Orders; Answer: A 3

Identify distinct values of variables using queries.

1) Which command retrieves the distinct "ProductID" values from an "OrderDetails" table in SQL? A) SELECT DISTINCT ProductID FROM OrderDetails; B) SELECT ProductID FROM OrderDetails; C) SELECT * FROM OrderDetails; D) SELECT ProductID, OrderID FROM OrderDetails; Answer: A 2) What SQL query would you use to get the distinct values of the "EmployeeID" variable from an "Employees" table?

A) SELECT DISTINCT EmployeeID FROM Employees; B) SELECT EmployeeID FROM Employees; C) SELECT * FROM Employees; D) SELECT EmployeeID, LastName FROM Employees; Answer: A 5.3

Filtering and Ordering Observations

1

Explain critical concepts involved in filtering and ordering observations.

1) When ordering observations in a dataset by a variable in descending order, which function is typically used in R? A) arrange(desc()) B) filter() C) select() D) summarize() Answer: A 2 Copyright © 2026 Pearson Education, Inc.

2) What character(s) is/are used to filter a quantitative variable to be not equal to a specific value?

A) != B) ≠ C) >< D) < Answer: A 2

Filter observations by text variables.

1) How can you filter observations in Excel to show only those where a text variable equals "Mexico"?

A) Use the filter dropdown menu and select "Mexico".

B) Use the sort function to arrange by "Mexico".

C) Use the pivot table to summarize by "Mexico".

D) Use the VLOOKUP function to find "Mexico".

Answer: A 2) In Python, what method would you use to filter a DataFrame to show only rows where the "Country" column is "Canada"?

A) df[df["Country"] == "Canada"] B) df.sort_values("Country") C) df.groupby("Country") D) df["Country"].unique() Answer: A 3

Filter observations using a wildcard.

1) Which wildcard character is typically used to filter observations where a text variable starts with a specific letter in SQL? A) % B) _ C) * D) ?

Answer: A 2) In SQL, what operator would you use to filter observations where a text variable contains any characters after a specific letter?

A) % B) _ C) * D) ?

Answer: A

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4

Filter observations by quantitative variables.

1) How can you filter observations in Excel to show only those where a numerical variable is greater than 50?

A) Use the filter dropdown menu and select "Number Filters" -> "Greater Than".

B) Use the sort function to arrange by values greater than 50.

C) Use the pivot table to summarize by values greater than 50.

D) Use the VLOOKUP function to find values greater than 50.

Answer: A 2) In Python, what method would you use to filter a DataFrame to show only rows where the "Price" column is greater than 100?

A) df[df["Price"] > 100] B) df.sort_values("Price") C) df.groupby("Price") D) df["Price"].unique() Answer: A 5

Filter observations with missing values.

1) How would you filter observations with missing values in a specific column in R? A) filter(is.na(column_name)) B) select(column_name) C) arrange(column_name) D) summarize(column_name) Answer: A 2) In Excel, how can you filter observations with missing values in a specific column?

A) Use the filter dropdown menu and select "Blanks".

B) Use the sort function to arrange by missing values.

C) Use the pivot table to summarize by missing values.

D) Use the VLOOKUP function to find missing values.

Answer: A 6

Filter observations using multiply conditions simultaneously.

1) How would you filter observations in R where both "Price" is greater than 100 and "Quantity" is less than 50?

A) filter(Price > 100 & Quantity < 50) B) select(Price, Quantity) C) arrange(Price, Quantity) D) summarize(Price, Quantity) Answer: A

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2) What SQL clause would you use to filter observations where "Country" is "USA" and "Sales" are greater than 1000?

A) WHERE Country = "USA" AND Sales > 1000 B) ORDER BY Country, Sales C) GROUP BY Country, Sales D) HAVING Country = "USA" AND Sales > 1000 Answer: A 7

Order observations by a variable.

1) Which function would you use in Python to order observations in a DataFrame by a specific variable?

A) sort_values() B) filter() C) select() D) summarize() Answer: A 2) How would you order observations in R by a specific variable in descending order?

A) arrange(desc(variable_name)) B) filter(variable_name) C) select(variable_name) D) summarize(variable_name) Answer: A 5.4

Summarizing and Structuring Data

1

Explain critical concepts involved in summarizing and structuring data.

1) What is the primary purpose of summarizing data?

A) To provide a concise representation of the data B) To delete unnecessary data points C) To convert data into a different format D) To merge multiple datasets into one Answer: A 2) Which of the following is a common method for summarizing quantitative data?

A) Calculating the mean B) Counting the number of text entries C) Sorting the data alphabetically D) Filtering out missing values Answer: A

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2

Use summarizing queries to generate basic descriptive statistics.

1) Which function would you use to find the maximum value in a quantitative dataset?

A) MAX B) MIN C) AVG D) COUNT Answer: A 2) What is the result of using the COUNT function in a summarizing query?

A) The number of entries in a dataset B) The sum of all values in a dataset C) The average of all values in a dataset D) The highest value in a dataset Answer: A 3

Create new variables by combining existing variables.

1) Which of the following is an example of creating a new variable by combining existing ones?

A) Calculating total revenue by multiplying price and quantity B) Sorting data alphabetically C) Filtering observations based on a condition D) Counting the number of text entries Answer: A 2) Why might you create a new variable for price per order in a sales dataset?

A) To analyze the revenue generated per order B) To reduce the size of the dataset C) To change the format of the data D) To merge multiple datasets into one Answer: A 5.5

Merging Tables

1

Explain critical concepts involved in merging tables.

1) What is a primary key in the context of merging tables?

A) A unique identifier within a specific table B) A key that references the primary key in another table C) A visual representation of relationships between tables D) A combination of two or more tables into one Answer: A

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2) What is the purpose of a foreign key in merging tables?

A) To reference the primary key in another table B) To uniquely identify observations within a table C) To combine observations from two tables based on a related variable D) To represent the relationship between different sets of items Answer: A 2

Define types of joins and determine when each is appropriate.

1) Why would a data scientist choose a left join over an inner join?

A) To include all observations from the left table, regardless of a match B) To return the least amount of observations C) To include all observations from the left table without matching the right one D) To include all observations from the right table, regardless of a match Answer: A 2) What join type should be used to include only the records that have matching values in both tables?

A) Inner Join B) Left Join C) Right Join D) Full Join Answer: A 3

Identify values of variables by merging tables.

1) After merging the Products and Categories tables, what category name will "Chef Anton's Cajun Seasoning" belong to?

A) Condiments B) Beverages C) Confections D) Dairy Products Answer: A 2) What category name will "Gorgonzola Telino" belong to after merging the Products and Categories tables?

A) Dairy Products B) Beverages C) Condiments D) Confections Answer: A

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Data Science for All, 1e (Glanz/Davis) Chapter 6 Understanding Uncertainty, Probability, and Variability 6.1

Variability and Uncertainty

1

Answer vocabulary and conceptual questions involving variability and uncertainty.

1) Which of the following best defines a "random trial" in data science?

A) An observation leading to an uncertain outcome B) A trial with a predetermined outcome C) An observation with a constant value D) An experiment with repeatable results Answer: A 2) Which of the following measures is used to quantify variability in a dataset?

A) Standard deviation B) Mean C) Median D) Mode Answer: A 2

Create and interpret histograms to assess variability and predictability.

1) Consider a histogram showing the variability of exam scores for a class of students. If the histogram is right-skewed, what can you predict about the median score compared to the mean score?

A) The median will be less than the mean.

B) The median will be greater than the mean.

C) The median will be equal to the mean.

D) The median cannot be determined from the histogram.

Answer: A 2) When examining a histogram of car prices that is left-skewed, what can you predict about the cheaper cars?

A) There will be more cheap cars than expensive cars.

B) There will be fewer cheap cars than expensive cars.

C) There will be an equal number of cheap and expensive cars.

D) Car prices cannot be predicted from the histogram.

Answer: A

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3

Create and interpret scatterplots to assess variability and predictability.

1) Consider a scatterplot showing the relationship between hours studied and exam scores. If the scatterplot shows a positive correlation, what can you predict?

A) More hours studied generally leads to higher exam scores.

B) More hours studied generally leads to lower exam scores.

C) There is no relationship between hours studied and exam scores.

D) Exam scores cannot be predicted from the scatterplot.

Answer: A 2) A scatterplot of age and income shows that as age increases, income also increases but at a decreasing rate. What type of relationship does this indicate?

A) A positive but diminishing relationship B) A negative relationship C) A linear relationship D) No relationship Answer: A 6.2

Probability

1

Answer vocabulary and conceptual questions involving probability.

1) What term is used to refer to a specific collection of outcomes?

A) Event B) Sample space C) Complement D) Instance Answer: A 2) Which of the following is a rule of probability?

A) The sum of all probabilities in a sample space must be 1.

B) The sum of all probabilities in a sample space must be 0.

C) Probabilities can be negative.

D) Probabilities can be greater than 1.

Answer: A 2

Determine outcomes and sample spaces for events.

1) How many outcomes involve rolling at least one 6 with two six-sided dice?

A) 11 B) 6 C) 12 D) 15 Answer: A

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2) What is the sample space for flipping a coin twice?

A) {HH, HT, TH, TT} B) {H, T} C) {HH, HT, TT} D) {H, T, HH, TT} Answer: A 3

Determine whether a probability is valid.

1) Which of the following is a valid probability?

A) 1 B) 0 C) 0.5 D) 2 Answer: A 2) What is the probability of an impossible event?

A) 0 B) 1 C) 0.5 D) 2 Answer: A 6.3

Sampling Methods

1

Answer vocabulary and conceptual questions involving sampling methods.

1) Which sampling method involves dividing the population into subgroups and taking random samples from each subgroup?

A) Simple random sampling B) Convenience sampling C) Stratified random sampling D) Purposive sampling Answer: A 2) What is a convenience sample?

A) A sample in which each member of the population has an equal chance of being selected B) A sample in which the population is divided into subgroups and random samples are taken from each subgroup C) A sample in which members are selected based on specific characteristics D) A sample in which members are selected based on ease of access and proximity Answer: A

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2

Classify the method of sampling given a scenario.

1) A company wants to survey employees from different departments to ensure each department is proportionally represented. Which sampling method should they use?

A) Simple random sampling B) Convenience sampling C) Stratified random sampling D) Purposive sampling Answer: A 2) A researcher wants to compare average test scores across different schools. What sampling method should they use to ensure each school is represented?

A) Stratified random sampling B) Convenience sampling C) Simple random sampling D) Systematic sampling Answer: A 3 Compute and interpret statistics from samples obtained with different sampling methods.

1) In a systematic sample, if every 10th person is selected from a population of 1000, how many people will be in the sample?

A) 100 B) 50 C) 10 D) 200 Answer: A 2) How does increasing the sample size affect the reliability of the sample statistics?

A) It increases the reliability.

B) It decreases the reliability.

C) It has no effect on the reliability.

D) It makes the sample biased.

Answer: A 6.4

Simulation

1

Answer conceptual questions involving simulations.

1) Why is it important to use many repetitions in a simulation?

A) To ensure the simulation is quick B) To reduce the need for complex calculations C) To achieve more accurate estimates of probabilities D) To eliminate the possibility of errors Answer: A 4 Copyright © 2026 Pearson Education, Inc.

2) What is a key component necessary for conducting a successful simulation?

A) Ensuring all outcomes are equally likely B) Minimizing the number of repetitions C) Using real-world data exclusively D) Accurate description of the random trial and probabilities Answer: A 2

Interpret the results of a simulation.

1) How does increasing the number of repetitions in a simulation affect the results?

A) It makes the estimates more reliable.

B) It makes the simulation faster.

C) It reduces the need for accurate data.

D) It eliminates the impact of random errors.

Answer: A 2) What does it mean if a simulation result is highly unlikely under the assumed model?

A) The model may not accurately represent the real-world process.

B) The simulation needs more repetitions.

C) The result should be ignored.

D) The data used was incorrect.

Answer: A 6.5

Working with Probabilities and Common Fallacies

1

Define and identify properties of probabilistic fallacies.

1) Which fallacy involves assuming something is certain because it is likely to happen?

A) Appeal to probability B) Gambler's fallacy C) Base rate fallacy D) Confusion of the inverse Answer: A 2) What is confusion of the inverse?

A) The incorrect belief that the conditional probability of an event A given B is the same as the probability of B given A. B) The incorrect belief that past events affect the probability of future independent events.

C) The incorrect belief that rare events are more likely to occur than common events.

D) The incorrect belief that the probability of an event increases with the number of trials.

Answer: A

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2

Identify probabilistic fallacies occurring in given scenarios.

1) A person believes that because they haven't rolled a 6 in their last 10 dice rolls, they are more likely to roll a 6 on the next roll. What type of fallacy is this?

A) Gambler's fallacy B) Appeal to probability C) Base rate fallacy D) Confusion of the inverse Answer: A 2) A medical test has a 95% accuracy rate for detecting a disease. However, the disease is very rare. A person tests positive and assumes they definitely have the disease. What fallacy are they committing?

A) Base rate fallacy B) Gambler's fallacy C) Appeal to probability D) Confusion of the inverse Answer: A

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Data Science for All, 1e (Glanz/Davis) Chapter 7 Drawing Conclusions from Data 7.1

Introduction to Statistical Inference

1

Answer conceptual questions involving statistical inference.

1) Statistical inference allows us to form judgments about a: A) Population B) Sample C) data set D) Variable Answer: A 2) A question asked at the start of the scientific method pertains to: A) The population of interest B) A particular sample C) The bounds of a variable of interest D) The type of data to be collected Answer: A 7.2

Data Collection and Study Design

1

Determine the population of interest of a study.

Choose the term that describes the example within the following context.

1) An economist wants to investigate the wage gap between men and women in the United States. They look at the United States census data from 2020. However, the economist is aware that men tend to work in fields that pay more on average. All men and women in the United States are the ________ in this study.

A) population B) explanatory variable C) confounding variable D) sample Answer: A 2) An economist wants to investigate the wage gap between men and women in the United States. They look at the United States census data from 2020. However, the economist is aware that men tend to work in fields that pay more on average. All men and women who were included in U.S. census responses in 2020 are the ________ in this study.

A) sample B) population C) explanatory variable D) confounding variable Answer: A 1 Copyright © 2026 Pearson Education, Inc.

2

Answer questions involving data collection and study design.

1) Analyzing data can include A) Computing descriptive statistics B) Creating visualizations C) Doing statistical inference D) All of the above Answer: D 2) Which of the following is an advantage of using a randomized controlled trial?

A) It minimizes bias and confounding variables.

B) It requires fewer resources than observational studies.

C) It does not require informed consent.

D) It is always ethical to use.

Answer: A 3

Determine characteristics of a study.

1) Which of the following best describes a confounding variable?

A) A variable that influences both the dependent and independent variables B) A variable that is manipulated by the researcher C) A variable that is measured but not analyzed D) A variable that is held constant during the experiment Answer: A 2) In a study measuring the impact of diet on weight loss, what is the response variable?

A) Weight loss B) Diet C) Number of participants D) Duration of the study Answer: A 7.3

The Language of Statistical Inference

1

Answer questions regarding the feasibility of performing an experiment.

1) In which of the following cases would simulation be most appropriate?

A) Estimating the likelihood of a rare event B) Calculating the sum of a dataset C) Determining the interquartile range D) Computing the variance of a sample Answer: A

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2) When would simulation be an effective tool for making statistical inferences?

A) When analytical solutions are complex or unavailable B) When the dataset is small and easily manageable C) When calculating descriptive statistics D) When performing simple arithmetic operations Answer: A 2

Define and explain terms related to hypothesis tests.

1) Which of these best describes a null hypothesis?

A) A hypothesis that there is no effect or no difference B) A hypothesis that has been proven null and void C) The primary hypothesis that the researcher believes to be true D) A hypothesis that is formulated after seeing the data Answer: A 2) What is the term for the threshold at which we reject the null hypothesis in a hypothesis test?

A) Significance level B) p-value C) Alternative hypothesis D) Null hypothesis Answer: A 3

Use probabilities to make a statistical decision.

1) Which of the following is true if a hypothesis test results in a p-value of 0.03 and the significance level is set at 0.05?

A) The null hypothesis should be rejected.

B) The null hypothesis should not be rejected.

C) The alternative hypothesis should be rejected.

D) The test should be repeated.

Answer: A 2) If a hypothesis test yields a p-value of 0.08 with a significance level of 0.05, what is the appropriate conclusion?

A) Fail to reject the null hypothesis.

B) Reject the null hypothesis.

C) Accept the alternative hypothesis.

D) Increase the sample size.

Answer: A

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4

Answer questions involving hypotheses, statistical significance, and p-values.

1) What does statistical significance imply in the context of hypothesis testing?

A) The results are unlikely to have occurred by chance alone, assuming the null hypothesis is true.

B) The results are guaranteed to be true.

C) The results prove the alternative hypothesis.

D) The results are always practically significant.

Answer: A 2) How is the significance level used in hypothesis testing?

A) It is the threshold at which we reject the null hypothesis.

B) It is the probability of obtaining an extreme test statistic.

C) It is the hypothesis that contradicts the null hypothesis.

D) It is the measure of the effect size.

Answer: A 7.4

Exploratory Data Analysis to Begin Inference

1

Answer questions involving data analysis.

1) A researcher may use which of the following as the basis for an experiment's evidence?

A) Statistic B) Parameter C) Hypothesis D) Significance Level Answer: A 2) What dataset would be appropriate for analyzing the correlation between daily screen time and sleep quality among adults?

A) A dataset containing daily screen time and sleep quality ratings for a sample of adults B) A dataset containing daily screen time for teenagers and sleep quality ratings for adults C) A dataset containing only daily screen time for adults D) A dataset containing only sleep quality ratings for adults Answer: A 7.5

Drawing Conclusions in an Observational Study

1

Define and explain terms related to hypothesis tests.

1) Which term refers to the hypothesis that there is no effect or no difference, and it is the hypothesis that researchers aim to test against?

A) Null hypothesis B) Alternative hypothesis C) p-value D) Significance level Answer: A 4 Copyright © 2026 Pearson Education, Inc.

2) What is the term for the hypothesis that contradicts the null hypothesis and reflects the researcher's suspicions about the population characteristic?

A) Alternative hypothesis B) Null hypothesis C) p-value D) Significance level Answer: A 2

Answer questions involving drawing conclusions in an observational study.

1) Which of the following is a limitation of observational studies compared to randomized experiments?

A) Inability to control for confounding variables B) Inability to collect large samples C) Inability to measure outcomes D) Inability to use statistical methods Answer: A 2) What is a common method to address confounding variables in observational studies?

A) Using statistical controls B) Using larger sample sizes C) Increasing the number of variables D) Reducing the scope of the study Answer: A 3

Draw conclusions from hypothesis tests.

1) When performing a t-test, the standardized statistic can also be referred to as the: A) T-test statistic B) Z-score C) Star statistic D) P-value Answer: A 2) A p-value is computed as ________ A) The proportion of all possible results at least as surprising as the ones observed in our random sample B) (sample mean - hypothesized mean)/ (s/sqrt(n)) C) (sample mean - hypothesized mean) / s D) The correlation between the independent and dependent variables in our study Answer: A

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7.6

A/B Testing as a Case of Experiments

1

Answer questions involving hypothesis tests.

1) What is the role of hypothesis testing in A/B testing?

A) To determine if there is a statistically significant difference between groups B) To increase the sample size C) To collect more data D) To implement changes based on intuition Answer: A 2) Which term describes the group in an A/B test that receives the existing version of the variable being tested?

A) Control group B) Treatment group C) Experimental group D) Sample group Answer: A 2

Answer questions involving implementing and analyzing A/B tests.

1) A/B testing can be used in the graphic arts to test the effectiveness of different kinds of A) Images B) Text C) Framing D) All of the above Answer: D 2) What does a high p-value indicate in the context of an A/B test?

A) There is no strong evidence against the null hypothesis.

B) The test was successful.

C) The alternative hypothesis is true.

D) The sample size is too large.

Answer: A 3

Answer conceptual questions involving experiments.

1) In the context of experiments, what does the term "confounding variable" mean?

A) A variable that influences both the explanatory and response variables, leading to a spurious association between them B) A variable that is intentionally manipulated by the researcher C) A variable that is measured but not analyzed D) A variable that has no impact on the outcome of the experiment Answer: A

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2) In an A/B test, what does it mean if the p-value is less than the significance level?

A) Reject the null hypothesis and conclude that there is a significant difference between the control and treatment groups.

B) Fail to reject the null hypothesis and conclude that there is no significant difference between the control and treatment groups.

C) Collect more data before making a conclusion.

D) Increase the sample size to achieve significance.

Answer: A 4

Perform an A/B test.

1) Which of the following is the correct interpretation of a statistically significant result in an A/B test?

A) The observed difference is unlikely to have occurred by chance alone.

B) The null hypothesis is true.

C) The sample size was too small.

D) The test was not properly conducted.

Answer: A 2) The results of more than two treatments of a digital phenomenon are compared in A) A/B testing B) A/B/n testing C) Bandit testing D) B and C Answer: D

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Data Science for All, 1e (Glanz/Davis) Chapter 8 Machine Learning 8.1

Artificial Intelligence

1

Identify properties of artificial intelligence and machine learning.

1) Artificial intelligence involves computer-controlled machines acquiring: A) Human-like mental capacities B) Improved speed C) The ability to forecast future results D) A human appearance Answer: A 2) What is a common application of machine learning in everyday technology?

A) Face ID on smartphones B) Traditional desktop calculators C) Analog wristwatches D) Manual typewriters Answer: A 2

Identify objects and technologies that use artificial intelligence.

1) Which technology relies heavily on artificial intelligence for its functionality?

A) Autonomous vehicles B) Mechanical watches C) Manual coffee grinders D) Traditional landline phones Answer: A 2) Which of the following devices typically uses machine learning algorithms?

A) Email spam filters B) Mechanical clocks C) Manual toothbrushes D) Analog radios Answer: A 8.2

Three Steps in the Machine Learning Process

1

Identify properties of supervised learning and unsupervised learning.

1) Which scenario is an example of supervised learning?

A) A model trained to classify emails as spam or not spam using labeled training data B) A model identifying clusters of customer behavior without pre-existing labels C) A model optimizing game strategies through trial and error D) A model that generates random data for analysis Answer: A 1 Copyright © 2026 Pearson Education, Inc.

2) Which of the following is true about supervised learning?

A) It involves a response variable for prediction.

B) It does not involve any response variable.

C) It cannot be used for classification problems.

D) It is only used for clustering data.

Answer: A 2

Identify properties of the general machine learning process.

1) Which step in the machine learning process involves training the model on data?

A) Analyzing data B) Ingesting data C) Producing model-based results D) Deploying the model Answer: A 2) What is the final step in the machine learning process?

A) Producing model-based results B) Ingesting data C) Analyzing data D) Validating the model Answer: A 3

Determine the step in the machine learning process for a given situation.

1) In a machine learning application, syncing with an academic calendar to track deadlines falls under which step?

A) Ingesting data B) Analyzing data C) Producing model-based results D) Testing the model Answer: A 2) What is an example of producing model-based results in the machine learning process?

A) Generating movie recommendations based on user preferences B) Collecting and cleaning data from various sources C) Training a neural network on image data D) Assessing the performance of a regression model Answer: A

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8.3

Characteristics of Machine Learning Methods

1

Distinguish between different machine learning models and identify their properties.

1) Which of the following is a key property of linear regression models?

A) They predict continuous outcomes based on a linear relationship between input features.

B) They predict categorical outcomes based on logistic functions.

C) They use multiple decision trees to improve performance.

D) They reduce dimensionality by transforming features.

Answer: A 2) Which of the following models is used to predict continuous outcomes?

A) Linear regression B) Logistic regression C) Decision tree D) K-means clustering Answer: A 2

Make classifications using a decision tree.

1) When constructing a decision tree, what does each internal node represent?

A) A question about a variable in the dataset B) A final prediction C) A split point for numerical data only D) A leaf node Answer: A 2) In a decision tree, what does a leaf node represent?

A) A predicted value of the response variable B) A question about a variable in the datase C) A split into branches D) A root node Answer: A 3

Decide between using a regression or classification approach.

1) Which machine learning method is most appropriate for predicting house prices?

A) Regression B) Classification C) Clustering D) Dimensionality reduction Answer: A

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2) What type of response variable is suitable for a classification model?

A) Categorical B) Numerical C) Continuous D) Ordinal Answer: A 8.4

Machine Learning Method Evaluation

1

Define terms and identify properties related to evaluation and validation of models.

1) What is the primary purpose of model validation in machine learning?

A) To assess the model's performance on new, unseen data B) To collect additional data for training C) To fine-tune the model parameters on the training data D) To deploy the model in a production environment Answer: A 2) Which of the following metrics is used to evaluate the performance of a regression model?

A) Mean Squared Error (MSE) B) Accuracy C) Precision D) Recall Answer: A 2

Calculate accuracy, precision, recall, and specificity.

1) What does specificity measure in a classification model?

A) The proportion of negative predictions that are correct B) The proportion of positive predictions that are correct C) The proportion of actual positives that are predicted correctly D) The overall accuracy of the model Answer: A 2) Which of the following metrics is most important for a medical diagnosis model where false negatives are critical to avoid?

A) Recall B) Accuracy C) Precision D) Specificity Answer: A

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3

Use accuracy to determine whether models are overfitted, well-fitted, or underfitted.

1) What is the main issue with an overfitted model?

A) It fails to generalize well to new data.

B) It does not fit the training data well.

C) It uses too few variables.

D) It has high bias and low variance.

Answer: A 2) What scenario indicates an underfitted model?

A) Low accuracy on both training and test data B) High accuracy on both training and test data C) High accuracy on training data but low accuracy on test data D) Low accuracy on training data but high accuracy on test data Answer: A 8.5

Deep Learning

1

Identify properties of deep learning and neural networks.

1) Which of the following is used by machine learning to mimic human brain processes for learning?

A) Neural networks B) Logistic regression C) Deep learning D) Decision trees Answer: A 2) Which of the following best describes a deep neural network?

A) A neural network with multiple hidden layers B) A neural network with a single hidden layer C) A neural network used only for supervised learning D) A neural network that does not require training data Answer: A 8.6

Use High-Quality Data in Machine Learning

1

Identify properties of data that can affect machine learning outcomes.

1) Why is data diversity important in machine learning?

A) It ensures that the model can generalize well to different populations.

B) It reduces the computational power required.

C) It eliminates the need for data cleaning.

D) It ensures the data is always up-to-date.

Answer: A

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2) How does data volume affect machine learning outcomes?

A) Larger data volumes can improve model performance by providing more information for training.

B) Smaller data volumes are always preferable for faster training times.

C) Larger data volumes eliminate the need for data preprocessing.

D) Smaller data volumes ensure higher accuracy.

Answer: A

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Data Science for All, 1e (Glanz/Davis) Chapter 9 Supervised Learning 9.1

Linear Regression with No Explanatory Variables

1

Identify properties of modeling and reasons to use types of modeling.

1) Which of the following is a primary reason for using modeling in data science?

A) Inference B) Prediction C) Visualization D) Exploration Answer: A 2) What is the conceptual transition from describing data to estimating characteristics and relationships in the population called?

A) Modeling B) Inference C) Prediction D) Exploration Answer: A 2

Identify properties of linear regression and the null model.

1) What is the primary purpose of the null model in linear regression?

A) To provide a baseline for comparison B) To visualize data C) To organize data D) To collect data Answer: A 2) Which of the following techniques can help improve the accuracy of predicting the response variable in linear regression with no explanatory variables?

A) Using a larger sample size B) Using a smaller sample size C) Using the median instead of the mean D) Using only positive values Answer: A

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9.2

Linear Regression with a Categorical Explanatory Variable

1

Identify explanatory and response variables.

1) In a study examining the effect of diet on weight loss, what is the explanatory variable?

A) The type of diet B) The amount of weight lost C) The participants' age D) The study duration Answer: A 2) What variable would be considered the response variable in a study examining the relationship between study time and exam scores?

A) Exam scores B) Study time C) Student age D) Number of subjects studied Answer: A 2

Identify properties and benefits of modeling.

1) What characteristic of a model improves when including additional relevant variables?

A) Variance B) Bias C) Overfitting D) Generalizability Answer: A 2) How does modeling help in understanding data?

A) It provides a mathematical description of relationships.

B) It always provides exact predictions.

C) It eliminates the need for visualizations.

D) It removes the need for assumptions.

Answer: A 3

Determine appropriate modeling procedures for types of situations.

1) When should a two-sample t-test be used instead of a linear regression model?

A) When comparing the means of two independent groups B) When predicting a response variable based on an explanatory variable C) When the data is non-numeric D) When the sample size is very large Answer: A

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2) What is a key consideration when choosing between a two-sample t-test and a linear regression model?

A) Whether the goal is to compare means or predict values B) Whether the data is categorical or continuous C) Whether the sample size is small or large D) Whether the data is from a controlled experiment Answer: A 4

Perform a two-sample hypothesis test for a difference of means.

1) What is the null hypothesis in a two-sample hypothesis test for a difference of means?

A) The means of the two samples are equal.

B) The means of the two samples are different.

C) The variances of the two samples are equal.

D) The variances of the two samples are different.

Answer: A 2) In a two-sample t-test, what is the alternative hypothesis?

A) The means of the two samples are different.

B) The means of the two samples are equal.

C) The variances of the two samples are different.

D) The variances of the two samples are equal.

Answer: A 5

Perform and compare two-sample t-tests and linear regressions.

1) What is the purpose of performing a two-sample t-test in the context of linear regression with a categorical explanatory variable?

A) To compare the means of two groups B) To compare the variances of two groups C) To determine the correlation between two variables D) To predict the response variable Answer: A 2) In a study comparing bill depth between male and female penguins, the p-value from the twosample t-test is 0.03. What does this indicate about the difference in bill depth between genders?

A) There is a statistically significant difference.

B) There is no statistically significant difference.

C) The sample size is too small to conclude.

D) More data is needed to determine significance.

Answer: A

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9.3

Linear Regression with a Categorical Explanatory Variable

1

Answer conceptual questions about predicting values using linear regression.

1) The primary tools for evaluating relationships between two quantitative variables are: A) Scatterplot and correlation coefficient B) Histogram and description of skew C) Boxplot and interquartile range D) Means and standard deviations Answer: A 2) What is the primary purpose of using a linear regression model?

A) To predict the value of a response variable based on an explanatory variable B) To find the mean of the explanatory variable C) To calculate the median of the response variable D) To determine the mode of the explanatory variable Answer: A 2

Interpret values obtained in linear regression analysis.

1) Interpret the y-intercept for a linear regression model A) It represents the correlation between the explanatory variable and the response variable.

B) It may not have a meaningful interpretation.

C) It represents the predicted value of the response variable when the value of the explanatory variable is 0.

D) B and C Answer: D 2) In a regression model with the equation y = 12 - 3x, what does the intercept of 12 represent?

A) The value of y when x is zero B) The value of y when x is one C) The value of x when y is zero D) The value of x when y is twelve Answer: A 3

Identify possible explanatory variables in linear regression.

1) Which of the following could be a potential explanatory variable in a linear regression model predicting house prices?

A) Square footage of the house B) The price of a cup of coffee C) The number of pets in the house D) The color of the house Answer: A

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2) Which of the following is an example of a quantitative explanatory variable in a linear regression model?

A) Income level of the household B) Education level of the household C) Region where the household is located D) Occupation of the household head Answer: A 4

Compute a linear regression.

1) Which of the following best describes the equation of a linear regression line?

A) y = mx + b B) y = a + bx^2 C) y = ax^2 + bx + c D) y = a/x Answer: A 2) Which of the following is a necessary step in computing a linear regression?

A) Calculating the correlation coefficient B) Calculating the mode of the dataset C) Determining the range of the dataset D) Calculating the variance of the dataset Answer: A

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5

Interpret a linear regression equation and extrapolate data.

Case Study Data scientists commonly employ traditional linear regression for quantifying relationships between variables in medical research. Regression can be relevant to research in every field of medicine. Here, we will explore how researchers in the field of anesthesia used simple linear regression involving one response variable and one explanatory variable to evaluate an intravenous (delivered via an injection into a vein) anesthetic called propofol, which is widely used in clinical settings.

An article in the medical journal Anesthesia & Analgesia detailed the results of a linear regression analysis performed on rats that used tissue or plasma propofol concentrations to predict exhaled propofol concentrations. Propofol can be measured in exhaled gas. Researchers were curious about how exhaled propofol concentrations correlated with the propofol concentrations in different tissue types.

To investigate these relationships, the researchers ran several linear regression models, each using one of the various explanatory variables with the same response variable: propofol concentration in exhaled breath. These explanatory variables included propofol concentration in plasma and propofol concentration in the brain, lung, liver, kidney, muscle, and fat tissue after 6 hours, resulting in a total of seven different models.

For each model, the researchers recorded the correlation coefficient of the explanatory and response variables. They found that the correlation of exhaled versus plasma concentrations was similar to that of exhaled versus brain tissue concentrations, with correlation coefficients of 0.71 and 0.75, respectively.

1) According to the case study, how did the anesthesiology researchers use linear regression?

A) To quantify relationships between tissue and exhaled drug concentrations B) To identify nonlinear relationships C) To classify patients into high or low-risk categories D) To predict drug concentrations from patient demographics Answer: A 2) In the anesthesia case study, why did exhaled and plasma drug concentrations have a different strength of correlation?

A) Exhaled concentrations depend on tissue concentration.

B) Plasma concentrations followed a nonlinear trend.

C) Model assumptions were violated for plasma.

D) More variables were needed to predict plasma concentrations.

Answer: A

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9.4

Multiple Linear Regression

1 Identify changes to linear regression models as explanatory variables are added or removed.

1) What is the benefit of separating the data by species before modeling bill depth vs bill length?

A) The relationship appears more linear.

B) Fewer data points are needed.

C) Prediction intervals get wider.

D) Model interpretation becomes easier.

Answer: A 2) Removing an explanatory variable from a multiple linear regression model will generally have what effect on the model's R-squared value?

A) Decrease B) Increase C) Remain the same D) Become undefined Answer: A 2

Predict relationships and values based on given properties of linear regression models.

1) If a linear regression model with multiple explanatory variables is used to predict a response variable, which of the following statements is true?

A) Each explanatory variable contributes to the prediction based on its coefficient.

B) Only the explanatory variable with the highest coefficient contributes to the prediction.

C) Explanatory variables do not affect the prediction.

D) Only the intercept contributes to the prediction.

Answer: A 2) Which of the following best describes the relationship between the number of explanatory variables in a regression model and the risk of overfitting?

A) More explanatory variables generally increase the risk of overfitting.

B) Fewer explanatory variables generally increase the risk of overfitting.

C) The number of explanatory variables does not affect the risk of overfitting.

D) The risk of overfitting is unrelated to the number of explanatory variables.

Answer: A 3

Compute and compare linear regressions with different sets of explanatory variables.

1) By constructing a linear model for different values of a categorical variable in "the most flexible way possible," the models for each value of the categorical variable: A) Can have different slopes and different y-intercepts B) Will have the same slope C) Will have the same y-intercept D) Can have different slopes but y-intercepts will be equal Answer: A 7 Copyright © 2026 Pearson Education, Inc.

2) When comparing two multiple linear regression models, what does a higher adjusted Rsquared value indicate?

A) The model explains more variability in the response variable.

B) The model has fewer explanatory variables.

C) The model has a higher intercept.

D) The model has lower multicollinearity.

Answer: A 9.5

Nonparametric Regression Models

1

Identify properties of decision tree models.

1) What is a key property of decision tree models regarding their structure?

A) They split the data based on the most significant explanatory variables.

B) They always produce linear decision boundaries.

C) They require a large amount of data to function.

D) They are not prone to overfitting.

Answer: A 2) What is one advantage of decision tree models in terms of interpretability?

A) They produce a visual representation of the decision-making process.

B) They require fewer computational resources.

C) They always provide the best predictive accuracy.

D) They are not affected by outliers.

Answer: A 2

Identify properties of random forest models.

1) What is a key property of random forest models regarding their structure?

A) They aggregate the predictions of multiple decision trees.

B) They use a single deep decision tree.

C) They focus on a single explanatory variable.

D) They minimize the number of splits.

Answer: A 2) What is one advantage of using random forest models over single decision trees?

A) They reduce the risk of overfitting.

B) They are easier to interpret.

C) They require fewer computational resources.

D) They are simpler to implement.

Answer: A

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3

Identify properties of k-nearest neighbors models.

1) How does increasing the value of k in a k-nearest neighbors model typically affect its performance?

A) It reduces the risk of overfitting.

B) It increases the risk of overfitting.

C) It has no effect.

D) It always improves performance.

Answer: A 2) What is one disadvantage of using k-nearest neighbors (kNN) for regression?

A) It can be computationally expensive with large datasets.

B) It is difficult to interpret.

C) It always provides the best predictive accuracy.

D) It is not suitable for numerical data.

Answer: A 4

Answer conceptual questions about modeling procedures and machine learning.

1) Which is true about the test error?

A) Estimates performance on new data B) Uses the full dataset while test error uses a subset C) Calculate the difference between training and test error.

D) Estimates performance on all datasets Answer: A 2) In the context of machine learning, what is overfitting?

A) When a model performs well on training data but poorly on test data B) When a model performs equally well on both training and test data C) When a model has too few parameters D) When a model uses unsupervised learning techniques Answer: A 5

Compare properties of different modeling methods.

1) Which of the following is a disadvantage of using linear regression models compared to nonparametric models?

A) They assume a linear relationship between the explanatory and response variables.

B) They are computationally intensive.

C) They are difficult to interpret.

D) They require a large amount of data.

Answer: A

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2) What is one key advantage of k-nearest neighbors (kNN) over decision trees?

A) kNN does not assume a specific model form.

B) kNN is easier to interpret.

C) kNN is less computationally expensive.

D) kNN is less sensitive to outliers.

Answer: A 6

Compute and interpret decision tree models.

1) Which metric is commonly used to evaluate the performance of a decision tree model?

A) R-squared B) Mean Absolute Error (MAE) C) Root Mean Squared Error (RMSE) D) Adjusted R-squared Answer: A 2) How does increasing the maximum depth of a decision tree typically affect its performance on training data?

A) It increases the risk of overfitting.

B) It decreases the risk of overfitting.

C) It has no effect.

D) It always improves performance.

Answer: A 7

Compute and interpret random forest models.

1) What is the purpose of aggregating the predictions of multiple trees in a random forest model?

A) To improve prediction accuracy B) To simplify the model C) To reduce computational cost D) To focus on a single variable Answer: A 2) Which of the following is a characteristic of random forest models?

A) They reduce overfitting by averaging multiple decision trees.

B) They use a single decision tree for predictions.

C) They require fewer data points than other models.

D) They are simpler to interpret than decision trees.

Answer: A

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8

Compute and evaluate k-nearest neighbors models.

1) What does an R-squared value of 0.85 indicate in a k-nearest neighbors (kNN) model?

A) 85% of the variance in the response variable is explained by the model.

B) 85% of the data points are correctly classified.

C) The model has an 85% error rate.

D) The model uses 85 nearest neighbors for prediction.

Answer: A 2) Which of the following is a characteristic of k-nearest neighbor models?

A) They are based on the distance between data points.

B) They use a single decision tree for predictions.

C) They require fewer data points than other models.

D) They are simpler to interpret than decision trees.

Answer: A 9

Compute and compare models using different methods.

1) When comparing models, why is it important to use a test dataset?

A) To evaluate the model's performance on unseen data B) To improve the training accuracy C) To reduce the computational complexity D) To minimize the number of parameters Answer: A 2) What is the advantage of using the R-squared metric for comparing regression models?

A) It provides a measure of how well the model explains the variability of the response variable.

B) It is less sensitive to outliers.

C) It is easier to compute than other metrics.

D) It avoids the problem of overfitting.

Answer: A 9.6

Classification Models

1

Identify examples of types of variables and classification problems.

1) Which of the following is NOT an example of a classification problem?

A) Predicting the exact age of a person based on their height and weight B) Predicting whether an email is spam or not C) Predicting the species of a flower based on its petal measurements D) Predicting whether a customer will buy a product or not Answer: A

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2) Which of the following scenarios illustrates a classification problem in marketing?

A) Segmenting customers into different groups based on purchase behavior B) Predicting the total sales for the next quarter C) Estimating the average customer satisfaction score D) Forecasting the number of new customers Answer: A 2

Identify properties of regression and classification models.

1) Which of the following is a property of a logistic regression model?

A) It provides probabilities for a binary outcome.

B) It predicts continuous outcomes.

C) It requires the response variable to be quantitative.

D) It uses decision trees to make predictions.

Answer: A 2) Which of the following best describes the property of overfitting in a model?

A) The model performs well on training data but poorly on test data.

B) The model performs well on both training and test data.

C) The model performs poorly on training data but well on test data.

D) The model performs poorly on both training and test data.

Answer: A 3

Identify appropriate regression models.

1) Which regression model is most appropriate for predicting a binary outcome?

A) Logistic Regression B) Linear Regression C) Polynomial Regression D) Ridge Regression Answer: A 2) What regression model is suitable for predicting a continuous outcome with a linear relationship to the predictor variables?

A) Linear Regression B) Logistic Regression C) Multinomial Logistic Regression D) Decision Tree Regression Answer: A

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4 Compute, interpret, and compare logistic regression models using different sets of explanatory variables.

1) Which of the following best describes the purpose of logistic regression?

A) To predict a binary outcome based on one or more predictor variables B) To predict a continuous outcome based on one or more predictor variables C) To find the correlation between two continuous variables D) To classify data into more than two categories Answer: A 2) What is the main difference between logistic regression and linear regression?

A) Logistic regression predicts probabilities for a binary outcome, while linear regression predicts continuous outcomes.

B) Logistic regression predicts continuous outcomes, while linear regression predicts probabilities for a binary outcome.

C) Logistic regression uses a continuous response variable, while linear regression uses a binary response variable.

D) Logistic regression uses the least squares method, while linear regression uses maximum likelihood estimation.

Answer: A 5

Compute and compare classification models using different methods.

1) Which classification method involves creating a series of splits based on the explanatory variables?

A) Decision Trees B) Logistic Regression C) k-Nearest Neighbors D) Naive Bayes Answer: A 2) What classification method is most suitable for a binary response variable?

A) Logistic Regression B) Decision Trees C) k-Nearest Neighbors D) Naive Bayes Answer: A

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Data Science for All, 1e (Glanz/Davis) Chapter 10 Unsupervised Learning 10.1 1

What is Unsupervised Learning?

Identify anomalies in data sets.

1) Which method would you use to detect fraudulent transactions in a bank's dataset?

A) Anomaly detection B) Cluster analysis C) Dimensionality reduction D) Variable projection Answer: A 2) What is the difference between an anomaly and an outlier?

A) An anomaly may require immediate action, while an outlier may not.

B) An anomaly is always an error, while an outlier is not.

C) An anomaly conforms to a pattern, while an outlier does not.

D) An anomaly is a common data point, while an outlier is rare.

Answer: A 2 Determine whether unsupervised or supervised learning is more appropriate for an application.

1) In which scenario is unsupervised learning more appropriate than supervised learning?

A) Group customers based on purchasing behavior.

B) Predict next month's sales.

C) Identify fraudulent transactions.

D) Estimate the price of a house.

Answer: A 2) Which of the following is NOT a use case for unsupervised learning?

A) Predicting stock prices B) Clustering similar documents C) Detecting anomalies in network traffic D) Reducing the dimensionality of a dataset Answer: A 3

Answer conceptual questions about unsupervised learning.

1) Which of the following is NOT considered unsupervised learning?

A) Linear regression B) Cluster analysis C) Dimensionality reduction D) Anomaly detection Answer: A 1 Copyright © 2026 Pearson Education, Inc.

2) Unsupervised learning techniques are particularly useful for ________ A) Analyzing data without a specific variable to predict B) Predicting outcomes based on historical data C) Testing statistical hypotheses about data distributions D) Supervising machine learning algorithms for accuracy Answer: A 4

Answer conceptual questions about anomaly detection and standardized scores.

1) What technique can identify a handful of new variables containing similar information to the full set of original variables?

A) Principal components analysis B) Multiple linear regression C) K-nearest neighbors classification D) Hierarchical clustering Answer: A 2) Why is anomaly detection considered an unsupervised learning task?

A) Because it does not require labeled data for training B) Because it predicts specific outcomes C) Because it involves a response variable D) Because it always uses dimensionality reduction Answer: A 5

Answer conceptual questions about cluster analysis.

1) Why might a data scientist choose to use cluster analysis?

A) To explore potential relationships and patterns within a dataset B) To predict a target variable C) To standardize data points D) To reduce the number of datasets Answer: A 2) What is a common challenge when performing cluster analysis?

A) Determining the appropriate number of clusters to use B) Identifying the target variable C) Calculating the mean and standard deviation of data points D) Standardizing the dataset Answer: A

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6

Answer conceptual questions about dimensionality reduction .

1) What term refers to transforming data into a smaller, more manageable form while retaining key information?

A) Dimensionality reduction B) Multiple linear regression C) Logistic regression D) Variable selection Answer: A 2) What does dimensionality reduction refer to?

A) Transforming data into a smaller, more manageable form B) Reducing the number of observations in a dataset C) Identifying and removing noninformative variables D) Predicting a categorical response variable Answer: A 10.2 1

Getting to Know Cluster Analysis Identify clusters in data using scatterplots and computed distances.

1) When analyzing a scatterplot to identify clusters, which of the following is a key indicator of clusters in the data?

A) Dense groupings of points B) Points evenly spread across the plot C) Points forming a straight line D) Points forming a circular pattern Answer: A 2) In a scatterplot, if two points are very close to each other, what does this imply about their similarity?

A) They are highly similar B) They are not similar C) They are somewhat similar D) They are identical Answer: A 2

Answer conceptual questions about cluster analysis.

1) What is the primary goal of cluster analysis?

A) To group observations based on their similarities B) To predict future data points C) To determine the cause of data trends D) To identify outliers Answer: A

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2) What is a key challenge in cluster analysis?

A) Determining the number of clusters B) Collecting enough data C) Ensuring data privacy D) Visualizing data trends Answer: A 3

Answer conceptual questions about determining similarity between observations.

1) Which of the following is NOT used to determine how similar two observations are in cluster analysis?

A) Whether they are anomalies B) Their correlation C) Their distance D) Their values for key variables Answer: A 2) What linkage criterion defines the distance between two clusters as the shortest distance between points in each cluster?

A) Single linkage B) Complete linkage C) Average linkage D) Centroid linkage Answer: A 4

Answer conceptual questions about determining similarity between clusters.

1) Which linkage method is most likely to result in compact clusters?

A) Complete linkage B) Single linkage C) Centroid linkage D) Average linkage Answer: A 2) Which of the following is a key advantage of average linkage in clustering?

A) It balances between compactness and separation.

B) It always creates the smallest clusters.

C) It is the fastest to compute.

D) It does not require distance metrics.

Answer: A

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10.3

Introduction to K-Means Clustering

1 Compare outputs of cluster analysis when different sets of variables and scaling are used.

1) What clustering method would allow Vista to specify the number of song clusters at the start?

A) K-means clustering B) Hierarchical clustering C) Principal components analysis D) Linear regression Answer: A 2) Which of the following is a potential consequence of not scaling variables before performing k-means clustering?

A) One variable may dominate the distance calculations.

B) The number of clusters will be predetermined.

C) The clustering will be faster.

D) The clusters will be more interpretable.

Answer: A 2

Identify properties of the k-means clustering process.

1) Which of the following is NOT one of the key steps in the k-means clustering algorithm?

A) Predict a response variable based on clusters.

B) Randomly assign observations to clusters.

C) Compute the mean of each cluster.

D) Reassign observations to clusters based on distance to cluster means.

Answer: A 2) In k-means clustering, what is the purpose of the initialization step?

A) To assign initial cluster centers B) To scale the variables C) To determine the number of clusters D) To compute the distance matrix Answer: A 10.4 1

Introduction to Hierarchical Clustering Interpret cluster analysis results.

1) What would Vista use to visualize her song data's full hierarchical clustering process?

A) A dendrogram B) A scatterplot matrix C) A residual plot D) A pie chart Answer: A 5 Copyright © 2026 Pearson Education, Inc.

2) What part of a dendrogram indicates the merging of two clusters?

A) A horizontal line connecting two branches B) A vertical line at the top of the dendrogram C) The overall height of the dendrogram D) The labels at the bottom of the dendrogram Answer: A 2

Compare hierarchical clustering and k-means clustering.

1) Which of the following is a disadvantage of hierarchical clustering compared to k-means clustering?

A) It is computationally more expensive.

B) It requires the number of clusters to be specified in advance.

C) It cannot handle non-linear data structures.

D) It is less interpretable.

Answer: A 2) What clustering method is more suitable for large datasets due to its computational efficiency?

A) K-means clustering B) Hierarchical clustering C) DBSCAN D) Affinity Propagation Answer: A 3

Identify properties of hierarchical clustering and dendrograms.

1) What connects first at the bottom of the dendrogram in agglomerative hierarchical clustering?

A) The two most similar observations B) All observations into one cluster C) The two most dissimilar observations D) The two most similar clusters Answer: A 2) What is a dendrogram?

A) A tree-like diagram that records the sequences of merges or splits in hierarchical clustering B) A plot used to determine the optimal number of clusters in k-means clustering C) A matrix showing the distance between every pair of points in the dataset D) A graph showing the silhouette scores of clusters Answer: A

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10.5 1

Assessing the Quality of Clusters Perform cluster analysis for varying numbers of clusters and evaluate the results.

1) When interpreting a variability ratio graph, what does the "elbow" point typically represent?

A) The optimal number of clusters B) The maximum within-cluster variability C) The minimum between-cluster variability D) To calculate the mean of each cluster Answer: A 2) What is the primary purpose of plotting a variability ratio graph in cluster analysis?

A) To determine the appropriate number of clusters B) To identify outliers in the data C) The point where the variability ratio is zero D) To compare different clustering algorithms Answer: A 2

Answer conceptual questions about evaluating results of clustering procedures.

1) Which metric is used to evaluate the separation between clusters?

A) Between-cluster variability B) Within-cluster variability C) Cluster density D) Cluster centroid distance Answer: A 2) Which of the following is NOT a typical consideration when evaluating clustering results?

A) Total number of data points in the dataset B) Within-cluster variability C) Between-cluster variability D) Size of each cluster Answer: A 3

Identify properties of variability and the variability ratio.

1) Lower within-cluster variability and higher between-cluster variability generally indicate: A) Better clustering results B) Worse clustering results C) The need for more principal components D) Observations should be centered and scaled Answer: A

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2) Higher between-cluster variability indicates ________ A) Clusters that are sufficiently distinct B) Worse clustering results C) Clusters that are too small D) Observations that should be scaled and centered Answer: A

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