Data-Driven Decision Making
Exam Answer Key
Course Introduction
Data-Driven Decision Making explores the principles and practices of using data analytics to inform and enhance organizational decision processes. Students will learn how to collect, analyze, and interpret data from various sources, leveraging statistical and computational tools to support evidence-based decisions. The course covers topics such as data visualization, predictive modeling, ethical considerations, and the communication of analytical insights to stakeholders. Through case studies and hands-on projects, students will develop practical skills to transform raw data into actionable strategies that drive business and organizational success.
Recommended Textbook Essentials of Business Analytics 1st Edition by Jeffrey
D. Camm
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12 Chapters
688 Verified Questions
688 Flashcards
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Chapter 1: Introduction
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30 Flashcards
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Sample Questions
Q1) A children's apparel manufacturer used descriptive analytics:
A) to present supply chain to managers visually.
B) to achieve efficiency in delivery of goods.
C) to schedule staff and vehicle for delivery.
D) to plan capacity utilization by incorporating the inherent uncertainty in commodities pricing.
Answer: A
Q2) In a financial sector, we use ______ to construct financial instruments such as derivatives.
A) descriptive and prescriptive models
B) predictive models
C) descriptive models
D) prescriptive models
Answer: B
Q3) GE Asset Management uses optimization models to:
A) assess the risk of investment portfolios.
B) forecast future financial performance.
C) successfully manage commercial real estate risk.
D) decide on how to invest its cash received from insurance policies.
Answer: D
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Chapter 2: Descriptive Statistics
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Sample Questions
Q1) Consider a sample on the waiting times (in minutes) at the billing counter in a grocery store to be 15, 24, 18, 15, 21, 20, 15, 22, 19, 16, 15, 22, 20, 15, and 21. Compute the 25th, 50th, and 75th percentiles.
Answer: 25 percentile = 15.
50 percentile = 19.
75 percentile = 21.
Q2) Compute the mode for the following data:
12 16 19 10 12 11 21 12 21 10
A) 21
B) 11
C) 12
D) 10
Answer: C
Q3) A set of values corresponding to a set of variables is defined as a(n) _____. A) quantity
B) event
C) factor
D) observation
Answer: D
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Page 4
Chapter 3: Data Visualization
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61 Flashcards
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Sample Questions
Q1) A _____ is a graphical presentation of the relationship between two quantitative variables.
A) histogram
B) bar chart
C) pie chart
D) scatter chart
Answer: D
Q2) _____ helps in designing effective tables and charts for data visualization.
A) Data-ink ratio
B) Crosstabulation
C) PivotTable
D) Scatter charts
Answer: A
Q3) A data visualization tool that updates in real time and gives multiple outputs is called
A) a data table.
B) a metrics table.
C) the GIS.
D) a data dashboarD)
Answer: D
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Chapter 4: Linear Regression
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Sample Questions
Q1) _____ refers to the data set used to compare model forecasts and ultimately pick a model for predicting values of the dependent variable.
A) Codomain
B) Training set
C) Validation set
D) Range
Q2) The prespecified value of the independent variable at which its relationship with the dependent variable changes in a piecewise linear regression model is referred to as the _____.
A) milestone
B) breakpoint
C) tipping point
D) watchpoint
Q3) The graph of the simple linear regression equation is a(n) _____.
A) ellipse
B) hyperbola
C) parabola
D) straight line
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Chapter 5: Time Series Analysis and Forecasting
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58 Flashcards
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Sample Questions
Q1) Which of the following statements is the objective of the moving averages and exponential smoothing methods?
A) To characterize the variable fluctuations by a smooth curve
B) To smooth out random fluctuations in the time series
C) To characterize the variable fluctuations by an exponential equation
D) To transform a nonstationary time series into a stationary series
Q2) The measures of accuracy of the forecasts:
A) check how well a particular forecasting method is able to reproduce the time series data that are already available.
B) use the current value to estimate how well the model generates previous values correctly.
C) predict the future values and wait for a pre-defined time period to examine how accurate the predictions were.
D) check to see if the forecast error is negative.
Q3) Trends result from:
A) rapidly-arising short-term factors.
B) rapidly-arising long-term factors.
C) slowly-varying short-term factors.
D) slowly-varying long-term factors.
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Page 7
Chapter 6: Data Mining
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Sample Questions
Q1) _____ can be used to partition observations in a manner to obtain clusters with the least amount of information loss due to the aggregation.
A) Single linkage
B) Ward's method
C) Average group linkage
D) Dendrogram
Q2) The impurity of a group of observations is based on the variance of the outcome value for the observations in the group for _____.
A) regression trees
B) time-series plots
C) classification trees
D) cumulative lift charts
Q3) Which of the following methods is used by the analyst to decide if a particular variable needs to be retained in the sample during the sampling process?
A) Descriptive statistics and data visualization
B) Regression
C) Outlier analysis
D) Data Testing
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8
Chapter 7: Spreadsheet Models
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Sample Questions
Q1) The conceptual model:
A) helps in organizing the data requirements.
B) controls the model inputs.
C) has tools defined to identify the optimal solution.
D) explores the effects of changing model parameters.
Q2) With reference to the SUMPRODUCT function, which of the following statements is true?
A) The range of cells for each array must contain only nonzero values.
B) Any cell that does not satisfy the specified given condition or criteria will not be considered.
C) The array appearing as the first argument must be sorted in ascending order.
D) The arrays that appear as arguments must be of the same dimension.
Q3) The SUM function in Excel:
A) adds up all the numbers in the cells diagonally.
B) adds up only positive numbers in a range of cells.
C) adds up all the numbers in a range of cells.
D) adds up the cells specified by a given condition or criteria.
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Chapter 8: Linear Optimization Models
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Sample Questions
Q1) Sunseel Industries produces two types of raw materials A and B, with a production cost of $4 and $8 per unit, respectively. The combined production of the raw materials A and B must be at least 700 units per month. At least 400 units of the raw material B and not more than 1200 units of the raw material A must be produced per month. The processing time for the raw material A is observed to be 5 hours and for B, it is found to be 4 hours. A total of 3000 such hours are available per month. How much of each raw material should be produced in order to minimize the cost. Develop a linear program that Sunseel Industries can use to determine how many units of each raw material to produce to minimize the total cost.
Q2) Clever Sporting Equipment, Inc. makes two types of balls: Soccer balls and Cork balls. The making of each soccer ball and cork ball requires 3 hours and 4 hours of production time, respectively. For the next month, the total production hours of 500 are available. Also, it is given that the combined production quantity for these two balls must be at least 150 units. The objective for this linear programming model is to fulfil the given production requirements at a minimum cost for the total production. The production cost for each Soccer ball is $9 and each Cork ball is $7. Formulate and solve for the recommended production quantities.
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Page 10
Chapter 9: Integer Linear Optimization Models
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Sample Questions
Q1) In a fixed-cost model, each fixed cost is associated with a binary variable and a specification of the:
A) upper bound for the corresponding production variable.
B) upper bound for each of the binary variable.
C) integer constraints involving the corresponding production variables.
D) objective function involving these binary variables only.
Q2) The linear program that results from dropping the integer requirements for the variables in an integer linear program is known as _____.
A) convex hull
B) a mixed-integer linear program
C) LP relaxation
D) a binary integer linear program
Q3) Reference - 9.1. What category does the above objective fall under?
A) Capital budgeting problem
B) Covering problem
C) Fixed-cost problem
D) Product design and market share optimization problem
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11
Chapter 10: Nonlinear Optimization Models
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Sample Questions
Q1) The Lagrangian multiplier is the _____ for a constraint in a nonlinear problem.
A) shadow price
B) payoff value
C) reducing gradient
D) reduced cost
Q2) The reduced gradient is analogous to the _____ for linear models.
A) binary variable
B) binding constraint
C) reduced cost
D) objective coefficient
Q3) In the Bass forecasting model, the _____ measures the likelihood of adoption due to a potential adopter being influenced by someone who has already adopted the product.
A) coefficient of innovation
B) coefficient of imitation
C) coefficient of regression
D) coefficient of the objective function
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12
Chapter 11: Monte Carlo Simulation
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Sample Questions
Q1) Reference - 11.1: Which of the following inferences can be drawn about the monthly salary?
A) The average monthly salary is $3000.
B) The monthly salary is always less than $3000.
C) The monthly salary is always greater than $3000.
D) The range of the monthly salary distribution is $3000 to $5000.
Q2) A distribution of a random variable for which values extremely larger or smaller than the mean are increasingly unlikely can possibly be modeled as a(n) _____ probability distribution.
A) binomial
B) normal
C) exponential
D) gamma
Q3) In a _____, a random variable can take any value in a specified range.
A) discrete probability distribution
B) cumulative distribution
C) relative frequency distribution
D) continuous probability distribution
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Chapter 12: Decision Analysis
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Sample Questions
Q1) _____ are possible outcomes for chance events that affect the consequences associated with a decision alternative.
A) Payoffs
B) Forecasts
C) Decision trees
D) States of nature
Q2) Lines showing the alternatives from decision nodes and the outcomes from chance nodes are called _____.
A) weights
B) payoffs
C) diagonals
D) branches
Q3) _____ refer to graphical representations of the decision problems that show the sequential nature of the decision-making process.
A) Influence diagrams
B) Utility functions
C) Decision trees
D) Payoff tables
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