Data-Driven Decision Making Test Preparation
Course Introduction
Data-Driven Decision Making equips students with the knowledge and skills to leverage quantitative data and analytical tools in organizational decision processes. This course introduces foundational concepts of data collection, analysis, and interpretation, emphasizing their application in real-world business scenarios. Students learn to use statistical reasoning, data visualization, and predictive modelling to identify trends, solve complex problems, and support strategic planning. Through practical case studies and hands-on projects, learners gain experience in transforming raw data into actionable insights, enabling evidence-based decisions that drive organizational success.
Recommended Textbook Essentials of Business Analytics 1st Edition by Jeffrey D. Camm
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688 Flashcards
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Chapter 1: Introduction
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Sample Questions
Q1) _____ is the most critical step in a decision making process.
A) Choosing an alternative
B) Identifying and defining the problem
C) Evaluating the alternatives
D) Determining the set of alternatives
Answer: B
Q2) A better understanding of consumer behavior through analytics leads to a(n):
A) decrease in demand.
B) assured increase in sales.
C) more effective pricing strategies.
D) poor customer satisfaction.
Answer: C
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) A summary of data that shows the number of observations in each of several nonoverlapping bins is called
A) a frequency distribution.
B) a sample summary.
C) a bin distribution.
D) an observed distribution.
Answer: A
Q2) A variable is defined as a
A) quantity of interest that can take on same values.
B) set of values.
C) quantity of interest that can take on different values.
D) characteristic that takes on same values from a set of values.
Answer: C
Q3) 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 mean, median, and mode.
Answer: Mean = 18.53.
Median = 19.
Mode = 15.
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Chapter 3: Data Visualization
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Sample Questions
Q1) A disadvantage of a stacked column and bar chart is
A) it does not include all the values of the variable.
B) it cannot be used to compare relative values of quantitative variables for the same category.
C) it has difficulty perceiving small differences in areas.
D) it is used when many quantitative variables need to be displayeD)
Answer: C
Q2) An alternative for a stacked column chart when comparing more than a couple of quantitative variables in each category is a
A) stacked bar chart.
B) clustered column chart.
C) pie chart.
D) clustered bar chart.
Answer: B
Q3) The best way to differentiate chart elements is using A) too many colors.
B) labels.
C) bubbles.
D) chart titles.
Answer: B
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Chapter 4: Linear Regression
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Sample Questions
Q1) The difference between the observed value of the dependent variable and the value predicted using the estimated regression equation is known as the _____.
A) constant term
B) error term
C) residual
D) model parameter
Q2) _____ is used to test the hypothesis that the values of the regression parameters 1, 2, . . . , q are all zero.
A) An F test
B) A t test
C) The least squares method
D) Extrapolation
Q3) _____ 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
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Chapter 5: Time Series Analysis and Forecasting
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Sample Questions
Q1) The moving averages method refers to a forecasting method that:
A) moves up the average of every subsequent forecast by one.
B) uses regression relationship based on past time series values to predict the future time series values.
C) relates a time series to other variables that are believed to explain or cause its behavior.
D) uses the average of the most recent data values in the time series as the forecast for the next perioD)
Q2) A time series with a seasonal pattern can be modeled by treating the season as a
A) predictor variable
B) dependent variable
C) dummy variable
D) categorical variable
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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Chapter 6: Data Mining
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Sample Questions
Q1) _____ is a measure of calculating dissimilarity between clusters by considering only the two most dissimilar observations in the two clusters.
A) Single linkage
B) Complete linkage
C) Average linkage
D) Average group linkage
Q2) Separate error rates with respect to the false negative and false positive cases are computed to take into account the:
A) assymetric costs in misclassification.
B) symmetric weights of these two cases.
C) distortions due to outliers.
D) effect of sampling error.
Q3) In which of the following data-mining process steps is the data manipulated to make it suitable for formal modeling?
A) Data sampling
B) Data preparation
C) Model construction
D) Model assessment
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Chapter 7: Spreadsheet Models
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Sample Questions
Q1) The impact of two inputs on the output of interest is given by a _____.
A) Goal Seek
B) Watch Window
C) multiple-way data table
D) two-way data table
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 influence in an influence diagram is visually depicted by:
A) a circular symbol
B) an arrow
C) a straight line
D) the height of the influence diagram
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Chapter 8: Linear Optimization Models
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Sample Questions
Q1) Which of the following error messages is displayed in Excel Solver when attempting to solve an unbounded problem?
A) Solver could not find a feasible solution.
B) Solver cannot improve the current solution. All constraints are satisfied.
C) Solver could not find a bounded solution.
D) Objective Cell values do not converge.
Q2) The intersections of half spaces represent_____.
A) objective functions
B) feasible solutions
C) decision variables
D) 2-spaces
Q3) The term _____ refers to the expression that defines the quantity to be maximized or minimized in a linear programming model.
A) objective function
B) problem formulation
C) decision variable
D) association rule
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Chapter 9: Integer Linear Optimization Models
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Sample Questions
Q1) 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
Q2) A manufacturer makes two types of rubber, Butadiene and Polyisoprene. The plant has two machines, Machine-1 and Machine-2, and both of them are used to make the rubber strips. Machine-1 is available 180 hours per month and Machine-2 is available 200 hours per month. Manufacturing one strip of Butadiene requires 2.75 hours on Machine-1 and 3 hours on Machine-2. For processing one strip of Polyisoprene, it takes 3.5 hours on Machine-1 and 4 hours on Machine-2. Formulate an all-integer model that will determine how many units of each type of the rubber should be used to maximize the manufacturer's contribution to profit if he gets a profit of $20 on Butadiene and $26 on Polyisoprene?
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Chapter 10: Nonlinear Optimization Models
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Sample Questions
Q1) The portfolio variance is the:
A) sum of the squares of the deviations from the mean value under each scenario.
B) average of the sum of the squares of the deviations from the mean value under each investment scenario.
C) average of the product of the squares of the deviations from the mean value under each scenario.
D) average of the sum of the deviations from the mean value under each investment scenario.
Q2) In the Bass forecasting model, parameter m:
A) measures the likelihood of adoption due to a potential adopter being influenced by someone who has already adopted the product.
B) measures the likelihood of adoption, assuming no influence from someone who has already adopted the product.
C) refers to the number of people estimated to eventually adopt the new product.
D) refers to the number of people who have already adopted the new product.
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Chapter 11: Monte Carlo Simulation
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Sample Questions
Q1) Reference - 11.1: The type of distribution shown in the graph is
A) uniform.
B) normal.
C) exponential.
D) beta.
Q2) ASP cannot guarantee an optimal solution to a simulation optimization model because
A) the optimization problem will have only one local optimal solution due to the linear relationship.
B) of sampling error resulting from nonlinear relationships.
C) it is not possible to compare solutions across runs.
D) of sampling error resulting from the presence of the random variables.
Q3) Which of the following parameters is required to convert a computer-generated random variable into a uniform random variable?
A) Range of the distribution
B) Mean of the distribution
C) Variance of the distribution
D) Moments of the distribution
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Chapter 12: Decision Analysis
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Sample Questions
Q1) The states of nature are defined so that they are _____. This means that at least one state of nature must occur at a given time for a chance event.
A) collectively exhaustive
B) mutually exclusive
C) certain events
D) optimistic outcomes
Q2) Reference - 12.2: What would be the joint probabilities, P(U Sj)?
A) 0.83, 0.12, and 0.05
B) 0.49, 0.07, and 0.03
C) 0.47, 0.49, and 0.04
D) 1.00, 0.59, and 1.00
Q3) The study of how changes in the probability assessments for the states of nature or changes in the payoffs affect the recommended decision alternative is known as
A) uncertainty analysis
B) cost analysis
C) sensitivity analysis
D) probability analysis
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