Business Statistics and Analytics in Practice Bruce Bowerman 9e - Test Bank Instant Download - Complete Test Bank With Answers Sample Questions Are Posted Below Business Statistics and Analytics in Practice, 9e (Bowerman) Chapter 5 Predictive Analytics I: Trees, k-Nearest Neighbors, Naive Bayes', and Ensemble Estimates 1) A classification …
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Business Statistics and Analytics in Practice Bruce Bowerman 9e – Test Bank
Instant Download – Complete Test Bank With Answers
Sample Questions Are Posted Below
Business Statistics and Analytics in Practice, 9e (Bowerman)
Chapter 5 Predictive Analytics I: Trees, k-Nearest Neighbors, Naive Bayes’,
and Ensemble Estimates
1) A classification tree is useful for predicting a quantitative response variable.
Answer: FALSE
Explanation: A classification tree is used to predict a qualitative, or categorical, response variable.
Difficulty: 1 Easy
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by classification trees.
Bloom’s: Remember
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
2) To predict a qualitative, or categorical, response variable we could use a classification tree.
Answer: TRUE
Explanation: A classification tree is used to predict a qualitative, or categorical, response variable.
Difficulty: 1 Easy
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by classification trees.
Bloom’s: Remember
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
3) To predict a quantitative response variable, we could use a regression tree.
Answer: TRUE
Explanation: A regression tree is used to predict a quantitative response variable.
Difficulty: 1 Easy
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by classification trees.
Bloom’s: Remember
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
4) A regression tree is used for predicting a qualitative response variable.
Answer: FALSE
Explanation: A classification tree is used to predict a qualitative, or categorical, response variable.
Difficulty: 1 Easy
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by classification trees.
Bloom’s: Remember
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
5) A quantitative variable which can have only the values of zero (0) or one (1) and which is used to represent a qualitative variable is known as a (1, 0) dummy variable.
Answer: TRUE
Explanation: A (1, 0) dummy variable is a quantitative variable used to represent a qualitative variable.
Difficulty: 1 Easy
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by classification trees.
Bloom’s: Remember
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
6) The confusion matrix for a classification tree shows which combinations of predictor variables cannot be used to predict the response variable.
Answer: FALSE
Explanation: The confusion matrix for a classification tree shows the number of observed response variables that are (or are not) classified correctly by their associated predictor variables using the classification tree.
Difficulty: 2 Medium
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by classification trees.
Bloom’s: Understand
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
7) The confusion matrix is not a good indicator of a classification tree’s accuracy.
Answer: FALSE
Explanation: The confusion matrix is a good indicator of a classification tree’s accuracy.
Difficulty: 1 Easy
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by classification trees.
Bloom’s: Remember
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
8) To “overfit” the data is to adjust the data until it matches our desired classification tree.
Answer: FALSE
Explanation: We do not “overfit” the data by adjusting the data. Rather, we “overfit” the data by developing an overly complex classification tree (or other model) that fits the observed data too closely and thus fails to capture the real underlying data patterns that would help to accurately predict and/or classify future observations.
Difficulty: 2 Medium
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by classification trees.
Bloom’s: Understand
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
9) One approach to avoid overfitting a classification tree is to use a validation data set to identify valid splits and a training data set to train the classification tree on when to stop making splits.
Answer: FALSE
Explanation: The training data set is used to make splits, while the validation data set is used to determine when to stop making splits.
Difficulty: 1 Easy
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by classification trees.
Bloom’s: Remember
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
10) The nearest neighbors to an observation are determined by measuring the distance between the set of predictor variables for that observation and the set of predictor variables for every other observation.
Answer: TRUE
Explanation: The nearest neighbors to an observation are determined by measuring the distance between the set of predictor variables for that observation and the set of predictor variables for every other observation.
Difficulty: 1 Easy
Topic: k-Nearest Neighbors
Learning Objective: 05-03 Interpret the information provided by k-nearest neighbors.
Bloom’s: Remember
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
11) The best value of k to use for the k-nearest neighbors approach to classifying a qualitative response variable is the largest value of k for which all distances between neighbors is less than some prespecified distance.
Answer: FALSE
Explanation: The best value of k is the one which results in the smallest misclassification rate.
Difficulty: 1 Easy
Topic: k-Nearest Neighbors
Learning Objective: 05-03 Interpret the information provided by k-nearest neighbors.
Bloom’s: Remember
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
12) For a sufficiently large value of k, the k-nearest neighbors classification approach will always result in a lower misclassification rate than the simple branch splitting approach of the classification tree.
Answer: FALSE
Explanation: Neither the classification tree approach nor the k-nearest neighbors approach is always guaranteed to result in the lowest misclassification rate, regardless of k.
Difficulty: 1 Easy
Topic: k-Nearest Neighbors
Learning Objective: 05-03 Interpret the information provided by k-nearest neighbors.
Bloom’s: Remember
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
13) The optimal value of k to use for the k-nearest neighbors approach to predicting a quantitative response variable is the value of k that minimizes RMSE (the square root of the mean of the squared deviations of the predicted values from the observed values).
Answer: TRUE
Explanation: The optimal value of k to use for the k-nearest neighbors approach to predicting a quantitative response variable is the value of k that minimizes RMSE (the square root of the mean of the squared deviations of the predicted values from the observed values).
Difficulty: 1 Easy
Topic: k-Nearest Neighbors
Learning Objective: 05-03 Interpret the information provided by k-nearest neighbors.
Bloom’s: Remember
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
14) Naive Bayes’ Theorem assumes that the events that the predictor variables take on the values x1, x2, …, xk are highly correlated for observations that fall into the particular category and statistically independent for observations that do not fall into the particular category.
Answer: FALSE
Explanation: Naive Bayes’ Theorem assumes that the events that the predictor variables take on the values x1, x2, …, xk are statistically independent for observations that fall into the particular category and statistically independent for observations that do not fall into the particular category.
Difficulty: 1 Easy
Topic: Naive Bayes’ Classification
Learning Objective: 05-04 Interpret the information provided by naive Bayes’ classification.
Bloom’s: Remember
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
15) Because different trust levels may be appropriate for different techniques, ensemble estimates may use a weighted average of the different results given by the different techniques.
Answer: TRUE
Explanation: Different trust in different techniques (based on historical RSquare values, misclassification rates, confusion matrices, and/or other metrics) may be a basis for using a weighted average of the different results given by the different techniques.
Difficulty: 1 Easy
Topic: An Introduction to Ensemble Estimates
Learning Objective: 05-05 Interpret the information provided by ensemble models.
Bloom’s: Remember
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
16) Because different classification techniques will perform better for different data sets, ensemble models consider multiple classification techniques before selecting the best classification technique to use for a particular data set.
Answer: FALSE
Explanation: Ensemble models look for the predominant classification from multiple classification techniques.
Difficulty: 1 Easy
Topic: An Introduction to Ensemble Estimates
Learning Objective: 05-05 Interpret the information provided by ensemble models.
Bloom’s: Remember
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
17) Classification involves identifying common traits in items in order to develop broad classes into which the items may be grouped based on those traits.
Answer: FALSE
Explanation: Classification involves assigning items to prespecified categories, or classes.
Difficulty: 1 Easy
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by classification trees.
Bloom’s: Remember
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
18) The process of assigning items to prespecified categories is known as classification.
Answer: TRUE
Explanation: Classification involves assigning items to prespecified categories, or classes.
Difficulty: 1 Easy
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by classification trees.
Bloom’s: Remember
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
19) The confusion matrix shows the number of observed response variables which are classified correctly.
Answer: TRUE
Explanation: The confusion matrix shows the number of observed response variables that are (or are not) classified correctly.
Difficulty: 2 Medium
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by classification trees.
Bloom’s: Understand
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
20) The confusion matrix shows the number of observed response variables which are inaccurately classified.
Answer: TRUE
Explanation: The confusion matrix shows the number of observed response variables that are (or are not) classified correctly.
Difficulty: 2 Medium
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by classification trees.
Bloom’s: Understand
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
21) Combining the estimates or predictions obtained from different analytics to arrive at an overall result is done by developing a(n) ________.
Answer: E
Explanation: An ensemble estimate combines (e.g., averages) the estimates or predictions obtained from different analytics to arrive at an overall result.
Difficulty: 1 Easy
Topic: An Introduction to Ensemble Estimates
Learning Objective: 05-05 Interpret the information provided by ensemble models.
Bloom’s: Remember
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
22) Which of the following possible response variables is most appropriate to predict using a classification tree?
Answer: E
Explanation: A classification tree is useful for predicting a qualitative, or categorical, response variable.
Difficulty: 1 Easy
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by classification trees.
Bloom’s: Remember
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
23) Which of the following possible response variables is most appropriate to predict using a classification tree?
Answer: B
Explanation: A classification tree is useful for predicting a qualitative, or categorical, response variable.
Difficulty: 1 Easy
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by classification trees.
Bloom’s: Remember
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
24) Which of the following possible response variables is most appropriate to predict using a regression tree?
Answer: B
Explanation: A regression tree is useful for predicting a quantitative response variable.
Difficulty: 1 Easy
Topic: Decision Trees II: Regression Trees
Learning Objective: 05-02 Interpret the information provided by regression trees.
Bloom’s: Remember
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
25) Which of the following possible response variables is most appropriate to predict using a regression tree?
Answer: D
Explanation: A regression tree is useful for predicting a quantitative response variable.
Difficulty: 1 Easy
Topic: Decision Trees II: Regression Trees
Learning Objective: 05-02 Interpret the information provided by regression trees.
Bloom’s: Remember
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
26) Which of the following would you find on a classification tree?
Answer: D
Explanation: A classification tree starts with a trunk and splits into leafs as it identifies combinations of predictor variables that are useful for predicting the class to which an item should be assigned.
Difficulty: 1 Easy
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by classification trees.
Bloom’s: Remember
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
27) Dividing the entire data set into a training data set and a validation training set is a key step in one approach to ________ the data.
Answer: E
Explanation: One approach to avoid overfitting is to divide the entire data set into a training data set, which is used to make splits based on the specified minimum split size/leaf purity termination criterion, and a validation data set, which is used as an additional criterion to decide when to stop making splits.
Difficulty: 1 Easy
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by classification trees.
Bloom’s: Remember
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
28) Unlike a classification tree, a regression tree enables us to predict the value of a ________ response variable.
Answer: A
Explanation: A regression tree is used to predict the value of a quantitative response variable, while a classification tree is used to predict the value of a qualitative, or categorical, variable.
Difficulty: 1 Easy
Topic: Decision Trees II: Regression Trees
Learning Objective: 05-02 Interpret the information provided by regression trees.
Bloom’s: Remember
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
29) The k-nearest neighbors approach can be used to predict
Answer: D
Explanation: The k-nearest neighbors approach can be used to predict either qualitative or quantitative response variables based on predictor variables.
Difficulty: 2 Medium
Topic: k-Nearest Neighbors
Learning Objective: 05-03 Interpret the information provided by k-nearest neighbors.
Bloom’s: Understand
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
30) The naive Bayes’ classification procedure can be used to predict
Answer: A
Explanation: The naive Bayes’ classification procedure uses a “naive” version of Bayes’ Theorem to classify observations into preselected categories.
Difficulty: 2 Medium
Topic: Naive Bayes’ Classification
Learning Objective: 05-04 Interpret the information provided by naive Bayes’ classification.
Bloom’s: Understand
AACSB: Reflective Thinking
Accessibility: Keyboard Navigation
31) Suppose that a bank wishes to predict whether or not an existing holder of its Silver credit card will upgrade, for an annual fee, to its Platinum credit card. To do this, the bank carries out a pilot study that randomly selects 40 of its existing Silver card holders and offers each Silver card holder an upgrade to its Platinum card. Here, the response variable Upgrade equals 1 if the Silver card holder decided to upgrade and 0 otherwise. Moreover, the predictor variable Purchases is last year’s purchases (in thousands of dollars) by the Silver card holder, and the predictor variable PlatProfile equals 1 if the Silver card holder conforms to the bank’s Platinum profile and 0 otherwise. Below is the classification tree they derived from the data collected in the study.
Of these 40 Silver card holders, what is the proportion that did not upgrade?
Answer: B
Explanation: 21/40 = .5250 is the sample proportion that did not upgrade.
Difficulty: 1 Easy
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by classification trees.
Bloom’s: Understand
AACSB: Analytical Thinking
Accessibility: Keyboard Navigation
32) Suppose that a bank wishes to predict whether or not an existing holder of its Silver credit card will upgrade, for an annual fee, to its Platinum credit card. To do this, the bank carries out a pilot study that randomly selects 40 of its existing Silver card holders and offers each Silver card holder an upgrade to its Platinum card. Here, the response variable Upgrade equals 1 if the Silver card holder decided to upgrade and 0 otherwise. Moreover, the predictor variable Purchases is last year’s purchases (in thousands of dollars) by the Silver card holder, and the predictor variable PlatProfile equals 1 if the Silver card holder conforms to the bank’s Platinum profile and 0 otherwise. Below is the classification tree they derived from the data collected in the study.
Based on this classification tree, which of the following Silver card holders would the bank classify as an upgrader (assuming they classify those with an upgrade probability estimate of at least .5 as upgraders)?
Answer: B
Explanation: No Silver card holders with PlatProfile(0) would be classified as upgraders. All Silver card holders with PlatProfile(1) would be classified as upgraders. Simply knowing the Purchases without knowing the PlatProfile would be insufficient to classify the Silver card holder as an upgrader.
Difficulty: 2 Medium
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by classification trees.
Bloom’s: Apply
AACSB: Analytical Thinking
Accessibility: Keyboard Navigation
33) Suppose that a bank wishes to predict whether or not an existing holder of its Silver credit card will upgrade, for an annual fee, to its Platinum credit card. To do this, the bank carries out a pilot study that randomly selects 40 of its existing Silver card holders and offers each Silver card holder an upgrade to its Platinum card. Here, the response variable Upgrade equals 1 if the Silver card holder decided to upgrade and 0 otherwise. Moreover, the predictor variable Purchases is last year’s purchases (in thousands of dollars) by the Silver card holder, and the predictor variable PlatProfile equals 1 if the Silver card holder conforms to the bank’s Platinum profile and 0 otherwise. Below is the classification tree they derived from the data collected in the study.
Based on this classification tree, which of the following Silver card holders would the bank classify as a non-upgrader (assuming they classify those with an upgrade probability estimate of at least .5 as upgraders)?
Answer: E
Explanation: No Silver card holders with PlatProfile(0) would be classified as upgraders. All Silver card holders with PlatProfile(1) would be classified as upgraders. Simply knowing the Purchases without knowing the PlatProfile would be insufficient to classify the Silver card holder as an upgrader.
Difficulty: 2 Medium
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by classification trees.
Bloom’s: Apply
AACSB: Analytical Thinking
Accessibility: Keyboard Navigation
34) Suppose that a bank wishes to predict whether or not an existing holder of its Silver credit card will upgrade, for an annual fee, to its Platinum credit card. To do this, the bank carries out a pilot study that randomly selects 40 of its existing Silver card holders and offers each Silver card holder an upgrade to its Platinum card. Here, the response variable Upgrade equals 1 if the Silver card holder decided to upgrade and 0 otherwise. Moreover, the predictor variable Purchases is last year’s purchases (in thousands of dollars) by the Silver card holder, and the predictor variable PlatProfile equals 1 if the Silver card holder conforms to the bank’s Platinum profile and 0 otherwise. Below is the classification tree they derived from the data collected in the study.
Assume they classify those with an upgrade probability estimate of at least .5 as upgraders. Based on this classification tree, a member of the study sample who made $28,520 in purchases last year and conformed to the bank’s Platinum profile but did not upgrade to the Platinum card would be…
Answer: C
Explanation: A Silver card holder with PlatProfile(1) and Purchases = 28.52 would be classified as an upgrader. However, since the specific Silver card holder in the study did not upgrade, the classification for them would be inaccurate.
Difficulty: 2 Medium
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by classification trees.
Bloom’s: Apply
AACSB: Analytical Thinking
Accessibility: Keyboard Navigation
35) Suppose that a bank wishes to predict whether or not an existing holder of its Silver credit card will upgrade, for an annual fee, to its Platinum credit card. To do this, the bank carries out a pilot study that randomly selects 40 of its existing Silver card holders and offers each Silver card holder an upgrade to its Platinum card. Here, the response variable Upgrade equals 1 if the Silver card holder decided to upgrade and 0 otherwise. Moreover, the predictor variable Purchases is last year’s purchases (in thousands of dollars) by the Silver card holder, and the predictor variable PlatProfile equals 1 if the Silver card holder conforms to the bank’s Platinum profile and 0 otherwise. Below is the classification tree they derived from the data collected in the study.
Assume they classify those with an upgrade probability estimate of at least .5 as upgraders. Based on this classification tree, a member of the study sample who made $50,450 in purchases last year, did not conform to the bank’s Platinum profile, and upgraded to the Platinum card would be…
Answer: D
Explanation: A Silver card holder with PlatProfile(0) and Purchases = 50.45 would be classified as a non-upgrader. However, since the specific Silver card holder in the study did upgrade, the classification for them would be inaccurate.
Difficulty: 2 Medium
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by classification trees.
Bloom’s: Apply
AACSB: Analytical Thinking
Accessibility: Keyboard Navigation
36) An internet service provider (ISP) has randomly selected a sample of 223 observations concerning values of the response variable Churn and several predictor variables based on customer activity during the most recently billed month. Here Churn equals Yes if a customer churned—left the internet service provider for another ISP—and equals No otherwise. The predictor variable MinutesOn is the average daily minutes the customer spent online. EmailSent is the average daily number of emails the customer sent from the email address provided by the ISP. ServCalls is the number of times the customer called for service. Below is part of the classification tree they derived from the data collected in the study.
Of the 223 sampled customers, what is the sample proportion of those who churned?
Answer: A
Explanation: 32/223 = .143 is the proportion of all sampled customers who churned.
Difficulty: 1 Easy
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by classification trees.
Bloom’s: Understand
AACSB: Analytical Thinking
Accessibility: Keyboard Navigation
37) An internet service provider (ISP) has randomly selected a sample of 223 observations concerning values of the response variable Churn and several predictor variables based on customer activity during the most recently billed month. Here Churn equals Yes if a customer churned—left the internet service provider for another ISP—and equals No otherwise. The predictor variable MinutesOn is the average daily minutes the customer spent online. EmailSent is the average daily number of emails the customer sent from the email address provided by the ISP. ServCalls is the number of times the customer called for service. Below is part of the classification tree they derived from the data collected in the study.
Of the sampled customers who spent an average of less than 511 minutes online per day and placed fewer than 3 service calls, what is the sample proportion of those who churned?
Answer: B
Explanation: 15/192 = .078 is the proportion of sampled customers with MinutesOn < 511 and ServCalls < 3 who churned.
Difficulty: 1 Easy
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by classification trees.
Bloom’s: Understand
AACSB: Analytical Thinking
Accessibility: Keyboard Navigation
38) An internet service provider (ISP) has randomly selected a sample of 223 observations concerning values of the response variable Churn and several predictor variables based on customer activity during the most recently billed month. Here Churn equals Yes if a customer churned—left the internet service provider for another ISP—and equals No otherwise. The predictor variable MinutesOn is the average daily minutes the customer spent online. EmailSent is the average daily number of emails the customer sent from the email address provided by the ISP. ServCalls is the number of times the customer called for service. Below is part of the classification tree they derived from the data collected in the study.
Of the sampled customers who spent an average of at least 511 minutes online per day and sent an average of fewer than 7 emails per day from their ISP-provided email address, how many churned?
Answer: A
Explanation:
There are 9 – 2 = 7 sampled customers who have MinutesOn ≥ 511 and EmailSent < 7 and who churned.
Difficulty: 2 Medium
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by classification trees.
Bloom’s: Understand
AACSB: Analytical Thinking
Accessibility: Keyboard Navigation
39) An internet service provider (ISP) has randomly selected a sample of 223 observations concerning values of the response variable Churn and several predictor variables based on customer activity during the most recently billed month. Here Churn equals Yes if a customer churned—left the internet service provider for another ISP—and equals No otherwise. The predictor variable MinutesOn is the average daily minutes the customer spent online. EmailSent is the average daily number of emails the customer sent from the email address provided by the ISP. ServCalls is the number of times the customer called for service. Below is part of the classification tree they derived from the data collected in the study.
Of the sampled customers who spent an average of at least 511 minutes online per day and sent an average of fewer than 7 emails per day from their ISP-provided email address, what is the sample proportion of those who churned?
Answer: D
Explanation:
There are 9 – 2 = 7 sampled customers who have MinutesOn ≥ 511 and EmailSent < 7 and who churned. This represents a proportion of 7/9 = .778.
Difficulty: 2 Medium
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by classification trees.
Bloom’s: Understand
AACSB: Analytical Thinking
Accessibility: Keyboard Navigation
40) An internet service provider (ISP) has randomly selected a sample of 223 observations concerning values of the response variable Churn and several predictor variables based on customer activity during the most recently billed month. Here Churn equals Yes if a customer churned—left the internet service provider for another ISP—and equals No otherwise. The predictor variable MinutesOn is the average daily minutes the customer spent online. EmailSent is the average daily number of emails the customer sent from the email address provided by the ISP. ServCalls is the number of times the customer called for service. Below is part of the classification tree they derived from the data collected in the study.
Of the sampled customers who spent an average of at least 511 minutes online per day, how many sent an average of at least 7 emails per day from their ISP-provided email address?
Answer: D
Explanation: There are 223 – 209 = 14 sampled customers who have MinutesOn ≥ 511. Of those, 14 – 9 = 5 have EmailSent ≥ 7.
Difficulty: 2 Medium
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by classification trees.
Bloom’s: Understand
AACSB: Analytical Thinking
Accessibility: Keyboard Navigation
41) An internet service provider (ISP) has randomly selected a sample of 223 observations concerning values of the response variable Churn and several predictor variables based on customer activity during the most recently billed month. Here Churn equals Yes if a customer churned—left the internet service provider for another ISP—and equals No otherwise. The predictor variable MinutesOn is the average daily minutes the customer spent online. EmailSent is the average daily number of emails the customer sent from the email address provided by the ISP. ServCalls is the number of times the customer called for service. Below is part of the classification tree they derived from the data collected in the study.
Of the sampled customers who spent an average of at least 511 minutes online per day and sent an average of at least 7 emails per day from their ISP-provided email address, how many churned?
Answer: E
Explanation: There are 223 – 209 = 14 sampled customers who have MinutesOn ≥ 511. Of those, 14 – 9 = 5 have EmailSent ≥ 7. There are 32 – 24 = 8 sampled customers who have MinutesOn ≥ 511 and who churned. There are 9 – 2 = 7 sampled customers who have MinutesOn ≥ 511 and EmailSent < 7 and who churned. Thus, there is 8 – 7 = 1 sampled customer who has MinutesOn ≥ 511 and EmailSent ≥ 7 and who churned.
Difficulty: 3 Hard
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by classification trees.
Bloom’s: Understand
AACSB: Analytical Thinking
Accessibility: Keyboard Navigation
42) An internet service provider (ISP) has randomly selected a sample of 223 observations concerning values of the response variable Churn and several predictor variables based on customer activity during the most recently billed month. Here Churn equals Yes if a customer churned—left the internet service provider for another ISP—and equals No otherwise. The predictor variable MinutesOn is the average daily minutes the customer spent online. EmailSent is the average daily number of emails the customer sent from the email address provided by the ISP. ServCalls is the number of times the customer called for service. Below is part of the classification tree they derived from the data collected in the study.
Of the sampled customers who spent an average of at least 511 minutes online per day and sent an average of at least 7 emails per day from their ISP-provided email address, what is the sample proportion of those who churned?
Answer: C
Explanation: There are 223 – 209 = 14 sampled customers who have MinutesOn ≥ 511. Of those, 14 – 9 = 5 have EmailSent ≥ 7. There are 32 – 24 = 8 sampled customers who have MinutesOn ≥ 511 and who churned. There are 9 – 2 = 7 sampled customers who have MinutesOn ≥ 511 and EmailSent < 7 and who churned. Thus, there is 8 – 7 = 1 sampled customer who has MinutesOn ≥ 511 and EmailSent ≥ 7 and who churned. This represents a proportion of 1/5 = .200.
Difficulty: 3 Hard
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by classification trees.
Bloom’s: Understand
AACSB: Analytical Thinking
Accessibility: Keyboard Navigation
43) An internet service provider (ISP) has randomly selected a sample of 223 observations concerning values of the response variable Churn and several predictor variables based on customer activity during the most recently billed month. Here Churn equals Yes if a customer churned—left the internet service provider for another ISP—and equals No otherwise. The predictor variable MinutesOn is the average daily minutes the customer spent online. EmailSent is the average daily number of emails the customer sent from the email address provided by the ISP. ServCalls is the number of times the customer called for service. Below is part of the classification tree they derived from the data collected in the study.
Of the 223 sampled customers, what is the sample proportion of those who did not churn?
Answer: E
Explanation: 191/223 = .857 is the proportion of all sampled customers who did not churn.
Difficulty: 1 Easy
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by classification trees.
Bloom’s: Understand
AACSB: Analytical Thinking
Accessibility: Keyboard Navigation
44) An internet service provider (ISP) has randomly selected a sample of 223 observations concerning values of the response variable Churn and several predictor variables based on customer activity during the most recently billed month. Here Churn equals Yes if a customer churned—left the internet service provider for another ISP—and equals No otherwise. The predictor variable MinutesOn is the average daily minutes the customer spent online. EmailSent is the average daily number of emails the customer sent from the email address provided by the ISP. ServCalls is the number of times the customer called for service. Below is part of the classification tree they derived from the data collected in the study.
Of the sampled customers who spent an average of at least 511 minutes online per day and sent an average of less than 7 emails per day from their ISP-provided email address, what is the sample proportion of those who did not churn?
Answer: E
Explanation: 2/9 = .222 is the proportion of sampled customers with MinutesOn ≥ 511 and EmailSent < 7 who did not churn.
Difficulty: 1 Easy
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by classification trees.
Bloom’s: Understand
AACSB: Analytical Thinking
Accessibility: Keyboard Navigation
45) An internet service provider (ISP) has randomly selected a sample of 223 observations concerning values of the response variable Churn and several predictor variables based on customer activity during the most recently billed month. Here Churn equals Yes if a customer churned—left the internet service provider for another ISP—and equals No otherwise. The predictor variable MinutesOn is the average daily minutes the customer spent online. EmailSent is the average daily number of emails the customer sent from the email address provided by the ISP. ServCalls is the number of times the customer called for service. Below is part of the classification tree they derived from the data collected in the study.
Of the sampled customers who spent an average of less than 511 minutes online per day and placed at least 3 service calls, how many did not churn?
Answer: B
Explanation:
There are 209 – 192 = 17 sampled customers who have MinutesOn < 511 ServCalls ≥ 3. Of those, 17 – 9 = 8 did not churn.
Difficulty: 2 Medium
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by classification trees.
Bloom’s: Understand
AACSB: Analytical Thinking
Accessibility: Keyboard Navigation
46) An internet service provider (ISP) has randomly selected a sample of 223 observations concerning values of the response variable Churn and several predictor variables based on customer activity during the most recently billed month. Here Churn equals Yes if a customer churned—left the internet service provider for another ISP—and equals No otherwise. The predictor variable MinutesOn is the average daily minutes the customer spent online. EmailSent is the average daily number of emails the customer sent from the email address provided by the ISP. ServCalls is the number of times the customer called for service. Below is part of the classification tree they derived from the data collected in the study.
Of the sampled customers who spent an average of less than 511 minutes online per day and placed at least 3 service calls, what is the sample proportion of those who did not churn?
Answer: B
Explanation:
There are 209 – 192 = 17 sampled customers who have MinutesOn < 511 ServCalls ≥ 3. Of those, 17 – 9 = 8 did not churn. This represents a proportion of 8/17 = .471.
Difficulty: 2 Medium
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by classification trees.
Bloom’s: Understand
AACSB: Analytical Thinking
Accessibility: Keyboard Navigation
47) An internet service provider (ISP) has randomly selected a sample of 223 observations concerning values of the response variable Churn and several predictor variables based on customer activity during the most recently billed month. Here Churn equals Yes if a customer churned—left the internet service provider for another ISP—and equals No otherwise. The predictor variable MinutesOn is the average daily minutes the customer spent online. EmailSent is the average daily number of emails the customer sent from the email address provided by the ISP. ServCalls is the number of times the customer called for service. Below is part of the classification tree they derived from the data collected in the study.
Of the sampled customers who spent an average of at least 511 minutes online per day, how many did not churn?
Answer: D
Explanation: There are 191 of the 223 customers sampled who did not churn. Of those, 185 had MinutesOn < 511. Therefore, there are 191 – 185 = 6 customers who had MinutesOn ≥ 511 and who did not churn.
Difficulty: 2 Medium
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by classification trees.
Bloom’s: Understand
AACSB: Analytical Thinking
Accessibility: Keyboard Navigation
48) An internet service provider (ISP) has randomly selected a sample of 223 observations concerning values of the response variable Churn and several predictor variables based on customer activity during the most recently billed month. Here Churn equals Yes if a customer churned—left the internet service provider for another ISP—and equals No otherwise. The predictor variable MinutesOn is the average daily minutes the customer spent online. EmailSent is the average daily number of emails the customer sent from the email address provided by the ISP. ServCalls is the number of times the customer called for service. Below is part of the classification tree they derived from the data collected in the study.
Of the sampled customers who spent an average of at least 511 minutes online per day and sent an average of at least 7 emails per day from their ISP-provided email address, how many did not churn?
Answer: A
Explanation:
There are 223 – 209 = 14 sampled customers who have MinutesOn ≥ 511. Of those, 14 – 9 = 5 have EmailSent ≥ 7. There are 191 – 185 = 6 sampled customers who have MinutesOn ≥ 511 and who did not churn. There are 2 sampled customers who have MinutesOn ≥ 511 and EmailSent < 7 and who did not churn. Thus, there are 6 – 2 = 4 sampled customers who have MinutesOn ≥ 511 and EmailSent ≥ 7 and who did not churn. This represents a proportion of 4/5 = .800.
Difficulty: 3 Hard
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by classification trees.
Bloom’s: Understand
AACSB: Analytical Thinking
Accessibility: Keyboard Navigation
49) An internet service provider (ISP) has randomly selected a sample of 223 observations concerning values of the response variable Churn and several predictor variables based on customer activity during the most recently billed month. Here Churn equals Yes if a customer churned—left the internet service provider for another ISP—and equals No otherwise. The predictor variable MinutesOn is the average daily minutes the customer spent online. EmailSent is the average daily number of emails the customer sent from the email address provided by the ISP. ServCalls is the number of times the customer called for service. Below is part of the classification tree they derived from the data collected in the study.
Of the sampled customers who spent an average of at least 511 minutes online per day and sent an average of at least 7 emails per day from their ISP-provided email address, what is the sample proportion of those who did not churn?
Answer: A
Explanation:
There are 223 – 209 = 14 sampled customers who have MinutesOn ≥ 511. Of those, 14 – 9 = 5 have EmailSent ≥ 7. There are 191 – 185 = 6 sampled customers who have MinutesOn ≥ 511 and who did not churn. There are 2 sampled customers who have MinutesOn ≥ 511 and EmailSent < 7 and who did not churn. Thus, there are 6 – 2 = 4 sampled customers who have MinutesOn ≥ 511 and EmailSent ≥ 7 and who did not churn. This represents a proportion of 4/5 = .800.
Difficulty: 3 Hard
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by classification trees.
Bloom’s: Understand
AACSB: Analytical Thinking
Accessibility: Keyboard Navigation
50) An internet service provider (ISP) has randomly selected a sample of 223 observations concerning values of the response variable Churn and several predictor variables based on customer activity during the most recently billed month. Here Churn equals Yes if a customer churned—left the internet service provider for another ISP—and equals No otherwise. The predictor variable MinutesOn is the average daily minutes the customer spent online. EmailSent is the average daily number of emails the customer sent from the email address provided by the ISP. ServCalls is the number of times the customer called for service. Below is part of the classification tree they derived from the data collected in the study.
Assume they classify those with a churn probability estimate (based on the sample proportion) of at least .5 as churners. Based on this classification tree, a member of the study sample who spent an average of 327 minutes online per day, sent an average of 4 emails per day from their ISP-provided email address, placed no service calls, and churned would be
Answer: B
Explanation:
This customer would have MinutesOn < 511 and ServCalls < 3. (Their average number of emails would not be relevant in this case.) The 192 customers with MinutesOn < 511 and ServCalls < 3 would be classified as non-churners because only 15 were churners for a churner proportion of 15/192 = .078. However, since the specific customer being considered did churn, the non-churner classification for them would be inaccurate.
Difficulty: 3 Hard
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by classification trees.
Bloom’s: Apply
AACSB: Analytical Thinking
Accessibility: Keyboard Navigation
51) An automobile finance company analyzed a sample of recent automobile loans to try to determine key factors in identifying borrowers who would be likely to default on their auto loan. The response variable Default equals 1 if the borrower defaulted during the term of the loan and 0 otherwise. The predictor variable AutoDebt% was the ratio (expressed as a percent) of the required loan payments to the borrower’s take-home income at the time of purchase. JobTime was the number of years the borrower had worked at their current job at the time of purchase. CredScore was the borrower’s credit score at the time of purchase. Below is part of the classification tree the finance company derived from the data collected in the study. Assume they classify those with a default probability estimate of at least .5 as Defaulters.
Based on this classification tree, a member of the study sample who had a credit score of 523, been at their current job for 1 year, took out a loan with payments equaling 17% of their income, and defaulted would be
Answer: C
Explanation: This customer would have CredScore < 724, JobTime < 4, and AutoDebt% ≥ 13. They would be classified as a Defaulter which is accurate since they actually defaulted.
Difficulty: 2 Medium
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by classification trees.
Bloom’s: Understand
AACSB: Analytical Thinking
Accessibility: Keyboard Navigation
52) An automobile finance company analyzed a sample of recent automobile loans to try to determine key factors in identifying borrowers who would be likely to default on their auto loan. The response variable Default equals 1 if the borrower defaulted during the term of the loan and 0 otherwise. The predictor variable AutoDebt% was the ratio (expressed as a percent) of the required loan payments to the borrower’s take-home income at the time of purchase. JobTime was the number of years the borrower had worked at their current job at the time of purchase. CredScore was the borrower’s credit score at the time of purchase. Below is part of the classification tree the finance company derived from the data collected in the study. Assume they classify those with a default probability estimate of at least .5 as Defaulters.
Based on this classification tree, a member of the study sample who had a credit score of 724, been at their current job for 2 years, took out a loan with payments equaling 20% of their income, and did not default would be
Answer: D
Explanation: This customer would have CredScore ≥ 724, AutoDebt% ≥ 19, and JobTime ≥ 2. They would be classified as a non-Defaulter which is accurate since they did not default.
Difficulty: 2 Medium
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by classification trees.
Bloom’s: Understand
AACSB: Analytical Thinking
Accessibility: Keyboard Navigation
53) An automobile finance company analyzed a sample of recent automobile loans to try to determine key factors in identifying borrowers who would be likely to default on their auto loan. The response variable Default equals 1 if the borrower defaulted during the term of the loan and 0 otherwise. The predictor variable AutoDebt% was the ratio (expressed as a percent) of the required loan payments to the borrower’s take-home income at the time of purchase. JobTime was the number of years the borrower had worked at their current job at the time of purchase. CredScore was the borrower’s credit score at the time of purchase. Below is part of the classification tree the finance company derived from the data collected in the study. Assume they classify those with a default probability estimate of at least .5 as Defaulters.
Based on this classification tree, a member of the study sample who had a credit score of 667, been at their current job for 3 years, took out a loan with payments equaling 13% of their income, and did not default would be
Answer: A
Explanation: This customer would have CredScore < 724, JobTime < 4, and AutoDebt% ≥ 13. They would be classified as a Defaulter which is inaccurate since they did not default.
Difficulty: 2 Medium
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by classification trees.
Bloom’s: Understand
AACSB: Analytical Thinking
Accessibility: Keyboard Navigation
54) An automobile finance company analyzed a sample of recent automobile loans to try to determine key factors in identifying borrowers who would be likely to default on their auto loan. The response variable Default equals 1 if the borrower defaulted during the term of the loan and 0 otherwise. The predictor variable AutoDebt% was the ratio (expressed as a percent) of the required loan payments to the borrower’s take-home income at the time of purchase. JobTime was the number of years the borrower had worked at their current job at the time of purchase. CredScore was the borrower’s credit score at the time of purchase. Below is part of the classification tree the finance company derived from the data collected in the study. Assume they classify those with a default probability estimate of at least .5 as Defaulters.
Based on this classification tree, a member of the study sample who had a credit score of 819, been at their current job for 3 years, took out a loan with payments equaling 15% of their income, and did not default would be
Answer: D
Explanation: This customer would have CredScore ≥ 724 and AutoDebt% < 19. Their JobTime of 3 would be irrelevant in this case. They would be classified as a non-Defaulter which is accurate since they did not default.
Difficulty: 2 Medium
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by classification trees.
Bloom’s: Understand
AACSB: Analytical Thinking
Accessibility: Keyboard Navigation
55) An automobile finance company analyzed a sample of recent automobile loans to try to determine key factors in identifying borrowers who would be likely to default on their auto loan. The response variable Default equals 1 if the borrower defaulted during the term of the loan and 0 otherwise. The predictor variable AutoDebt% was the ratio (expressed as a percent) of the required loan payments to the borrower’s take-home income at the time of purchase. JobTime was the number of years the borrower had worked at their current job at the time of purchase. CredScore was the borrower’s credit score at the time of purchase. Below is part of the classification tree the finance company derived from the data collected in the study. Assume they classify those with a default probability estimate of at least .5 as Defaulters.
Based on this classification tree, a member of the study sample who had a credit score of 537, been at their current job for 12 years, took out a loan with payments equaling 16% of their income, and defaulted would be
Answer: B
Explanation:
This customer would have CredScore < 724 and JobTime ≥ 4. Their AutoDebt% of 16 would be irrelevant in this case. They would be classified as a non-Defaulter which is inaccurate since they actually defaulted.
Difficulty: 2 Medium
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by classification trees.
Bloom’s: Understand
AACSB: Analytical Thinking
Accessibility: Keyboard Navigation
56) An automobile finance company analyzed a sample of recent automobile loans to try to determine key factors in identifying borrowers who would be likely to default on their auto loan. The response variable Default equals 1 if the borrower defaulted during the term of the loan and 0 otherwise. The predictor variable AutoDebt% was the ratio (expressed as a percent) of the required loan payments to the borrower’s take-home income at the time of purchase. JobTime was the number of years the borrower had worked at their current job at the time of purchase. CredScore was the borrower’s credit score at the time of purchase. Below is part of the classification tree the finance company derived from the data collected in the study. Assume they classify those with a default probability estimate of at least .5 as Defaulters.
Based on this classification tree, a member of the study sample who had a credit score of 698, had just started a new job, took out a loan with payments equaling 7% of their income, and defaulted would be
Answer: B
Explanation: This customer would have CredScore < 724, JobTime < 4, and AutoDebt% < 13. They would be classified as a non-Defaulter which is inaccurate since they actually defaulted.
Difficulty: 2 Medium
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by classification trees.
Bloom’s: Understand
AACSB: Analytical Thinking
Accessibility: Keyboard Navigation
57) An automobile finance company analyzed a sample of recent automobile loans to try to determine key factors in identifying borrowers who would be likely to default on their auto loan. The response variable Default equals 1 if the borrower defaulted during the term of the loan and 0 otherwise. The predictor variable AutoDebt% was the ratio (expressed as a percent) of the required loan payments to the borrower’s take-home income at the time of purchase. JobTime was the number of years the borrower had worked at their current job at the time of purchase. CredScore was the borrower’s credit score at the time of purchase. Below is part of the classification tree the finance company derived from the data collected in the study. Assume they classify those with a default probability estimate of at least .5 as Defaulters.
Based on this classification tree, a member of the study sample who had a credit score of 774, just started their current job, took out a loan with payments equaling 19% of their income, and did not default would be
Answer: A
Explanation: This customer would have CredScore ≥ 724, AutoDebt% ≥ 19, and JobTime < 2. They would be classified as a Defaulter which is inaccurate since they did not default.
Difficulty: 2 Medium
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by classification trees.
Bloom’s: Understand
AACSB: Analytical Thinking
Accessibility: Keyboard Navigation
58) An automobile finance company analyzed a sample of recent automobile loans to try to determine key factors in identifying borrowers who would be likely to default on their auto loan. The response variable Default equals 1 if the borrower defaulted during the term of the loan and 0 otherwise. The predictor variable AutoDebt% was the ratio (expressed as a percent) of the required loan payments to the borrower’s take-home income at the time of purchase. JobTime was the number of years the borrower had worked at their current job at the time of purchase. CredScore was the borrower’s credit score at the time of purchase. Below is part of the classification tree the finance company derived from the data collected in the study. Assume they classify those with a default probability estimate of at least .5 as Defaulters.
Based on this classification tree, a member of the study sample who had a credit score of 802, been at their current job for 1 year, took out a loan with payments equaling 19% of their income, and defaulted would be
Answer: C
Explanation: This customer would have CredScore ≥ 724, AutoDebt% ≥ 19, and JobTime < 2. They would be classified as a Defaulter which is accurate since they actually defaulted.
Difficulty: 2 Medium
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by classification trees.
Bloom’s: Understand
AACSB: Analytical Thinking
Accessibility: Keyboard Navigation
59) An automobile finance company analyzed a sample of recent automobile loans to try to determine key factors in identifying borrowers who would be likely to default on their auto loan. The response variable Default equals 1 if the borrower defaulted during the term of the loan and 0 otherwise. The predictor variable AutoDebt% was the ratio (expressed as a percent) of the required loan payments to the borrower’s take-home income at the time of purchase. JobTime was the number of years the borrower had worked at their current job at the time of purchase. CredScore was the borrower’s credit score at the time of purchase. Below is part of the classification tree the finance company derived from the data collected in the study. Assume they classify those with a default probability estimate of at least .5 as Defaulters.
Based on this classification tree, a member of the study sample who had a credit score of 423, been at their current job for 4 years, took out a loan with payments equaling 22% of their income, and did not default would be
Answer: D
Explanation: This customer would have CredScore < 724 and JobTime ≥ 4. Their AutoDebt% of 22 would be irrelevant in this case. They would be classified as a non-Defaulter which is accurate since they did not default.
Difficulty: 2 Medium
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by classification trees.
Bloom’s: Understand
AACSB: Analytical Thinking
Accessibility: Keyboard Navigation
60) An automobile finance company analyzed a sample of recent automobile loans to try to determine key factors in identifying borrowers who would be likely to default on their auto loan. The response variable Default equals 1 if the borrower defaulted during the term of the loan and 0 otherwise. The predictor variable AutoDebt% was the ratio (expressed as a percent) of the required loan payments to the borrower’s take-home income at the time of purchase. JobTime was the number of years the borrower had worked at their current job at the time of purchase. CredScore was the borrower’s credit score at the time of purchase. Below is part of the classification tree the finance company derived from the data collected in the study. Assume they classify those with a default probability estimate of at least .5 as Defaulters.
Based on this classification tree, a member of the study sample who had a credit score of 786, just started their current job, took out a loan with payments equaling 9% of their income, and defaulted would be
Answer: B
Explanation: This customer would have CredScore ≥ 724 and AutoDebt% < 19. Their JobTime of 0 would be irrelevant in this case. They would be classified as a non-Defaulter which is inaccurate since they actually defaulted.
Difficulty: 2 Medium
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by classification trees.
Bloom’s: Understand
AACSB: Analytical Thinking
Accessibility: Keyboard Navigation
61) An automobile finance company analyzed a sample of recent automobile loans to try to determine key factors in identifying borrowers who would be likely to default on their auto loan. The response variable Default equals 1 if the borrower defaulted during the term of the loan and 0 otherwise. The predictor variable AutoDebt% was the ratio (expressed as a percent) of the required loan payments to the borrower’s take-home income at the time of purchase. JobTime was the number of years the borrower had worked at their current job at the time of purchase. CredScore was the borrower’s credit score at the time of purchase. Below is part of the classification tree the finance company derived from the data collected in the study. Assume they classify those with a default probability estimate of at least .5 as Defaulters.
A potential borrower with a credit score of 523 who has been at their current job for 1 year is applying for a loan with payments equaling 17% of their income. Based on this classification tree, the best estimate of the probability that this loan applicant would default would be
Answer: D
Explanation: This customer would have CredScore < 724, JobTime < 4, and AutoDebt% ≥ 13.
Difficulty: 2 Medium
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by a classification tree.
Bloom’s: Apply
AACSB: Analytical Thinking
Accessibility: Keyboard Navigation
62) An automobile finance company analyzed a sample of recent automobile loans to try to determine key factors in identifying borrowers who would be likely to default on their auto loan. The response variable Default equals 1 if the borrower defaulted during the term of the loan and 0 otherwise. The predictor variable AutoDebt% was the ratio (expressed as a percent) of the required loan payments to the borrower’s take-home income at the time of purchase. JobTime was the number of years the borrower had worked at their current job at the time of purchase. CredScore was the borrower’s credit score at the time of purchase. Below is part of the classification tree the finance company derived from the data collected in the study. Assume they classify those with a default probability estimate of at least .5 as Defaulters.
A potential borrower with a credit score of 743 who has been at their current job for 3 years is applying for a loan with payments equaling 12% of their income. Based on this classification tree, the best estimate of the probability that this loan applicant would default would be
Answer: B
Explanation: This customer would have CredScore ≥ 724 and AutoDebt% < 19. Their JobTime of 3 would be irrelevant in this case.
Difficulty: 2 Medium
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by a classification tree.
Bloom’s: Apply
AACSB: Analytical Thinking
Accessibility: Keyboard Navigation
63) An automobile finance company analyzed a sample of recent automobile loans to try to determine key factors in identifying borrowers who would be likely to default on their auto loan. The response variable Default equals 1 if the borrower defaulted during the term of the loan and 0 otherwise. The predictor variable AutoDebt% was the ratio (expressed as a percent) of the required loan payments to the borrower’s take-home income at the time of purchase. JobTime was the number of years the borrower had worked at their current job at the time of purchase. CredScore was the borrower’s credit score at the time of purchase. Below is part of the classification tree the finance company derived from the data collected in the study. Assume they classify those with a default probability estimate of at least .5 as Defaulters.
A potential borrower with a credit score of 724 who has been at their current job for 6 years is applying for a loan with payments equaling 21% of their income. Based on this classification tree, the best estimate of the probability that this loan applicant would default would be
Answer: B
Explanation: This customer would have CredScore ≥ 724, AutoDebt% ≥ 19, and JobTime ≥ 2.
Difficulty: 2 Medium
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by a classification tree.
Bloom’s: Apply
AACSB: Analytical Thinking
Accessibility: Keyboard Navigation
64) An automobile finance company analyzed a sample of recent automobile loans to try to determine key factors in identifying borrowers who would be likely to default on their auto loan. The response variable Default equals 1 if the borrower defaulted during the term of the loan and 0 otherwise. The predictor variable AutoDebt% was the ratio (expressed as a percent) of the required loan payments to the borrower’s take-home income at the time of purchase. JobTime was the number of years the borrower had worked at their current job at the time of purchase. CredScore was the borrower’s credit score at the time of purchase. Below is part of the classification tree the finance company derived from the data collected in the study. Assume they classify those with a default probability estimate of at least .5 as Defaulters.
A potential borrower with a credit score of 752 who just started their current job is applying for a loan with payments equaling 19% of their income. Based on this classification tree, the best estimate of the probability that this loan applicant would default would be
Answer: D
Explanation: This customer would have CredScore ≥ 724, AutoDebt% ≥ 19, and JobTime < 2.
Difficulty: 2 Medium
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by a classification tree.
Bloom’s: Apply
AACSB: Analytical Thinking
Accessibility: Keyboard Navigation
65) An automobile finance company analyzed a sample of recent automobile loans to try to determine key factors in identifying borrowers who would be likely to default on their auto loan. The response variable Default equals 1 if the borrower defaulted during the term of the loan and 0 otherwise. The predictor variable AutoDebt% was the ratio (expressed as a percent) of the required loan payments to the borrower’s take-home income at the time of purchase. JobTime was the number of years the borrower had worked at their current job at the time of purchase. CredScore was the borrower’s credit score at the time of purchase. Below is part of the classification tree the finance company derived from the data collected in the study. Assume they classify those with a default probability estimate of at least .5 as Defaulters.
A potential borrower with a credit score of 668 who has just started their current job is applying for a loan with payments equaling 7% of their income. Based on this classification tree, the best estimate of the probability that this loan applicant would default would be ________.
Answer: C
Explanation: This customer would have CredScore < 724, JobTime < 4, and AutoDebt% < 13.
Difficulty: 2 Medium
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by a classification tree.
Bloom’s: Apply
AACSB: Analytical Thinking
Accessibility: Keyboard Navigation
66) An automobile finance company analyzed a sample of recent automobile loans to try to determine key factors in identifying borrowers who would be likely to default on their auto loan. The response variable Default equals 1 if the borrower defaulted during the term of the loan and 0 otherwise. The predictor variable AutoDebt% was the ratio (expressed as a percent) of the required loan payments to the borrower’s take-home income at the time of purchase. JobTime was the number of years the borrower had worked at their current job at the time of purchase. CredScore was the borrower’s credit score at the time of purchase. Below is part of the classification tree the finance company derived from the data collected in the study. Assume they classify those with a default probability estimate of at least .5 as Defaulters.
A potential borrower with a credit score of 723 who has been at their current job for 7 years is applying for a loan with payments equaling 11% of their income. Based on this classification tree, the best estimate of the probability that this loan applicant would default would be ________.
Answer: C
Explanation: This customer would have CredScore < 724 and JobTime ≥ 4. Their AutoDebt% of 11 would be irrelevant in this case.
Difficulty: 2 Medium
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by a classification tree.
Bloom’s: Apply
AACSB: Analytical Thinking
Accessibility: Keyboard Navigation
67) An automobile finance company analyzed a sample of recent automobile loans to try to determine key factors in identifying borrowers who would be likely to default on their auto loan. The response variable Default equals 1 if the borrower defaulted during the term of the loan and 0 otherwise. The predictor variable AutoDebt% was the ratio (expressed as a percent) of the required loan payments to the borrower’s take-home income at the time of purchase. JobTime was the number of years the borrower had worked at their current job at the time of purchase. CredScore was the borrower’s credit score at the time of purchase. Below is part of the classification tree the finance company derived from the data collected in the study. Assume they classify those with a default probability estimate of at least .5 as Defaulters.
A potential borrower with a credit score of 717 who has been at their current job for 3 years is considering applying for a loan. They do not yet have a particular loan amount in mind. Based on this classification tree, the best estimate that this loan applicant would default would be ________.
Answer: C
Explanation: This customer would have CredScore < 724 and JobTime < 4. Since the loan amount (and therefore the percentage of their income required for loan payments) is unknown, their estimated probability of default would be found before the split based on AutoDebt%.
Difficulty: 3 Hard
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by a classification tree.
Bloom’s: Apply
AACSB: Analytical Thinking
Accessibility: Keyboard Navigation
68) An automobile finance company analyzed a sample of recent automobile loans to try to determine key factors in identifying borrowers who would be likely to default on their auto loan. The response variable Default equals 1 if the borrower defaulted during the term of the loan and 0 otherwise. The predictor variable AutoDebt% was the ratio (expressed as a percent) of the required loan payments to the borrower’s take-home income at the time of purchase. JobTime was the number of years the borrower had worked at their current job at the time of purchase. CredScore was the borrower’s credit score at the time of purchase. Below is part of the classification tree the finance company derived from the data collected in the study. Assume they classify those with a default probability estimate of at least .5 as Defaulters.
A potential borrower who has just started their current job would like to apply for a loan with payments equaling 17% of their income. To be approved for the loan they would need to be classified as a non-Defaulter. Of the following credit scores, which is the lowest this potential borrower could have to be approved for the loan?
Answer: B
Explanation: This customer would have JobTime < 4 and AutoDebt% ≥ 13, so there is no credit score less than 724 that would allow them to be classified as a non-Defaulter. Since they would have AutoDebt < 19, they would be classified as a non-Defaulter with a credit score of at least 724 regardless of how long they have been at their current job.
Difficulty: 3 Hard
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by a classification tree.
Bloom’s: Apply
AACSB: Analytical Thinking
Accessibility: Keyboard Navigation
69) An automobile finance company analyzed a sample of recent automobile loans to try to determine key factors in identifying borrowers who would be likely to default on their auto loan. The response variable Default equals 1 if the borrower defaulted during the term of the loan and 0 otherwise. The predictor variable AutoDebt% was the ratio (expressed as a percent) of the required loan payments to the borrower’s take-home income at the time of purchase. JobTime was the number of years the borrower had worked at their current job at the time of purchase. CredScore was the borrower’s credit score at the time of purchase. Below is part of the classification tree the finance company derived from the data collected in the study. Assume they classify those with a default probability estimate of at least .5 as Defaulters.
A potential borrower who has been at their current job for 16 years would like to apply for a loan. To be approved for the loan they would need to be classified as a non-Defaulter. Of the following credit scores, which is the lowest this potential borrower could have to be approved for the loan?
Answer: A
Explanation: This customer would have JobTime ≥ 4. They would be classified as a non-Defaulter with any credit score, regardless of the percentage of income required for payments.
Difficulty: 3 Hard
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by a classification tree.
Bloom’s: Apply
AACSB: Analytical Thinking
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70) An automobile finance company analyzed a sample of recent automobile loans to try to determine key factors in identifying borrowers who would be likely to default on their auto loan. The response variable Default equals 1 if the borrower defaulted during the term of the loan and 0 otherwise. The predictor variable AutoDebt% was the ratio (expressed as a percent) of the required loan payments to the borrower’s take-home income at the time of purchase. JobTime was the number of years the borrower had worked at their current job at the time of purchase. CredScore was the borrower’s credit score at the time of purchase. Below is part of the classification tree the finance company derived from the data collected in the study. Assume they classify those with a default probability estimate of at least .5 as Defaulters.
A potential borrower who has been at their current job for 1 year would like to apply for a loan with payments equaling 21% of their income. To be approved for the loan they would need to be classified as a non-Defaulter. Of the following credit scores, which is the lowest this potential borrower could have to be approved for the loan?
Answer: D
Explanation: This customer would have JobTime < 2 and AutoDebt% ≥ 19. They would be classified as a Defaulter with any credit score.
Difficulty: 3 Hard
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by a classification tree.
Bloom’s: Apply
AACSB: Analytical Thinking
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71) An automobile finance company analyzed a sample of recent automobile loans to try to determine key factors in identifying borrowers who would be likely to default on their auto loan. The response variable Default equals 1 if the borrower defaulted during the term of the loan and 0 otherwise. The predictor variable AutoDebt% was the ratio (expressed as a percent) of the required loan payments to the borrower’s take-home income at the time of purchase. JobTime was the number of years the borrower had worked at their current job at the time of purchase. CredScore was the borrower’s credit score at the time of purchase. Below is part of the classification tree the finance company derived from the data collected in the study. Assume they classify those with a default probability estimate of at least .5 as Defaulters.
A potential borrower with a credit score of 600 who has just started their current job with a monthly salary of $5,000 would like to apply for a loan. To be approved for the loan they would need to be classified as a non-Defaulter. Of the following monthly payments, which is the highest this loan could have to be approved for this potential borrower?
Answer: A
Explanation: This customer would have CredScore < 724 and JobTime < 4. To be classified as a non-Defaulter they would need AutoDebt% < 13. Therefore, the maximum monthly payment they could have must be less than $5,000 × 13% = $650.
Difficulty: 3 Hard
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by a classification tree.
Bloom’s: Apply
AACSB: Analytical Thinking
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72) An automobile finance company analyzed a sample of recent automobile loans to try to determine key factors in identifying borrowers who would be likely to default on their auto loan. The response variable Default equals 1 if the borrower defaulted during the term of the loan and 0 otherwise. The predictor variable AutoDebt% was the ratio (expressed as a percent) of the required loan payments to the borrower’s take-home income at the time of purchase. JobTime was the number of years the borrower had worked at their current job at the time of purchase. CredScore was the borrower’s credit score at the time of purchase. Below is part of the classification tree the finance company derived from the data collected in the study. Assume they classify those with a default probability estimate of at least .5 as Defaulters.
A potential borrower with a credit score of 724 who has been at their current job for 3 years and has a monthly salary of $6,000 would like to apply for a loan. To be approved for the loan they would need to be classified as a non-Defaulter. Of the following monthly payments, which is the highest this loan could have to be approved for this potential borrower?
Answer: C
Explanation: This customer would have CredScore ≥ 724 and JobTime ≥ 2. They would be classified as a non-Defaulter even with AutoDebt% ≥ 19. Therefore, the maximum monthly payment they could have would be unlimited.
Difficulty: 3 Hard
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by a classification tree.
Bloom’s: Apply
AACSB: Analytical Thinking
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73) An automobile finance company analyzed a sample of recent automobile loans to try to determine key factors in identifying borrowers who would be likely to default on their auto loan. The response variable Default equals 1 if the borrower defaulted during the term of the loan and 0 otherwise. The predictor variable AutoDebt% was the ratio (expressed as a percent) of the required loan payments to the borrower’s take-home income at the time of purchase. JobTime was the number of years the borrower had worked at their current job at the time of purchase. CredScore was the borrower’s credit score at the time of purchase. Below is part of the classification tree the finance company derived from the data collected in the study. Assume they classify those with a default probability estimate of at least .5 as Defaulters.
A potential borrower with a credit score of 792 who has been at their current job for 1 year and has a monthly income of $3,000 would like to apply for a loan. To be approved for the loan they would need to be classified as a non-Defaulter. Of the following monthly payments, which is the highest this loan could have to be approved for this potential borrower?
Answer: B
Explanation: This customer would have CredScore ≥ 724 and JobTime < 2. To be classified as a non-Defaulter they would need AutoDebt% < 19. Therefore, the maximum monthly payment they could have must be less than $3,000 × 19% = $570.
Difficulty: 3 Hard
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by a classification tree.
Bloom’s: Apply
AACSB: Analytical Thinking
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74) An automobile finance company analyzed a sample of recent automobile loans to try to determine key factors in identifying borrowers who would be likely to default on their auto loan. The response variable Default equals 1 if the borrower defaulted during the term of the loan and 0 otherwise. The predictor variable AutoDebt% was the ratio (expressed as a percent) of the required loan payments to the borrower’s take-home income at the time of purchase. JobTime was the number of years the borrower had worked at their current job at the time of purchase. CredScore was the borrower’s credit score at the time of purchase. Below is part of the classification tree the finance company derived from the data collected in the study. Assume they classify those with a default probability estimate of at least .5 as Defaulters.
A potential borrower with a credit score of 812 and a monthly income of $4,000 would like to apply for a loan. To be approved for the loan they would need to be classified as a non-Defaulter. Of the following monthly payments, which is the highest this loan could have to be approved for this potential borrower?
Answer: E
Explanation: This customer would have CredScore ≥ 724. They could be classified as a non-Defaulter with AutoDebt% ≥ 19 if they have JobTime ≥ 2. If they have JobTime < 2, they would be limited to AutoDebt% < 19. Therefore, without knowing JobTime we cannot determine the maximum monthly payment.
Difficulty: 3 Hard
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by a classification tree.
Bloom’s: Apply
AACSB: Analytical Thinking
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75) An automobile finance company analyzed a sample of recent automobile loans to try to determine key factors in identifying borrowers who would be likely to default on their auto loan. The response variable Default equals 1 if the borrower defaulted during the term of the loan and 0 otherwise. The predictor variable AutoDebt% was the ratio (expressed as a percent) of the required loan payments to the borrower’s take-home income at the time of purchase. JobTime was the number of years the borrower had worked at their current job at the time of purchase. CredScore was the borrower’s credit score at the time of purchase. Below is part of the classification tree the finance company derived from the data collected in the study. Assume they classify those with a default probability estimate of at least .5 as Defaulters.
A potential borrower with a credit score of 503 who has been at their current job for 4 years and has a monthly income of $4,700 would like to apply for a loan. To be approved for the loan they would need to be classified as a non-Defaulter. Of the following monthly payments, which is the highest this loan could have to be approved for this potential borrower?
Answer: C
Explanation: This customer would have CredScore ≥ 724 and JobTime ≥ 4. They would be classified as a non-Defaulter regardless of their AutoDebt%. Therefore, the maximum monthly payment they could have would be unlimited.
Difficulty: 3 Hard
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by a classification tree.
Bloom’s: Apply
AACSB: Analytical Thinking
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76) An MBA admissions officer wishes to predict an MBA applicant’s grade point average (GPA) for the MBA program on the basis of the applicant’s score on the Graduate Management Admission Test (GMAT) and their undergraduate GPA (UGPA). The admissions officer used a random sample of previously admitted applicants to build a regression tree that can be used to predict the MBA GPAs of future MBA students. Below is the final regression tree.
Based on this regression tree, how many of the admitted applicants in the sample had a GMAT score of at least 650?
Answer: B
Explanation: This is the Count of admitted applicants with GMAT ≥ 650.
Difficulty: 1 Easy
Topic: Decision Trees II: Regression Trees
Learning Objective: 05-02 Interpret the information provided by a regression tree.
Bloom’s: Understand
AACSB: Analytical Thinking
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77) An MBA admissions officer wishes to predict an MBA applicant’s grade point average (GPA) for the MBA program on the basis of the applicant’s score on the Graduate Management Admission Test (GMAT) and their undergraduate GPA (UGPA). The admissions officer used a random sample of previously admitted applicants to build a regression tree that can be used to predict the MBA GPAs of future MBA students. Below is the final regression tree.
Based on this regression tree, how many of the admitted applicants in the sample had a GMAT score of less than 650?
Answer: D
Explanation: This is the Count of admitted applicants with GMAT < 650.
Difficulty: 1 Easy
Topic: Decision Trees II: Regression Trees
Learning Objective: 05-02 Interpret the information provided by a regression tree.
Bloom’s: Understand
AACSB: Analytical Thinking
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78) An MBA admissions officer wishes to predict an MBA applicant’s grade point average (GPA) for the MBA program on the basis of the applicant’s score on the Graduate Management Admission Test (GMAT) and their undergraduate GPA (UGPA). The admissions officer used a random sample of previously admitted applicants to build a regression tree that can be used to predict the MBA GPAs of future MBA students. Below is the final regression tree.
Based on this regression tree, how many of the admitted applicants in the sample had a GMAT score of at least 740?
Answer: E
Explanation: Of the 126 admitted applicants with GMAT ≥ 650 and UGPA < 3.87, there is no breakdown to know how many have GMAT ≥ 740.
Difficulty: 2 Medium
Topic: Decision Trees II: Regression Trees
Learning Objective: 05-02 Interpret the information provided by a regression tree.
Bloom’s: Understand
AACSB: Analytical Thinking
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79) An MBA admissions officer wishes to predict an MBA applicant’s grade point average (GPA) for the MBA program on the basis of the applicant’s score on the Graduate Management Admission Test (GMAT) and their undergraduate GPA (UGPA). The admissions officer used a random sample of previously admitted applicants to build a regression tree that can be used to predict the MBA GPAs of future MBA students. Below is the final regression tree.
Based on this regression tree, how many of the admitted applicants in the sample had both a GMAT score of at least 740 and an undergraduate GPA of at least 3.87?
Answer: A
Explanation: These would all be among the 507 admitted applicants with GMAT ≥ 650. Furthermore, they would all be among the 381 of those with UGPA ≥ 3.87. Of those, there is a Count of 129 that have GMAT ≥ 740.
Difficulty: 2 Medium
Topic: Decision Trees II: Regression Trees
Learning Objective: 05-02 Interpret the information provided by a regression tree.
Bloom’s: Understand
AACSB: Analytical Thinking
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80) An MBA admissions officer wishes to predict an MBA applicant’s grade point average (GPA) for the MBA program on the basis of the applicant’s score on the Graduate Management Admission Test (GMAT) and their undergraduate GPA (UGPA). The admissions officer used a random sample of previously admitted applicants to build a regression tree that can be used to predict the MBA GPAs of future MBA students. Below is the final regression tree.
Based on this regression tree, what proportion of the admitted applicants in the sample had both a GMAT score of at least 740 and an undergraduate GPA of at least 3.87?
Answer: D
Explanation: These would all be among the 507 admitted applicants with GMAT ≥ 650. Furthermore, they would all be among the 381 of those with UGPA ≥ 3.87. Of those, there is a Count of 129 that have GMAT ≥ 740. This represents a proportion of 129/1056 = 0.122 of the total sample.
Difficulty: 3 Hard
Topic: Decision Trees II: Regression Trees
Learning Objective: 05-02 Interpret the information provided by a regression tree.
Bloom’s: Understand
AACSB: Analytical Thinking
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81) An MBA admissions officer wishes to predict an MBA applicant’s grade point average (GPA) for the MBA program on the basis of the applicant’s score on the Graduate Management Admission Test (GMAT) and their undergraduate GPA (UGPA). The admissions officer used a random sample of previously admitted applicants to build a regression tree that can be used to predict the MBA GPAs of future MBA students. Below is the final regression tree.
An MBA applicant has a GMAT score of 630 and an undergraduate GPA of 3.51. Based on this regression tree, which of the following is the best estimate of this applicant’s MBA GPA if they are admitted into the program?
Answer: B
Explanation: This MBA applicant would have GMAT < 650 and UGPA ≥ 3.21.
Difficulty: 1 Easy
Topic: Decision Trees II: Regression Trees
Learning Objective: 05-02 Interpret the information provided by a regression tree.
Bloom’s: Apply
AACSB: Analytical Thinking
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82) An MBA admissions officer wishes to predict an MBA applicant’s grade point average (GPA) for the MBA program on the basis of the applicant’s score on the Graduate Management Admission Test (GMAT) and their undergraduate GPA (UGPA). The admissions officer used a random sample of previously admitted applicants to build a regression tree that can be used to predict the MBA GPAs of future MBA students. Below is the final regression tree.
An MBA applicant has a GMAT score of 650 and an undergraduate GPA of 3.87. Based on this regression tree, which of the following is the best estimate of this applicant’s MBA GPA if they are admitted into the program?
Answer: E
Explanation:
This MBA applicant would have GMAT ≥ 650, UGPA ≥ 3.87, and then GMAT < 740.
Difficulty: 1 Easy
Topic: Decision Trees II: Regression Trees
Learning Objective: 05-02 Interpret the information provided by a regression tree.
Bloom’s: Apply
AACSB: Analytical Thinking
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83) An MBA admissions officer wishes to predict an MBA applicant’s grade point average (GPA) for the MBA program on the basis of the applicant’s score on the Graduate Management Admission Test (GMAT) and their undergraduate GPA (UGPA). The admissions officer used a random sample of previously admitted applicants to build a regression tree that can be used to predict the MBA GPAs of future MBA students. Below is the final regression tree.
An MBA applicant has a GMAT score of 730 and an undergraduate GPA of 3.21. Based on this regression tree, which of the following is the best estimate of this applicant’s MBA GPA if they are admitted into the program?
Answer: D
Explanation: This MBA applicant would have GMAT ≥ 650 and UGPA < 3.87.
Difficulty: 1 Easy
Topic: Decision Trees II: Regression Trees
Learning Objective: 05-02 Interpret the information provided by a regression tree.
Bloom’s: Apply
AACSB: Analytical Thinking
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84) An MBA admissions officer wishes to predict an MBA applicant’s grade point average (GPA) for the MBA program on the basis of the applicant’s score on the Graduate Management Admission Test (GMAT) and their undergraduate GPA (UGPA). The admissions officer used a random sample of previously admitted applicants to build a regression tree that can be used to predict the MBA GPAs of future MBA students. Below is the final regression tree.
An MBA applicant has a GMAT score of 800 and an undergraduate GPA of 4.00. Based on this regression tree, which of the following is the best estimate of this applicant’s MBA GPA if they are admitted into the program?
Answer: D
Explanation:
This MBA applicant would have GMAT ≥ 650, UGPA ≥ 3.87, and then GMAT ≥ 740.
Difficulty: 1 Easy
Topic: Decision Trees II: Regression Trees
Learning Objective: 05-02 Interpret the information provided by a regression tree.
Bloom’s: Apply
AACSB: Analytical Thinking
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85) An MBA admissions officer wishes to predict an MBA applicant’s grade point average (GPA) for the MBA program on the basis of the applicant’s score on the Graduate Management Admission Test (GMAT) and their undergraduate GPA (UGPA). The admissions officer used a random sample of previously admitted applicants to build a regression tree that can be used to predict the MBA GPAs of future MBA students. Below is the final regression tree.
An MBA applicant has a GMAT score of 550 and an undergraduate GPA of 3.01. Based on this regression tree, which of the following is the best estimate of this applicant’s MBA GPA if they are admitted into the program?
Answer: A
Explanation: This MBA applicant would have GMAT < 650 and UGPA < 3.21.
Difficulty: 1 Easy
Topic: Decision Trees II: Regression Trees
Learning Objective: 05-02 Interpret the information provided by a regression tree.
Bloom’s: Apply
AACSB: Analytical Thinking
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86) An MBA admissions officer wishes to predict an MBA applicant’s grade point average (GPA) for the MBA program on the basis of the applicant’s score on the Graduate Management Admission Test (GMAT) and their undergraduate GPA (UGPA). The admissions officer used a random sample of previously admitted applicants to build a regression tree that can be used to predict the MBA GPAs of future MBA students. Below is the final regression tree.
The school awards a Dean’s Scholarship to admitted applicants who it predicts will earn a GPA of 3.85 or higher in the MBA program. An MBA applicant has an undergraduate GPA of 3.91. Based on this regression tree, which of the following GMAT scores is the lowest this applicant can earn to qualify for the Dean’s Scholarship?
Answer: C
Explanation:
There is no GMAT score < 650 which would allow them to qualify. With a UGPA ≥ 3.87 they will qualify with a GMAT ≥ 740.
Difficulty: 3 Hard
Topic: Decision Trees II: Regression Trees
Learning Objective: 05-02 Interpret the information provided by a regression tree.
Bloom’s: Apply
AACSB: Analytical Thinking
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87) An MBA admissions officer wishes to predict an MBA applicant’s grade point average (GPA) for the MBA program on the basis of the applicant’s score on the Graduate Management Admission Test (GMAT) and their undergraduate GPA (UGPA). The admissions officer used a random sample of previously admitted applicants to build a regression tree that can be used to predict the MBA GPAs of future MBA students. Below is the final regression tree.
The school awards an MBA Scholarship to admitted applicants who it predicts will earn a GPA of 3.7 or higher in the MBA program. An MBA applicant has an undergraduate GPA of 3.87. Based on this regression tree, which of the following GMAT scores is the lowest this applicant can earn to qualify for the Dean’s Scholarship?
Answer: B
Explanation:
There is no GMAT score < 650 which would allow them to qualify. With a UGPA ≥ 3.87 they will have a predicted MBA GPA of 3.72 even with a GMAT < 740. Therefore, they must earn at least 650 on the GMAT to qualify.
Difficulty: 3 Hard
Topic: Decision Trees II: Regression Trees
Learning Objective: 05-02 Interpret the information provided by a regression tree.
Bloom’s: Apply
AACSB: Analytical Thinking
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88) An MBA admissions officer wishes to predict an MBA applicant’s grade point average (GPA) for the MBA program on the basis of the applicant’s score on the Graduate Management Admission Test (GMAT) and their undergraduate GPA (UGPA). The admissions officer used a random sample of previously admitted applicants to build a regression tree that can be used to predict the MBA GPAs of future MBA students. Below is the final regression tree.
The school awards an MBA Scholarship to admitted applicants who it predicts will earn a GPA of 3.7 or higher in the MBA program. An MBA applicant has an undergraduate GPA of 3.21. Based on this regression tree, which of the following GMAT scores is the lowest this applicant can earn to qualify for the Dean’s Scholarship?
Answer: D
Explanation:
With a UGPA ≥ 3.21 they will have a predicted MBA GPA of 3.50 with a GMAT < 650 or a predicted MBA GPA of 3.57 with a GMAT ≥ 650. Therefore, there is no GMAT score which would make their predicted MBA GPA high enough to qualify them.
Difficulty: 3 Hard
Topic: Decision Trees II: Regression Trees
Learning Objective: 05-02 Interpret the information provided by a regression tree.
Bloom’s: Apply
AACSB: Analytical Thinking
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89) An MBA admissions officer wishes to predict an MBA applicant’s grade point average (GPA) for the MBA program on the basis of the applicant’s score on the Graduate Management Admission Test (GMAT) and their undergraduate GPA (UGPA). The admissions officer used a random sample of previously admitted applicants to build a regression tree that can be used to predict the MBA GPAs of future MBA students. Below is the final regression tree.
The school requires a predicted MBA GPA of at least 3.50 for an admitted applicant to be considered for a Graduate Assistantship. An MBA applicant has a GMAT score of 650. Based on this regression tree, which of the following undergraduate GPAs is the lowest this applicant can earn to be considered for a Graduate Assistantship?
Answer: A
Explanation:
With GMAT ≥ 650 they will have a predicted MBA GPA of 3.57 even with UGPA < 3.87. Therefore, the lowest GPA in the list above is sufficient.
Difficulty: 3 Hard
Topic: Decision Trees II: Regression Trees
Learning Objective: 05-02 Interpret the information provided by a regression tree.
Bloom’s: Apply
AACSB: Analytical Thinking
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90) An MBA admissions officer wishes to predict an MBA applicant’s grade point average (GPA) for the MBA program on the basis of the applicant’s score on the Graduate Management Admission Test (GMAT) and their undergraduate GPA (UGPA). The admissions officer used a random sample of previously admitted applicants to build a regression tree that can be used to predict the MBA GPAs of future MBA students. Below is the final regression tree.
The school requires a predicted MBA GPA of at least 3.50 for an admitted applicant to be considered for a Graduate Assistantship. An MBA applicant has a GMAT score of 620. Based on this regression tree, which of the following undergraduate GPAs is the lowest this applicant can earn to be considered for a Graduate Assistantship?
Answer: B
Explanation: With GMAT < 650 they will have a predicted MBA GPA of 3.50 if they earn a UGPA ≥ 3.21. Therefore, the lowest GPA in the list above that qualifies is 3.54.
Difficulty: 3 Hard
Topic: Decision Trees II: Regression Trees
Learning Objective: 05-02 Interpret the information provided by a regression tree.
Bloom’s: Apply
AACSB: Analytical Thinking
Accessibility: Keyboard Navigation
91) A cable television company has randomly selected a sample of 637 Basic package customers for a marketing test to see which customers are more likely to upgrade to the Premium package. They monitored several predictor variables based on customer activity during the most recently billed month. Then they included a special upgrade offer for the Premium package along with their bill. The response variable Upgrade equals Yes if a customer accepted the offer to upgrade to the Premium package and equals No otherwise. Below is the confusion matrix from the k-nearest neighbors classification analysis.
| Confusion Matrix for Best K = 3 | |||
| Predicted Count | |||
| Actual Upgrade | No | Yes | |
| No | 453 | 52 | |
| Yes | 25 | 107 | |
How many customers in the sample were incorrectly classified as upgraders by the k-nearest neighbors approach?
Answer: D
Explanation: There were 52 who the k-nearest neighbors approach would have predicted to upgrade but who did not actually upgrade.
Difficulty: 2 Medium
Topic: k-Nearest Neighbors
Learning Objective: 05-03 Interpret the information provided by k-nearest neighbors.
Bloom’s: Apply
AACSB: Analytical Thinking
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92) A cable television company has randomly selected a sample of 637 Basic package customers for a marketing test to see which customers are more likely to upgrade to the Premium package. They monitored several predictor variables based on customer activity during the most recently billed month. Then they included a special upgrade offer for the Premium package along with their bill. The response variable Upgrade equals Yes if a customer accepted the offer to upgrade to the Premium package and equals No otherwise. Below is the confusion matrix from the k-nearest neighbors classification analysis.
| Confusion Matrix for Best K = 3 | |||
| Predicted Count | |||
| Actual Upgrade | No | Yes | |
| No | 453 | 52 | |
| Yes | 25 | 107 | |
What is the overall misclassification rate for the k-nearest neighbors classification?
Answer: B
Explanation: (25 + 52) / 637 = .121
Difficulty: 2 Medium
Topic: k-Nearest Neighbors
Learning Objective: 05-03 Interpret the information provided by k-nearest neighbors.
Bloom’s: Apply
AACSB: Analytical Thinking
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93) A cable television company has randomly selected a sample of 637 Basic package customers for a marketing test to see which customers are more likely to upgrade to the Premium package. They monitored several predictor variables based on customer activity during the most recently billed month. Then they included a special upgrade offer for the Premium package along with their bill. The response variable Upgrade equals Yes if a customer accepted the offer to upgrade to the Premium package and equals No otherwise. Below is the confusion matrix from the k-nearest neighbors classification analysis.
| Confusion Matrix for Best K = 3 | |||
| Predicted Count | |||
| Actual Upgrade | No | Yes | |
| No | 453 | 52 | |
| Yes | 25 | 107 | |
What is the upgrader misclassification rate for the k-nearest neighbors classification?
Answer: C
Explanation: 25 / (25 + 107) = .189
Difficulty: 2 Medium
Topic: k-Nearest Neighbors
Learning Objective: 05-03 Interpret the information provided by k-nearest neighbors.
Bloom’s: Apply
AACSB: Analytical Thinking
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94) A cable television company has randomly selected a sample of 637 Basic package customers for a marketing test to see which customers are more likely to upgrade to the Premium package. They monitored several predictor variables based on customer activity during the most recently billed month. Then they included a special upgrade offer for the Premium package along with their bill. The response variable Upgrade equals Yes if a customer accepted the offer to upgrade to the Premium package and equals No otherwise. Below is the confusion matrix from the k-nearest neighbors classification analysis.
| Confusion Matrix for Best K = 3 | |||
| Predicted Count | |||
| Actual Upgrade | No | Yes | |
| No | 453 | 52 | |
| Yes | 25 | 107 | |
How many customers in the sample were correctly classified as non-upgraders by the k-nearest neighbors approach?
Answer: D
Explanation: There were 52 who the k-nearest neighbors approach would have predicted to upgrade but who did not actually upgrade.
Difficulty: 2 Medium
Topic: k-Nearest Neighbors
Learning Objective: 05-03 Interpret the information provided by k-nearest neighbors.
Bloom’s: Apply
AACSB: Analytical Thinking
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95) A cable television company has randomly selected a sample of 637 Basic package customers for a marketing test to see which customers are more likely to upgrade to the Premium package. They monitored several predictor variables based on customer activity during the most recently billed month. Then they included a special upgrade offer for the Premium package along with their bill. The response variable Upgrade equals Yes if a customer accepted the offer to upgrade to the Premium package and equals No otherwise. Below is the confusion matrix from the k-nearest neighbors classification analysis.
| Confusion Matrix for Best K = 3 | |||
| Predicted Count | |||
| Actual Upgrade | No | Yes | |
| No | 453 | 52 | |
| Yes | 25 | 107 | |
What is the non-upgrader misclassification rate for the k-nearest neighbors classification?
Answer: B
Explanation: 52 / (52 + 453) = .103
Difficulty: 2 Medium
Topic: k-Nearest Neighbors
Learning Objective: 05-03 Interpret the information provided by k-nearest neighbors.
Bloom’s: Apply
AACSB: Analytical Thinking
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96) A cable television company has randomly selected a sample of 222 Basic package customers for a marketing test to see which customers are more likely to upgrade to the Premium package. They monitored two predictor variables based on customer activity during the most recently billed month. Then they included a special upgrade offer for the Premium package along with their bill. The response variable Upgrade equals True if a customer accepted the offer to upgrade to the Premium package and equals False otherwise. The predictor variable ViewTime is the average daily minutes the customer had at least one TV on in their house. Network is 1 if the TV is tuned to a traditional (“over the public airwaves”) broadcast network at least 50 percent of the time that it is turned on and 0 otherwise. Let the events U, Uc, HV, and NV denote the events that the randomly selected Basic customer, respectively, upgraded, did not upgrade, had a high ViewTime [a view time greater than the median amount of 507 minutes], and was primarily a traditional broadcast network viewer [i.e., spent at least 50% of time tuned to a traditional network]. The data they collected show:
1) 43 out of 222 Basic customers upgraded, or P(U) = 43/222.
2) 179 out of 222 Basic customers did not upgrade, or P(Uc) = 179/222.
3) 37 out of 43 upgraders had a high ViewTime, or P(HV|U) = 37/43.
4) 63 out of 179 non-upgraders had a high ViewTime, or P(HV|Uc) = 63/179.
5) 7 out of 43 upgraders was primarily a traditional broadcast network viewer,
or P(NV|U) = 7/43.
6) 52 out of 179 non-upgraders was primarily a traditional broadcast network viewer,
or P(NV|Uc) = 52/179.
How many upgraders did not have a ViewTime greater than the median amount of 511 minutes?
Answer: A
Explanation: Since 37 out of 43 upgraders had a high ViewTime, 43 – 37 = 6 did not have a high ViewTime.
Difficulty: 1 Easy
Topic: Naive Bayes Classification
Learning Objective: 05-04 Interpret the information provided by naive Bayes classification.
Bloom’s: Understand
AACSB: Analytical Thinking
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97) A cable television company has randomly selected a sample of 222 Basic package customers for a marketing test to see which customers are more likely to upgrade to the Premium package. They monitored two predictor variables based on customer activity during the most recently billed month. Then they included a special upgrade offer for the Premium package along with their bill. The response variable Upgrade equals True if a customer accepted the offer to upgrade to the Premium package and equals False otherwise. The predictor variable ViewTime is the average daily minutes the customer had at least one TV on in their house. Network is 1 if the TV is tuned to a traditional (“over the public airwaves”) broadcast network at least 50 percent of the time that it is turned on and 0 otherwise. Let the events U, Uc, HV, and NV denote the events that the randomly selected Basic customer, respectively, upgraded, did not upgrade, had a high ViewTime [a view time greater than the median amount of 507 minutes], and was primarily a traditional broadcast network viewer [i.e., spent at least 50% of time tuned to a traditional network]. The data they collected show:
1) 43 out of 222 Basic customers upgraded, or P(U) = 43/222.
2) 179 out of 222 Basic customers did not upgrade, or P(Uc) = 179/222.
3) 37 out of 43 upgraders had a high ViewTime, or P(HV|U) = 37/43.
4) 63 out of 179 non-upgraders had a high ViewTime, or P(HV|Uc) = 63/179.
5) 7 out of 43 upgraders was primarily a traditional broadcast network viewer,
or P(NV|U) = 7/43.
6) 52 out of 179 non-upgraders was primarily a traditional broadcast network viewer,
or P(NV|Uc) = 52/179.
How many upgraders were not primarily traditional broadcast network viewers?
Answer: C
Explanation: Since 7 out of 43 upgraders were primarily traditional broadcast network viewers, 43 − 7 = 36 were not primarily traditional broadcast network viewers.
Difficulty: 1 Easy
Topic: Naive Bayes Classification
Learning Objective: 05-04 Interpret the information provided by naive Bayes classification.
Bloom’s: Understand
AACSB: Analytical Thinking
Accessibility: Keyboard Navigation
98) A cable television company has randomly selected a sample of 222 Basic package customers for a marketing test to see which customers are more likely to upgrade to the Premium package. They monitored two predictor variables based on customer activity during the most recently billed month. Then they included a special upgrade offer for the Premium package along with their bill. The response variable Upgrade equals True if a customer accepted the offer to upgrade to the Premium package and equals False otherwise. The predictor variable ViewTime is the average daily minutes the customer had at least one TV on in their house. Network is 1 if the TV is tuned to a traditional (“over the public airwaves”) broadcast network at least 50 percent of the time that it is turned on and 0 otherwise. Let the events U, Uc, HV, and NV denote the events that the randomly selected Basic customer, respectively, upgraded, did not upgrade, had a high ViewTime [a view time greater than the median amount of 507 minutes], and was primarily a traditional broadcast network viewer [i.e., spent at least 50% of time tuned to a traditional network]. The data they collected show:
1) 43 out of 222 Basic customers upgraded, or P(U) = 43/222.
2) 179 out of 222 Basic customers did not upgrade, or P(Uc) = 179/222.
3) 37 out of 43 upgraders had a high ViewTime, or P(HV|U) = 37/43.
4) 63 out of 179 non-upgraders had a high ViewTime, or P(HV|Uc) = 63/179.
5) 7 out of 43 upgraders was primarily a traditional broadcast network viewer,
or P(NV|U) = 7/43.
6) 52 out of 179 non-upgraders was primarily a traditional broadcast network viewer,
or P(NV|Uc) = 52/179.
Using naive Bayes’ Theorem, what is the approximate probability that a Basic customer will upgrade if they have a high ViewTime and primarily view traditional broadcast networks?
Answer: D
Explanation:
P(U | HV ∩ NV) =
≈
=
=.248
Difficulty: 3 Hard
Topic: Naive Bayes Classification
Learning Objective: 05-04 Interpret the information provided by naive Bayes classification.
Bloom’s: Apply
AACSB: Analytical Thinking
Accessibility: Keyboard Navigation
99) A cable television company has randomly selected a sample of 222 Basic package customers for a marketing test to see which customers are more likely to upgrade to the Premium package. They monitored two predictor variables based on customer activity during the most recently billed month. Then they included a special upgrade offer for the Premium package along with their bill. The response variable Upgrade equals True if a customer accepted the offer to upgrade to the Premium package and equals False otherwise. The predictor variable ViewTime is the average daily minutes the customer had at least one TV on in their house. Network is 1 if the TV is tuned to a traditional (“over the public airwaves”) broadcast network at least 50 percent of the time that it is turned on and 0 otherwise. Let the events U, Uc, HV, and NV denote the events that the randomly selected Basic customer, respectively, upgraded, did not upgrade, had a high ViewTime [a view time greater than the median amount of 507 minutes], and was primarily a traditional broadcast network viewer [i.e., spent at least 50% of time tuned to a traditional network]. The data they collected show:
1) 43 out of 222 Basic customers upgraded, or P(U) = 43/222.
2) 179 out of 222 Basic customers did not upgrade, or P(Uc) = 179/222.
3) 37 out of 43 upgraders had a high ViewTime, or P(HV|U) = 37/43.
4) 63 out of 179 non-upgraders had a high ViewTime, or P(HV|Uc) = 63/179.
5) 7 out of 43 upgraders was primarily a traditional broadcast network viewer,
or P(NV|U) = 7/43.
6) 52 out of 179 non-upgraders was primarily a traditional broadcast network viewer,
or P(NV|Uc) = 52/179.
Using naive Bayes’ Theorem, what is the approximate probability that a Basic customer will upgrade if they do not have a high ViewTime and primarily view traditional broadcast networks?
Answer: A
Explanation:
P(U | ∩ NV) =
≈
=
=.028
Difficulty: 3 Hard
Topic: Naive Bayes Classification
Learning Objective: 05-04 Interpret the information provided by naive Bayes classification.
Bloom’s: Apply
AACSB: Analytical Thinking
Accessibility: Keyboard Navigation
100) A cable television company has randomly selected a sample of 222 Basic package customers for a marketing test to see which customers are more likely to upgrade to the Premium package. They monitored two predictor variables based on customer activity during the most recently billed month. Then they included a special upgrade offer for the Premium package along with their bill. The response variable Upgrade equals True if a customer accepted the offer to upgrade to the Premium package and equals False otherwise. The predictor variable ViewTime is the average daily minutes the customer had at least one TV on in their house. Network is 1 if the TV is tuned to a traditional (“over the public airwaves”) broadcast network at least 50 percent of the time that it is turned on and 0 otherwise. Let the events U, Uc, HV, and NV denote the events that the randomly selected Basic customer, respectively, upgraded, did not upgrade, had a high ViewTime [a view time greater than the median amount of 507 minutes], and was primarily a traditional broadcast network viewer [i.e., spent at least 50% of time tuned to a traditional network]. The data they collected show:
1) 43 out of 222 Basic customers upgraded, or P(U) = 43/222.
2) 179 out of 222 Basic customers did not upgrade, or P(Uc) = 179/222.
3) 37 out of 43 upgraders had a high ViewTime, or P(HV|U) = 37/43.
4) 63 out of 179 non-upgraders had a high ViewTime, or P(HV|Uc) = 63/179.
5) 7 out of 43 upgraders was primarily a traditional broadcast network viewer,
or P(NV|U) = 7/43.
6) 52 out of 179 non-upgraders was primarily a traditional broadcast network viewer,
or P(NV|Uc) = 52/179.
Using naive Bayes’ Theorem, what is the approximate probability that a Basic customer will upgrade if they do not have a high ViewTime and do not primarily view traditional broadcast networks?
Answer: A
Explanation:
P(U | ∩ ) =
≈
=
=.058
Difficulty: 3 Hard
Topic: Naive Bayes Classification
Learning Objective: 05-04 Interpret the information provided by naive Bayes classification.
Bloom’s: Apply
AACSB: Analytical Thinking
Accessibility: Keyboard Navigation
101) An internet service provider (ISP) has randomly selected a sample of 347 observations concerning values of the response variable Churn and several predictor variables based on customer activity during the most recently billed month. Here Churn equals Yes if a customer churned—left the internet service provider for another ISP—and equals No otherwise. The predictor variable MinutesOn is the average daily minutes the customer spent online. EmailSent is the average daily number of emails the customer sent from the email address provided by the ISP. ServCalls is the number of times the customer called for service. Below is part of the classification tree they derived from the data collected in the study.
Fill in the missing counts and rates in the classification tree above.
Answer:
Explanation: Counts must add to the leaf above. Rates must add to 1.000.
Difficulty: 2 Medium
Topic: Decision Trees I: Classification Trees
Learning Objective: 05-01 Interpret the information provided by a classification tree.
Bloom’s: Apply
AACSB: Analytical Thinking
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Original price was: $30.00.$20.00Current price is: $20.00.
Original price was: $30.00.$20.00Current price is: $20.00.
Original price was: $30.00.$20.00Current price is: $20.00.
Original price was: $30.00.$20.00Current price is: $20.00.
Original price was: $30.00.$20.00Current price is: $20.00.
Original price was: $30.00.$20.00Current price is: $20.00.
