Deck 14: Business Analytics

Question 1

SCENARIO 14-1
The table below contains the sparklines for the rates of return (in percentage) for three different stocks from 2007 to 2013.
Referring to SCENARIO 14-1, the rates of return of stock have the smallest variation among the three.

Answer 1

B

Question 2

Which of the following finds relationships in data that may not be readily apparent?

A)Predictive analytics
B)Prescriptive analytics
C)Productive analytics
D)Descriptive analytics

Answer 2

Predictive analytics

Question 3

Double-clicking a cell in a PivotTable causes Excel to drill down and display the underlying data in a new worksheet.

Answer 3

True

Question 4

Some business analytics involve starting with many variables, followed by filtering the data by exploring specific combinations of categorical values or numerical range.In Excel, this approach is mimicked by using a drill-down.

Question 5

Treemaps that use color to represent the value of a second variable, thereby increasing the data density of the displays, is an example of chartjunk.

Question 6

Most information design specialists prefer bullet graphs over gauges because bullet graphs foster the direct comparison of each measurement.

Question 7

Bullet graphs that use color to represent the value of a second variable, thereby increasing the data density of the displays, is an example of chartjunk.

Question 8

Some business analytics involve starting with many variables, followed by filtering the data by exploring specific combinations of categorical values or numerical range.In Excel, this approach is mimicked by using a slicer.

Question 9

Which of the following investigates what should occur and suggest the best course of action for the future?

A)Predictive analytics
B)Prescriptive analytics
C)Productive analytics
D)Descriptive analytics

Question 10

Which of the following explores business activities that have occurred or are occurring in the present moment?

A)Predictive analytics
B)Prescriptive analytics
C)Productive analytics
D)Descriptive analytics

Question 11

Some business analytics involve starting with many variables, followed by filtering the data by exploring specific combinations of categorical values or numerical range.In Excel, this approach is mimicked by using gauges.

Question 12

Some business analytics are performed by adding variables to see if unforeseen relationships are uncovered.

Question 13

Which of the following disciplines is typically NOT involved in business analytics?

A)Economics
B)Statistics
C)Information system
D)Management science

Question 14

You can compute any of the numerical descriptive statistics for the variables of the new worksheet that a drill-down in a PivotTable creates.

Question 15

There is no significant difference between filtering performed in a complex real- world business analytic and filtering performed using the slicers in a PivotTable in Excel.

Question 16

Some business analytics involve starting with many variables, followed by filtering the data by exploring specific combinations of categorical values or numerical range.

Question 17

Which of the following is NOT among the three broad categories of analytic methods?

A)Predictive analytics
B)Prescriptive analytics
C)Productive analytics
D)Descriptive analytics

Question 18

Dashboards may contain which of the following?

A)Treemaps
B)Gauges
C)Contingency table
D)All of the above

Question 19

SCENARIO 14-1
The table below contains the sparklines for the rates of return (in percentage) for three different stocks from 2007 to 2013.
Referring to SCENARIO 14-1, the pattern of the rates of return of stock and stock are similar.

Question 20

In real-world business analytics, filtering is typically performed on large data based on complex conditional relationships.

Question 21

Which of the following is NOT one of the categories of predictive analytics methods?

A)Classification
B)Clustering
C)Association
D)Description

Question 22

In a classification tree, the dependent variable is a categorical variable.

Question 23

SCENARIO 14-1
The table below contains the sparklines for the rates of return (in percentage) for three different stocks from 2007 to 2013.
Referring to SCENARIO 14-1, the sparklines enable you to predict that the rates of return of the stock market in 2014 will be higher than in 2013.

Question 24

Which of the following is NOT among the predictive analytics methods covered in the book?

A)Neural networks
B)Cluster analysis
C)Factor analysis
D)Multidimensional scaling

Question 25

SCENARIO 14-2
The treemap below shows the amounts (size) measured in billions of US dollars and percentage changes from prior year (color) of business-to-consumer ecommerce sales last year for North America, Asia Pacific, Western Europe, Central & Eastern Europe, Latin America, and Middle East
& Africa.
Referring to SCENARIO 14-2, which region has the largest amount of business-to-consumer ecommerce sales last year?

Question 26

Which of the following is NOT among the predictive analytics methods covered in the book?

A)Neural networks
B)Simple component analysis
C)Cluster analysis
D)Multidimensional scaling

Question 27

SCENARIO 14-2
The treemap below shows the amounts (size) measured in billions of US dollars and percentage changes from prior year (color) of business-to-consumer ecommerce sales last year for North America, Asia Pacific, Western Europe, Central & Eastern Europe, Latin America, and Middle East
& Africa.
Referring to SCENARIO 14-2, the Middle East & Africa region has the slowest growth in business-to-consumer ecommerce sales last year.

Question 28

SCENARIO 14-2
The treemap below shows the amounts (size) measured in billions of US dollars and percentage changes from prior year (color) of business-to-consumer ecommerce sales last year for North America, Asia Pacific, Western Europe, Central & Eastern Europe, Latin America, and Middle East
& Africa.
Referring to SCENARIO 14-2, which region has the smallest amount of business-to-consumer ecommerce sales last year?

Question 29

SCENARIO 14-2
The treemap below shows the amounts (size) measured in billions of US dollars and percentage changes from prior year (color) of business-to-consumer ecommerce sales last year for North America, Asia Pacific, Western Europe, Central & Eastern Europe, Latin America, and Middle East
& Africa.
Referring to SCENARIO 14-2, the Western Europe region has the largest amount of business-to-consumer ecommerce sales last year.

Question 30

Data mining uses various techniques to extract useful information from huge depositories of data.

Question 31

In a regression tree, the dependent variable is a categorical variable.

Question 32

SCENARIO 14-1
The table below contains the sparklines for the rates of return (in percentage) for three different stocks from 2007 to 2013.
Referring to SCENARIO 14-1, the sparklines enable you to predict that the rates of return of the stock market in 2014 will be about the same as in 2013.

Question 33

Which of the following is NOT among the predictive analytics methods covered in the book?

A)Principle component analysis
B)Neural networks
C)Cluster analysis
D)Multidimensional scaling

Question 34

Data mining is used mostly in the mining industry.

Question 35

SCENARIO 14-2
The treemap below shows the amounts (size) measured in billions of US dollars and percentage changes from prior year (color) of business-to-consumer ecommerce sales last year for North America, Asia Pacific, Western Europe, Central & Eastern Europe, Latin America, and Middle East
& Africa.
Referring to SCENARIO 14-2, the North America region has the fastest growth in business-to-consumer ecommerce sales last year.

Question 36

SCENARIO 14-1
The table below contains the sparklines for the rates of return (in percentage) for three different stocks from 2007 to 2013.
Referring to SCENARIO 14-1, the sparklines enable you to draw conclusions on the historical trend of the rates of return of the three stocks.

Question 37

Which of the following is NOT one of the categories of predictive analytics methods?

A)Clustering
B)Recommendation
C)Association
D)Prediction

Question 38

SCENARIO 14-2
The treemap below shows the amounts (size) measured in billions of US dollars and percentage changes from prior year (color) of business-to-consumer ecommerce sales last year for North America, Asia Pacific, Western Europe, Central & Eastern Europe, Latin America, and Middle East
& Africa.
Referring to SCENARIO 14-2, the Asia Pacific region has the largest amount of business-to-consumer ecommerce sales last year.

Question 39

SCENARIO 14-2
The treemap below shows the amounts (size) measured in billions of US dollars and percentage changes from prior year (color) of business-to-consumer ecommerce sales last year for North America, Asia Pacific, Western Europe, Central & Eastern Europe, Latin America, and Middle East
& Africa.
Referring to SCENARIO 14-2, which region has the fastest growth in business-to-consumer ecommerce sales last year?

Question 40

SCENARIO 14-1
The table below contains the sparklines for the rates of return (in percentage) for three different stocks from 2007 to 2013.
Referring to SCENARIO 14-1, the sparklines enable you to conclude that the rates of return of the stock market in general are volatile from 2007 to 2013.

Question 41

Successful implementation of a classification tree requires rules for splitting the data at each node based on an independent variable.

Question 42

The likelihood ratio chi square statistic G2be attributed to the response that has occurred.

Question 43

The result of the regression tree is affected by the distribution of the independent variables.

Question 44

The Akaike information criteria (AIC) or the corrected Akaike information criteria(AICc) can be used to compare alternative models chosen by the classification tree.

Question 45

Splitting of a node might be followed by pruning if necessary in a classification tree.

Question 46

Successful implementation of a regression tree requires rules for deciding when a branch of the tree cannot be split any more.

Question 47

The Akaike information criteria (AIC) or the corrected Akaike information criteria(AICc) is a measure of the probability that can be attributed to the response that has occurred.

Question 48

SCENARIO 14-3
is a measure of the probability that can
The tree diagram below shows the results of the classification tree model that has been constructed to predict the probability of a cable company's customers who will switch ("Yes" or "No") into its bundled program offering based on the price ($30, $40, $50, $60) and whether the customer spends more than 5 hours a day watching TV ("Yes" or "No") using the data set of 100 customers collected from a survey.
Referring to SCENARIO 14-3, what is the highest rate of switching into the bundled offering?

Question 49

Splitting is always followed by pruning in a classification tree.

Question 50

SCENARIO 14-3
is a measure of the probability that can
The tree diagram below shows the results of the classification tree model that has been constructed to predict the probability of a cable company's customers who will switch ("Yes" or "No") into its bundled program offering based on the price ($30, $40, $50, $60) and whether the customer spends more than 5 hours a day watching TV ("Yes" or "No") using the data set of 100 customers collected from a survey.
Referring to SCENARIO 14-3, what percentage of the variation in whether a customer will switch into its bundled program offering can be explained by the price and whether the customer spends more than 5 hours a day watching TV?

Question 51

SCENARIO 14-3
is a measure of the probability that can
The tree diagram below shows the results of the classification tree model that has been constructed to predict the probability of a cable company's customers who will switch ("Yes" or "No") into its bundled program offering based on the price ($30, $40, $50, $60) and whether the customer spends more than 5 hours a day watching TV ("Yes" or "No") using the data set of 100 customers collected from a survey.
Referring to SCENARIO 14-3, the highest probability of switching is predicted to occur among customers who watch more than 5 hours of TV a day and are offered the bundled price of between $30 and $40.

Question 52

SCENARIO 14-3
is a measure of the probability that can
The tree diagram below shows the results of the classification tree model that has been constructed to predict the probability of a cable company's customers who will switch ("Yes" or "No") into its bundled program offering based on the price ($30, $40, $50, $60) and whether the customer spends more than 5 hours a day watching TV ("Yes" or "No") using the data set of 100 customers collected from a survey.
Referring to SCENARIO 14-3, the highest probability of switching is predicted to occur among customers who watch more than 5 hours of TV a day and are offered the bundled price of higher than $50.

Question 53

SCENARIO 14-3
is a measure of the probability that can
The tree diagram below shows the results of the classification tree model that has been constructed to predict the probability of a cable company's customers who will switch ("Yes" or "No") into its bundled program offering based on the price ($30, $40, $50, $60) and whether the customer spends more than 5 hours a day watching TV ("Yes" or "No") using the data set of 100 customers collected from a survey.
Referring to SCENARIO 14-3, the first split occurs at what price?

Question 54

SCENARIO 14-3
is a measure of the probability that can
The tree diagram below shows the results of the classification tree model that has been constructed to predict the probability of a cable company's customers who will switch ("Yes" or "No") into its bundled program offering based on the price ($30, $40, $50, $60) and whether the customer spends more than 5 hours a day watching TV ("Yes" or "No") using the data set of 100 customers collected from a survey.
Referring to SCENARIO 14-3, what is the lowest rate of switching into the bundled offering?

Question 55

Classification tree is not sensitive to the distribution of the independent variables.

Question 56

Successful implementation of a regression tree requires a method to provide prediction for the target variable at each of the nodes.

Question 57

The LogWorth statistic is a measure of the probability that can be attributed to the response that has occurred.

Question 58

The LogWorth statistic is used to decide when to split a node of a regression tree.

Question 59

Successful use of a regression tree requires a precise description of the parameters of the tree.

Question 60

Successful implementation of a classification tree requires objective criteria for splitting the data at each node based on a dependent variable.

Question 61

SCENARIO 14-4
The regression tree below was obtained for predicting the weekend box office revenue of a newly released movie (in thousands of dollars) based on data collected in different cities on the expenditure (at $25, $30, $35, $40, $45, $50, $55, $60, $65 or $70 thousand) spent on TV advertising and the number of times (10, 15, 20, 25, 30 or 35) a day the advertisement appear on TV.
Referring to SCENARIO 14-4, the highest mean weekend box office revenue is predicted to occur with less than $45 thousand spent on TV advertisement and fewer than 25 advertisement appearances a day.

Question 62

SCENARIO 14-4
The regression tree below was obtained for predicting the weekend box office revenue of a newly released movie (in thousands of dollars) based on data collected in different cities on the expenditure (at $25, $30, $35, $40, $45, $50, $55, $60, $65 or $70 thousand) spent on TV advertising and the number of times (10, 15, 20, 25, 30 or 35) a day the advertisement appear on TV.
Referring to SCENARIO 14-4, what percentage of the variation in weekend box office revenue can be explained by the amount spent on TV advertising and the number of times a day the advertisement appears on TV?

Question 63

SCENARIO 14-4
The regression tree below was obtained for predicting the weekend box office revenue of a newly released movie (in thousands of dollars) based on data collected in different cities on the expenditure (at $25, $30, $35, $40, $45, $50, $55, $60, $65 or $70 thousand) spent on TV advertising and the number of times (10, 15, 20, 25, 30 or 35) a day the advertisement appear on TV.
Referring to SCENARIO 14-4, the first split occurs at 25 TV appearances a day of the advertisement.

Question 64

SCENARIO 14-4
The regression tree below was obtained for predicting the weekend box office revenue of a newly released movie (in thousands of dollars) based on data collected in different cities on the expenditure (at $25, $30, $35, $40, $45, $50, $55, $60, $65 or $70 thousand) spent on TV advertising and the number of times (10, 15, 20, 25, 30 or 35) a day the advertisement appear on TV.
Neural networks do not make any a priori assumption about the distribution of the data and, hence, are nonparametric methods.

Question 65

SCENARIO 14-4
The regression tree below was obtained for predicting the weekend box office revenue of a newly released movie (in thousands of dollars) based on data collected in different cities on the expenditure (at $25, $30, $35, $40, $45, $50, $55, $60, $65 or $70 thousand) spent on TV advertising and the number of times (10, 15, 20, 25, 30 or 35) a day the advertisement appear on TV.
Referring to SCENARIO 14-4, the highest mean weekend box office revenue is predicted to occur with $55 thousand spent on TV advertisement and at least 25 advertisement appearances a day.

Question 66

SCENARIO 14-4
The regression tree below was obtained for predicting the weekend box office revenue of a newly released movie (in thousands of dollars) based on data collected in different cities on the expenditure (at $25, $30, $35, $40, $45, $50, $55, $60, $65 or $70 thousand) spent on TV advertising and the number of times (10, 15, 20, 25, 30 or 35) a day the advertisement appear on TV.
Referring to SCENARIO 14-4, the highest mean weekend box office revenue is predicted to occur with at least $45 thousand spent on TV advertisement and fewer than 25 advertisement appearances a day.

Question 67

SCENARIO 14-3
is a measure of the probability that can
The tree diagram below shows the results of the classification tree model that has been constructed to predict the probability of a cable company's customers who will switch ("Yes" or "No") into its bundled program offering based on the price ($30, $40, $50, $60) and whether the customer spends more than 5 hours a day watching TV ("Yes" or "No") using the data set of 100 customers collected from a survey.
Referring to SCENARIO 14-3, the highest probability of switching is predicted to occur among customers who watch more than 5 hours of TV a day and are offered the bundled price of higher than $40.

Question 68

Neural networks require only training data but not validating data.

Question 69

Neural networks can suffer from poor quality of data, insufficient data, or overfitted models.

Question 70

Multilayer perceptrons usually contain an input layer, a hidden layer and an output layer.

Question 71

SCENARIO 14-4
The regression tree below was obtained for predicting the weekend box office revenue of a newly released movie (in thousands of dollars) based on data collected in different cities on the expenditure (at $25, $30, $35, $40, $45, $50, $55, $60, $65 or $70 thousand) spent on TV advertising and the number of times (10, 15, 20, 25, 30 or 35) a day the advertisement appear on TV.
Referring to SCENARIO 14-4, the first split occurs at $45 thousand spent on TVadvertising.

Question 72

The forward-and-backward computation among the three layers of a multilayer perceptron is repeated until the output layer detects that the difference between the predicted results and the target values has been minimized or is at an acceptable level.

Question 73

Neural networks use the validating data to uncover a model and then the training data to see if the model can make the correct prediction or classification.

Question 74

SCENARIO 14-4
The regression tree below was obtained for predicting the weekend box office revenue of a newly released movie (in thousands of dollars) based on data collected in different cities on the expenditure (at $25, $30, $35, $40, $45, $50, $55, $60, $65 or $70 thousand) spent on TV advertising and the number of times (10, 15, 20, 25, 30 or 35) a day the advertisement appear on TV.
Referring to SCENARIO 14-4, how many cities were used in generating the regression tree?

Question 75

SCENARIO 14-4
The regression tree below was obtained for predicting the weekend box office revenue of a newly released movie (in thousands of dollars) based on data collected in different cities on the expenditure (at $25, $30, $35, $40, $45, $50, $55, $60, $65 or $70 thousand) spent on TV advertising and the number of times (10, 15, 20, 25, 30 or 35) a day the advertisement appear on TV.
Referring to SCENARIO 14-4, the highest mean weekend box office revenue is predicted to occur with $30 thousand spent on TV advertisement and 30 advertisement appearances a day.

Question 76

SCENARIO 14-4
The regression tree below was obtained for predicting the weekend box office revenue of a newly released movie (in thousands of dollars) based on data collected in different cities on the expenditure (at $25, $30, $35, $40, $45, $50, $55, $60, $65 or $70 thousand) spent on TV advertising and the number of times (10, 15, 20, 25, 30 or 35) a day the advertisement appear on TV.
Referring to SCENARIO 14-4, the highest mean weekend box office revenue is predicted to occur with $55 thousands spent on TV advertisement and 35 advertisement appearances a day.

Question 77

SCENARIO 14-3
is a measure of the probability that can
The tree diagram below shows the results of the classification tree model that has been constructed to predict the probability of a cable company's customers who will switch ("Yes" or "No") into its bundled program offering based on the price ($30, $40, $50, $60) and whether the customer spends more than 5 hours a day watching TV ("Yes" or "No") using the data set of 100 customers collected from a survey.
Referring to SCENARIO 14-3, the highest probability of switching is predicted to occur among customers who do not watch more than 5 hours of TV a day and are offered the bundled price of lower than $50.

Question 78

SCENARIO 14-4
The regression tree below was obtained for predicting the weekend box office revenue of a newly released movie (in thousands of dollars) based on data collected in different cities on the expenditure (at $25, $30, $35, $40, $45, $50, $55, $60, $65 or $70 thousand) spent on TV advertising and the number of times (10, 15, 20, 25, 30 or 35) a day the advertisement appear on TV.
Referring to SCENARIO 14-4, the highest mean weekend box office revenue is predicted to occur with at least $45 thousand spent on TV advertisement and at least 25 advertisement appearances a day.

Question 79

SCENARIO 14-3
is a measure of the probability that can
The tree diagram below shows the results of the classification tree model that has been constructed to predict the probability of a cable company's customers who will switch ("Yes" or "No") into its bundled program offering based on the price ($30, $40, $50, $60) and whether the customer spends more than 5 hours a day watching TV ("Yes" or "No") using the data set of 100 customers collected from a survey.
Referring to SCENARIO 14-3, the highest probability of switching is predicted to occur among customers who watch more than 5 hours of TV a day and are offered the bundled price of lower than $50.

Question 80

SCENARIO 14-3
is a measure of the probability that can
The tree diagram below shows the results of the classification tree model that has been constructed to predict the probability of a cable company's customers who will switch ("Yes" or "No") into its bundled program offering based on the price ($30, $40, $50, $60) and whether the customer spends more than 5 hours a day watching TV ("Yes" or "No") using the data set of 100 customers collected from a survey.
Referring to SCENARIO 14-3, the highest probability of switching is predicted to occur among customers who do not watch more than 5 hours of TV a day and are offered the bundled price of higher than $50.