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Deck 17: Business Analytics
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SCENARIO 17-1
The table below contains the sparklines for the rates of return (in percentage)for three different stocks from 2007 to 2013.
Referring to Scenario 17-1,the sparklines enable you to draw conclusions on the historical trend of the rates of return of the three stocks.
The table below contains the sparklines for the rates of return (in percentage)for three different stocks from 2007 to 2013.
Referring to Scenario 17-1,the sparklines enable you to draw conclusions on the historical trend of the rates of return of the three stocks.
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SCENARIO 17-2
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 17-2,the Western Europe region has the largest amount of business-to-consumer ecommerce sales last year.
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 17-2,the Western Europe region has the largest amount of business-to-consumer ecommerce sales last year.
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SCENARIO 17-2
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 17-2,which region has the slowest growth in business-to-consumer ecommerce sales last year?
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 17-2,which region has the slowest growth in business-to-consumer ecommerce sales last year?
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SCENARIO 17-2
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 17-2,which region has the largest amount of business-to-consumer ecommerce sales last year?
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 17-2,which region has the largest amount of business-to-consumer ecommerce sales last year?
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SCENARIO 17-1
The table below contains the sparklines for the rates of return (in percentage)for three different stocks from 2007 to 2013.
Referring to Scenario 17-1,the sparklines enable you to conclude that the rates of return of the stock market in general are volatile from 2007 to 2013.
The table below contains the sparklines for the rates of return (in percentage)for three different stocks from 2007 to 2013.
Referring to Scenario 17-1,the sparklines enable you to conclude that the rates of return of the stock market in general are volatile from 2007 to 2013.
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SCENARIO 17-2
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 17-2,which region has the fastest growth in business-to-consumer ecommerce sales last year?
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 17-2,which region has the fastest growth in business-to-consumer ecommerce sales last year?
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SCENARIO 17-2
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 17-2,the Middle East & Africa region has the slowest growth in business-to-consumer ecommerce sales last year.
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 17-2,the Middle East & Africa region has the slowest growth in business-to-consumer ecommerce sales last year.
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SCENARIO 17-1
The table below contains the sparklines for the rates of return (in percentage)for three different stocks from 2007 to 2013.
Referring to Scenario 17-1,the sparklines enable you to predict that the rates of return of the stock market in 2014 will be higher than in 2013.
The table below contains the sparklines for the rates of return (in percentage)for three different stocks from 2007 to 2013.
Referring to Scenario 17-1,the sparklines enable you to predict that the rates of return of the stock market in 2014 will be higher than in 2013.
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SCENARIO 17-2
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 17-2,the Asia Pacific region has the largest amount of business-to-consumer ecommerce sales last year.
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 17-2,the Asia Pacific region has the largest amount of business-to-consumer ecommerce sales last year.
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SCENARIO 17-2
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 17-2,the North America region has the fastest growth in business-to-consumer ecommerce sales last year.
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 17-2,the North America region has the fastest growth in business-to-consumer ecommerce sales last year.
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SCENARIO 17-1
The table below contains the sparklines for the rates of return (in percentage)for three different stocks from 2007 to 2013.
Referring to Scenario 17-1,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.
The table below contains the sparklines for the rates of return (in percentage)for three different stocks from 2007 to 2013.
Referring to Scenario 17-1,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.
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SCENARIO 17-2
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 17-2,which region has the smallest amount of business-to-consumer ecommerce sales last year?
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 17-2,which region has the smallest amount of business-to-consumer ecommerce sales last year?
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SCENARIO 17-3
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 17-3,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?
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 17-3,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?
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SCENARIO 17-3
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 17-3,what is the highest rate of switching into the bundled offering?
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 17-3,what is the highest rate of switching into the bundled offering?
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SCENARIO 17-3
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 17-3,the first split occurs at what price?
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 17-3,the first split occurs at what price?
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SCENARIO 17-3
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 17-3,what is the lowest rate of switching into the bundled offering?
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 17-3,what is the lowest rate of switching into the bundled offering?
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SCENARIO 17-4
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 17-4,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.
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 17-4,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.
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SCENARIO 17-3
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 17-3,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.
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 17-3,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.
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SCENARIO 17-4
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 17-4,how many cities were used in generating the regression tree?
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 17-4,how many cities were used in generating the regression tree?
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SCENARIO 17-4
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 17-4,the first split occurs at $45 thousand spent on TV advertising.
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 17-4,the first split occurs at $45 thousand spent on TV advertising.
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SCENARIO 17-4
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 17-4,the highest mean weekend box office revenue is predicted to occur with $55 thousands spent on TV advertisement and 35 advertisement appearances a day.
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 17-4,the highest mean weekend box office revenue is predicted to occur with $55 thousands spent on TV advertisement and 35 advertisement appearances a day.
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SCENARIO 17-4
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 17-4,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 appear on TV?
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 17-4,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 appear on TV?
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SCENARIO 17-3
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 17-3,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.
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 17-3,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.
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SCENARIO 17-3
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 17-3,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.
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 17-3,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.
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SCENARIO 17-4
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 17-4,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.
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 17-4,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.
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SCENARIO 17-3
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 17-3,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.
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 17-3,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.
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SCENARIO 17-4
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 17-4,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.
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 17-4,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.
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SCENARIO 17-3
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 17-3,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.
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 17-3,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.
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SCENARIO 17-3
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 17-3,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.
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 17-3,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.
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SCENARIO 17-4
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 17-4,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.
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 17-4,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.
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SCENARIO 17-4
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 17-4,the first split occurs at 25 TV appearances a day of the advertisement.
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 17-4,the first split occurs at 25 TV appearances a day of the advertisement.
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SCENARIO 17-4
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 17-4,the highest mean weekend box office revenue is predicted to occur with $30 thousand spent on TV advertisement and 30 advertisement appearances a day.
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 17-4,the highest mean weekend box office revenue is predicted to occur with $30 thousand spent on TV advertisement and 30 advertisement appearances a day.
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Deck 17: Business Analytics
1
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
A)Predictive analytics
B)Prescriptive analytics
C)Productive analytics
D)Descriptive analytics
Prescriptive analytics
2
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.
False
3
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.
True
4
Dashboards may contain all but which of the following?
A)Treemaps
B)Gauges
C)Contingency table
D)Bullet graphs
A)Treemaps
B)Gauges
C)Contingency table
D)Bullet graphs
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5
Dashboards may contain all but which of the following?
A)Contingency table
B)Sparklines
C)Gauges
D)Bullet graphs
A)Contingency table
B)Sparklines
C)Gauges
D)Bullet graphs
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6
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.
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7
Some business analytics are performed by adding variables to see if unforeseen relationships are uncovered.
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8
Some consider bullet graphs little more than examples of chartjunk,even as many decision makers request them due to their visual appeal,due to the amount of the space they consume.
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9
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.
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10
In real-world business analytics,filtering is typically performed on large data based on complex conditional relationships.
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11
Some consider gauges little more than examples of chartjunk,even as many decision makers request them due to their visual appeal,due to the amount of the space it consumes.
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12
Some business analytics involve starting with many variables,followed by filtering the data by exploring specific combinations of categorical values or numerical range.
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13
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
A)Predictive analytics
B)Prescriptive analytics
C)Productive analytics
D)Descriptive analytics
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14
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
A)Predictive analytics
B)Prescriptive analytics
C)Productive analytics
D)Descriptive analytics
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15
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
A)Predictive analytics
B)Prescriptive analytics
C)Productive analytics
D)Descriptive analytics
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16
Which of the following disciplines is typically NOT involved in business analytics?
A)Economics
B)Statistics
C)Information system
D)Management science
A)Economics
B)Statistics
C)Information system
D)Management science
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17
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.
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18
You can compute any of the numerical descriptive statistics for the variables of the new worksheet that a drill-down in a PivotTable creates.
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19
Most information design specialists prefer bullet graphs over gauges because bullet graphs foster the direct comparison of each measurement.
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20
Double-clicking a cell in a PivotTable causes Excel to drill down and display the underlying data in a new worksheet.
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21
Which of the following is NOT one of the categories of predictive analytics methods?
A)Classification
B)Clustering
C)Association
D)Description
A)Classification
B)Clustering
C)Association
D)Description
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22
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
A)Principle component analysis
B)Neural networks
C)Cluster analysis
D)Multidimensional scaling
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23
SCENARIO 17-1
The table below contains the sparklines for the rates of return (in percentage)for three different stocks from 2007 to 2013.
Referring to Scenario 17-1,the sparklines enable you to draw conclusions on the historical trend of the rates of return of the three stocks.
The table below contains the sparklines for the rates of return (in percentage)for three different stocks from 2007 to 2013.
Referring to Scenario 17-1,the sparklines enable you to draw conclusions on the historical trend of the rates of return of the three stocks.
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24
Data mining uses various techniques to extract useful information from huge depositories of data.
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25
SCENARIO 17-2
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 17-2,the Western Europe region has the largest amount of business-to-consumer ecommerce sales last year.
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 17-2,the Western Europe region has the largest amount of business-to-consumer ecommerce sales last year.
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26
SCENARIO 17-2
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 17-2,which region has the slowest growth in business-to-consumer ecommerce sales last year?
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 17-2,which region has the slowest growth in business-to-consumer ecommerce sales last year?
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27
SCENARIO 17-2
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 17-2,which region has the largest amount of business-to-consumer ecommerce sales last year?
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 17-2,which region has the largest amount of business-to-consumer ecommerce sales last year?
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28
SCENARIO 17-1
The table below contains the sparklines for the rates of return (in percentage)for three different stocks from 2007 to 2013.
Referring to Scenario 17-1,the sparklines enable you to conclude that the rates of return of the stock market in general are volatile from 2007 to 2013.
The table below contains the sparklines for the rates of return (in percentage)for three different stocks from 2007 to 2013.
Referring to Scenario 17-1,the sparklines enable you to conclude that the rates of return of the stock market in general are volatile from 2007 to 2013.
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29
SCENARIO 17-2
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 17-2,which region has the fastest growth in business-to-consumer ecommerce sales last year?
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 17-2,which region has the fastest growth in business-to-consumer ecommerce sales last year?
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30
SCENARIO 17-2
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 17-2,the Middle East & Africa region has the slowest growth in business-to-consumer ecommerce sales last year.
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 17-2,the Middle East & Africa region has the slowest growth in business-to-consumer ecommerce sales last year.
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31
SCENARIO 17-1
The table below contains the sparklines for the rates of return (in percentage)for three different stocks from 2007 to 2013.
Referring to Scenario 17-1,the sparklines enable you to predict that the rates of return of the stock market in 2014 will be higher than in 2013.
The table below contains the sparklines for the rates of return (in percentage)for three different stocks from 2007 to 2013.
Referring to Scenario 17-1,the sparklines enable you to predict that the rates of return of the stock market in 2014 will be higher than in 2013.
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32
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.
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33
SCENARIO 17-2
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 17-2,the Asia Pacific region has the largest amount of business-to-consumer ecommerce sales last year.
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 17-2,the Asia Pacific region has the largest amount of business-to-consumer ecommerce sales last year.
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34
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
A)Neural networks
B)Cluster analysis
C)Factor analysis
D)Multidimensional scaling
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35
Which of the following is NOT one of the categories of predictive analytics methods?
A)Clustering
B)Recommendation
C)Association
D)Prediction
A)Clustering
B)Recommendation
C)Association
D)Prediction
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36
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
A)Neural networks
B)Simple component analysis
C)Cluster analysis
D)Multidimensional scaling
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37
SCENARIO 17-2
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 17-2,the North America region has the fastest growth in business-to-consumer ecommerce sales last year.
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 17-2,the North America region has the fastest growth in business-to-consumer ecommerce sales last year.
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38
SCENARIO 17-1
The table below contains the sparklines for the rates of return (in percentage)for three different stocks from 2007 to 2013.
Referring to Scenario 17-1,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.
The table below contains the sparklines for the rates of return (in percentage)for three different stocks from 2007 to 2013.
Referring to Scenario 17-1,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.
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39
SCENARIO 17-2
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 17-2,which region has the smallest amount of business-to-consumer ecommerce sales last year?
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 17-2,which region has the smallest amount of business-to-consumer ecommerce sales last year?
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40
Data mining is used mostly in the mining industry.
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41
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.
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42
In a regression tree,the dependent variable is a categorical variable.
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43
Successful implementation of a regression tree requires rules for deciding when a branch of the tree cannot be split any more.
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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.
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45
In a classification tree,the dependent variable is a categorical variable.
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46
The LogWorth statistic is used to decide when to split a node of a regression tree.
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47
The LogWorth statistic is a measure of the probability that can be attributed to the response that has occurred.
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48
Classification tree is not sensitive to the distribution of the independent variables.
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49
Successful implementation of a regression tree requires a method to provide prediction for the target variable at each of the nodes.
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50
Splitting of a node might be followed by pruning if necessary in a classification tree.
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51
The result of the regression tree is affected by the distribution of the independent variables.
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52
SCENARIO 17-3
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 17-3,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?
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 17-3,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?
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53
Splitting is always followed by pruning in a classification tree.
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54
SCENARIO 17-3
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 17-3,what is the highest rate of switching into the bundled offering?
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 17-3,what is the highest rate of switching into the bundled offering?
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55
The G 2
statistic is a measure of the probability that can be attributed to the
response that has occurred.
statistic is a measure of the probability that can be attributed to the
response that has occurred.
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56
Successful implementation of a classification tree requires rules for splitting the data at each node based on a dependent variable.
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57
SCENARIO 17-3
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 17-3,the first split occurs at what price?
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 17-3,the first split occurs at what price?
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58
Successful use of a regression tree requires a precise description of the parameters of the tree.
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59
SCENARIO 17-3
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 17-3,what is the lowest rate of switching into the bundled offering?
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 17-3,what is the lowest rate of switching into the bundled offering?
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60
Successful implementation of a classification tree requires rules for splitting the data at each node based on an independent variable.
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61
Multilayer perceptrons usually contain an input layer,a hidden layer and an output layer.
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62
SCENARIO 17-4
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 17-4,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.
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 17-4,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.
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63
SCENARIO 17-3
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 17-3,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.
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 17-3,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.
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64
SCENARIO 17-4
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 17-4,how many cities were used in generating the regression tree?
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 17-4,how many cities were used in generating the regression tree?
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65
SCENARIO 17-4
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 17-4,the first split occurs at $45 thousand spent on TV advertising.
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 17-4,the first split occurs at $45 thousand spent on TV advertising.
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66
SCENARIO 17-4
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 17-4,the highest mean weekend box office revenue is predicted to occur with $55 thousands spent on TV advertisement and 35 advertisement appearances a day.
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 17-4,the highest mean weekend box office revenue is predicted to occur with $55 thousands spent on TV advertisement and 35 advertisement appearances a day.
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67
Neural networks do not make any a priori assumption about the distribution of the data and,hence,are nonparametric methods.
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68
SCENARIO 17-4
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 17-4,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 appear on TV?
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 17-4,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 appear on TV?
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69
SCENARIO 17-3
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 17-3,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.
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 17-3,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.
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70
SCENARIO 17-3
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 17-3,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.
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 17-3,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.
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71
SCENARIO 17-4
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 17-4,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.
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 17-4,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.
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72
SCENARIO 17-3
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 17-3,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.
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 17-3,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.
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73
SCENARIO 17-4
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 17-4,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.
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 17-4,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.
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74
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.
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75
SCENARIO 17-3
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 17-3,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.
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 17-3,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.
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76
SCENARIO 17-3
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 17-3,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.
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 17-3,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.
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77
SCENARIO 17-4
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 17-4,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.
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 17-4,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.
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78
Neural networks require only training data but not validating data.
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79
SCENARIO 17-4
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 17-4,the first split occurs at 25 TV appearances a day of the advertisement.
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 17-4,the first split occurs at 25 TV appearances a day of the advertisement.
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80
SCENARIO 17-4
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 17-4,the highest mean weekend box office revenue is predicted to occur with $30 thousand spent on TV advertisement and 30 advertisement appearances a day.
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 17-4,the highest mean weekend box office revenue is predicted to occur with $30 thousand spent on TV advertisement and 30 advertisement appearances a day.
Unlock Deck
Unlock for access to all 113 flashcards in this deck.
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k this deck
Unlock Deck
Unlock for access to all 113 flashcards in this deck.
