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SCENARIO 17-4 The Regression Tree Below Was Obtained for Predicting the Weekend

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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.
Business Analytics 17-15 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. Business Analytics 17-15   -True or False: 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.
-True or False: 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.


Definitions:

Significance F

A test statistic used in the analysis of variance (ANOVA) to determine if the means of several groups are all equal or if at least one is significantly different.

Fit

The degree to which a model, test, or value accurately reflects the expected outcomes or observations.

Homoscedasticity

A condition in regression analysis where the variance of the residuals, or errors, is constant across all levels of the independent variable.

Regression Line

A line of best fit through a dataset in linear regression analysis, showing the relationship between the independent and dependent variables.

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