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A corporation owns several companies. The strategic planner for the corporations believes dollars spend on advertising can to some extend be a predictor of total sales dollars. As an aid in long-term planning, she gathers the following sales and advertising information from several of the companies for 2017 (S millions). Advertising Sales 12.5 148 3.7 55 21.6 338 60.0 994 37.6 541 6.1 89 16.8 126 41.2 379 i) Based on the output given, develop the equation of the simple linear regression line to predict sales from advertising expenditures using this data. ii) Explain the values of r and r. iii) Predict the sales if the advertising expenditures is 50 ($ millions). iv) Do the data support the existence of a linear relationship between advertising expenditures and sales? Test using a = 0.05.

A corporation owns several companies. The strategic planner for the corporations believes dollars spend on advertising can to some extend be a predictor of total sales dollars. As an aid in long-term planning, she gathers the following sales and advertising information from several of the companies for 2017 (S millions). Advertising Sales 12.5 148 3.7 55 21.6 338 60.0 994 37.6 541 6.1 89 16.8 126 41.2 379 i) Based on the output given, develop the equation of the simple linear regression line to predict sales from advertising expenditures using this data. ii) Explain the values of r and r. iii) Predict the sales if the advertising expenditures is 50 ($ millions). iv) Do the data support the existence of a linear relationship between advertising expenditures and sales? Test using a = 0.05.

A First Course in Probability (10th Edition)
A First Course in Probability (10th Edition)
10th Edition
ISBN:9780134753119
Author:Sheldon Ross
Publisher:Sheldon Ross
Chapter1: Combinatorial Analysis
Section: Chapter Questions
Problem 1.1P: a. How many different 7-place license plates are possible if the first 2 places are for letters and...
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A corporation owns several companies. The strategic planner for the corporations believes dollars spend on advertising can to some extend be a predictor of total sales dollars. As an aid in long-term planning, she gathers the following sales and advertising information from several of the companies for 2017 ($ millions). Advertising Sales 12.5 148 3.7 55 21.6 338 60.0 994 37.6 541 6.1 89 16.8 126 41.2 379 i) Based on the output given, develop the equation of the simple linear regression line to predict sales from advertising expenditures using this data. ii) Explain the values of r and r. iii) Predict the sales if the advertising expenditures is 50 ($ millions). iv) Do the data support the existence of a linear relationship between advertising expenditures and sales? Test using a = 0.05.
Transcribed Image Text:A corporation owns several companies. The strategic planner for the corporations believes dollars spend on advertising can to some extend be a predictor of total sales dollars. As an aid in long-term planning, she gathers the following sales and advertising information from several of the companies for 2017 ($ millions). Advertising Sales 12.5 148 3.7 55 21.6 338 60.0 994 37.6 541 6.1 89 16.8 126 41.2 379 i) Based on the output given, develop the equation of the simple linear regression line to predict sales from advertising expenditures using this data. ii) Explain the values of r and r. iii) Predict the sales if the advertising expenditures is 50 ($ millions). iv) Do the data support the existence of a linear relationship between advertising expenditures and sales? Test using a = 0.05.
Variables Entered/Removed Variables Variables Model Entered Removed Method ADVERTISING Enter a. Dependent Variable: SALES b. All requested variables entered. Model Summary Adjusted R Std. Error of the Model R Square Square R Estimate 948 .898 881 108.75753 a. Predictors: (Constant), ADVERTISING ANOVA' Model Sum of Squares Mean Square Sig df F 1 Regression 625246.302 625246.302 52.861 .00 Residual 70969.198 6 11828.200 Total 696215.500 7 a. Dependent Variable: SALES b. Predictors: (Constant), ADVERTISING Coefficients Standardized Unstandardized Coefficients Coefficients Std. Error Model Beta Sig 1 (Constant) -46.292 64.891 -.713 502 ADVERTISING 15.240 2.096 948 7.271 000 a. Dependent Variable: SALES
Transcribed Image Text:Variables Entered/Removed Variables Variables Model Entered Removed Method ADVERTISING Enter a. Dependent Variable: SALES b. All requested variables entered. Model Summary Adjusted R Std. Error of the Model R Square Square R Estimate 948 .898 881 108.75753 a. Predictors: (Constant), ADVERTISING ANOVA' Model Sum of Squares Mean Square Sig df F 1 Regression 625246.302 625246.302 52.861 .00 Residual 70969.198 6 11828.200 Total 696215.500 7 a. Dependent Variable: SALES b. Predictors: (Constant), ADVERTISING Coefficients Standardized Unstandardized Coefficients Coefficients Std. Error Model Beta Sig 1 (Constant) -46.292 64.891 -.713 502 ADVERTISING 15.240 2.096 948 7.271 000 a. Dependent Variable: SALES
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