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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 milions). 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. i) 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. OUTPUT Variables Entered/Removed Variables Variables Model Entered Removed Method ADVERTISING Enter a. Dependent Variable: SALES b. All requested variables entered. Model Summary Adusted R Std. Error of the Model R R Square Square Estimate 948 898 881 108.75753 a. Predictors: (Constant), ADVERTISING ANOVA Model Mean Square Sum of Squares of Sig Regression 625246.302 625246.302 52.861 000 Residual 70909.198 11828 200 Total 696215 500 a. Dependent Variable: SALES b. Predictors: (Constant). ADVERTISING Coefficients Standardized Coefficients Unstandardized Coefficients Model Std. Error Beta (Constant) ADVERTISING 1 46.292 64.891 713 502 15 240 2 090 948 7.271 000 a. Dependent Variable: SALES

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 milions). 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. i) 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. OUTPUT Variables Entered/Removed Variables Variables Model Entered Removed Method ADVERTISING Enter a. Dependent Variable: SALES b. All requested variables entered. Model Summary Adusted R Std. Error of the Model R R Square Square Estimate 948 898 881 108.75753 a. Predictors: (Constant), ADVERTISING ANOVA Model Mean Square Sum of Squares of Sig Regression 625246.302 625246.302 52.861 000 Residual 70909.198 11828 200 Total 696215 500 a. Dependent Variable: SALES b. Predictors: (Constant). ADVERTISING Coefficients Standardized Coefficients Unstandardized Coefficients Model Std. Error Beta (Constant) ADVERTISING 1 46.292 64.891 713 502 15 240 2 090 948 7.271 000 a. Dependent Variable: SALES

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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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. i) Explain the values of r and r. i) 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. OUTPUT Variables Entered/Removed Variables Variables Model Entered ADVERTISING Removed Method Enter a. Dependent Variable: SALES b. All requested variables entered. Model Summary Adusted R Std. Error of the Model R R Square Square Estimate .948 890 881 108.75753 a. Predictors: (Constant). ADVERTISING ΑΝOVA Model Mean Square 625246.302 Sum of Squares df F Sig. Regression 625246.302 52.861 000 Residual 70909.198 11828.200 Total 696215 500 a. Dependent Variable: SALES b. Predictors: (Constant), ADVERTISING Coefficients Standardized Unstandardized Coefficients Std. Error Coefficients Model B Beta Sa (Constant) ADVERTISING 46.292 64.891 713 502 15 240 2096 948 7.271 000 a. Dependent Variable: SALES
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. i) Explain the values of r and r. i) 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. OUTPUT Variables Entered/Removed Variables Variables Model Entered ADVERTISING Removed Method Enter a. Dependent Variable: SALES b. All requested variables entered. Model Summary Adusted R Std. Error of the Model R R Square Square Estimate .948 890 881 108.75753 a. Predictors: (Constant). ADVERTISING ΑΝOVA Model Mean Square 625246.302 Sum of Squares df F Sig. Regression 625246.302 52.861 000 Residual 70909.198 11828.200 Total 696215 500 a. Dependent Variable: SALES b. Predictors: (Constant), ADVERTISING Coefficients Standardized Unstandardized Coefficients Std. Error Coefficients Model B Beta Sa (Constant) ADVERTISING 46.292 64.891 713 502 15 240 2096 948 7.271 000 a. Dependent Variable: SALES
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