The owner of Showtime Movie Theaters, Inc., would like to predict weekly gross revenue as a...

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The owner of Showtime Movie Theaters, Inc., would like to predict weekly gross revenue as a function of advertising expenditures. Historical data for a sample of eight weeks are entered into the Microsoft Excel Online file below. Use the XLMiner Analysis ToolPak to perform your regression analysis in the designated areas of the spreadsheet. Due to a recent change by Microsoft you will need to open the XLMiner Analysis ToolPak add-in manually from the home ribbon. Screenshot of ToolPak Open spreadsheet a. Develop an estimated regression equation with the amount of television advertising as the independent variable (to 2 decimals). Revenue = + TVAdv b. Develop an estimated regression equation with both television advertising and newspaper advertising as the independent variables (to 2 decimals). Revenue = + TVAdv + NewsAdv c. Is the estimated regression equation coefficient for television advertising expenditures the same in part (a) and in part (b)? A Weekly Gross Revenue B C D E F G 17 Television Advertising Newspaper Advertising 1 (1000s) ($1000s) ($1000s) 2 3 4 5 6 7 8 9 00 OF23456789 20 22283889 97 90 96 95 94 94 94 0266464 32 1.5 5 1.5 2.5 2.5 3.3 3.5 2.3 2.5 4.2 2.5 52553325 R Square Observations Regression Statistics Multiple R 0.904826025 0.818710135 Adjusted R Square 0.788495157 Standard Error 1.013562646 8 0 ANOVA 1 Predicted Revenue (Part D) 98.43 Regression Residual Total df SS 1 7 5.3 3.9 7 Formula for Predicted Revenue 19 =ROUND(F37+B15*F38+C15*F3 Intercept Television Advertising ($1000s) MS H J K L M F Significance F 27.83614458 27.83614458 27.09616888 0.002004322 6 6.163855422 1.027309237 34 Coefficients Standard Error t Stat P-value 88.59759036 1.465060241 Lower 95% Upper 95% Lower 95% Upper 95% 1.097971403 80.69207463 2.44E-10 85.91095113 91.28422959 85.91095113 91.28422959 0.281450184 5.205398052 0.002004322 0.776376451 2.153744031 0.776376451 2.153744031 28 29 =0 #1 #2 #5 =6 #7 =9 22 12345ONGO-MOTO Regression Statistics Multiple R 0.976487619 R Square 0.953528069 26 Adjusted R Square 0.934939297 27 Standard Error 0.562146892 Observations 8 ANOVA Regression Residual df SS 2 MS F 32.41995436 16.20997718 51.29591441 5 1.58004564 0.316009128 Total 7 34 8 Intercept Television Advertising ($1000s) 1.826636846 Coefficients Standard Error t Stat 84.69236271 1.19257281 7.10E+01 0.182702103 9.99789721 P-value Newspaper Advertising ($1000s) 1.039157139 0.272846097 3.808583484 Significance F 0.000465561 Lower 95% Upper 95% Lower 95% Upper 95% 1.05E-08 81.62675722 87.7579882 81.62675722 87.7579682 0.00017112 1.356986139 2.296287553 1.356986139 2.296287553 0.01251816 0.337783918 1.740530361 0.337783918 1.740530361 The owner of Showtime Movie Theaters, Inc., would like to predict weekly gross revenue as a function of advertising expenditures. Historical data for a sample of eight weeks are entered into the Microsoft Excel Online file below. Use the XLMiner Analysis ToolPak to perform your regression analysis in the designated areas of the spreadsheet. Due to a recent change by Microsoft you will need to open the XLMiner Analysis ToolPak add-in manually from the home ribbon. Screenshot of ToolPak Open spreadsheet a. Develop an estimated regression equation with the amount of television advertising as the independent variable (to 2 decimals). Revenue = + TVAdv b. Develop an estimated regression equation with both television advertising and newspaper advertising as the independent variables (to 2 decimals). Revenue = + TVAdv + NewsAdv c. Is the estimated regression equation coefficient for television advertising expenditures the same in part (a) and in part (b)? A Weekly Gross Revenue B C D E F G 17 Television Advertising Newspaper Advertising 1 (1000s) ($1000s) ($1000s) 2 3 4 5 6 7 8 9 00 OF23456789 20 22283889 97 90 96 95 94 94 94 0266464 32 1.5 5 1.5 2.5 2.5 3.3 3.5 2.3 2.5 4.2 2.5 52553325 R Square Observations Regression Statistics Multiple R 0.904826025 0.818710135 Adjusted R Square 0.788495157 Standard Error 1.013562646 8 0 ANOVA 1 Predicted Revenue (Part D) 98.43 Regression Residual Total df SS 1 7 5.3 3.9 7 Formula for Predicted Revenue 19 =ROUND(F37+B15*F38+C15*F3 Intercept Television Advertising ($1000s) MS H J K L M F Significance F 27.83614458 27.83614458 27.09616888 0.002004322 6 6.163855422 1.027309237 34 Coefficients Standard Error t Stat P-value 88.59759036 1.465060241 Lower 95% Upper 95% Lower 95% Upper 95% 1.097971403 80.69207463 2.44E-10 85.91095113 91.28422959 85.91095113 91.28422959 0.281450184 5.205398052 0.002004322 0.776376451 2.153744031 0.776376451 2.153744031 28 29 =0 #1 #2 #5 =6 #7 =9 22 12345ONGO-MOTO Regression Statistics Multiple R 0.976487619 R Square 0.953528069 26 Adjusted R Square 0.934939297 27 Standard Error 0.562146892 Observations 8 ANOVA Regression Residual df SS 2 MS F 32.41995436 16.20997718 51.29591441 5 1.58004564 0.316009128 Total 7 34 8 Intercept Television Advertising ($1000s) 1.826636846 Coefficients Standard Error t Stat 84.69236271 1.19257281 7.10E+01 0.182702103 9.99789721 P-value Newspaper Advertising ($1000s) 1.039157139 0.272846097 3.808583484 Significance F 0.000465561 Lower 95% Upper 95% Lower 95% Upper 95% 1.05E-08 81.62675722 87.7579882 81.62675722 87.7579682 0.00017112 1.356986139 2.296287553 1.356986139 2.296287553 0.01251816 0.337783918 1.740530361 0.337783918 1.740530361