The owner of Showtime Movie Theaters, Inc., would like to predict weekly gross r

Question

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 follow.

Develop an estimated regression equation with the amount of television advertising as the independent variable (to 1 decimal).
Revenue =   +   TVAdv

Develop an estimated regression equation with both television advertising and newspaper advertising as the independent variables (to 2 decimals).
Revenue =   +   TVAdv +   NewsAdv

Is the estimated regression equation coefficient for television advertising expenditures the same in part (a) and in part (b)?
SelectYes, the coefficients are the sameNo, the coefficients are not the sameItem 6  

Predict weekly gross revenue for a week when $3.6 thousand is spent on television advertising and $1.8 thousand is spent on newspaper advertising?

NOTE: To compute the predicted revenues, use the coefficients you have computed rounded to two decimals, as you have entered them here. Then, also round your predicted revenue to two decimal places.
$  in thousands

Weekly Television Newspaper Gross Revenue Advertising Advertising ($1,000s) ($1,000s) ($1,000s) 102 5.0 1.5 90 2.0 2.0 95 4.0 1.5 92 2.5 2.5 95 3.0 3.3 94 3.5 2.3 94 2.5 4.2 104 3.0 2.5

Explanation / Answer

we solve the regression in Excel

we go in Excel to Data - Data analysis - Regression

a] The estimated regression equation with the amount of television advertising as the independent variable is,

Revenue = 86.20 + 2.99 TVAdv

b] The estimated regression equation with both television advertising and newspaper advertising as the independent variables is,

Revenue = 81.67 + 3.56 TVAdv + 1.08 NewsAdv

c] No, the coefficients are not the same

d] in summary of answer c] we can see that the P value is 0.29 is grether than 0.05

hence regression coefficients are not significant

If regression coefficients is not significant, we cannot use regression equation, but we use mean of response as a best predictor.

SUMMARY OUTPUT Regression Statistics Multiple R 0.599439372 R Square 0.35932756 Adjusted R Square 0.25254882 Standard Error 4.152681845 Observations 8 ANOVA df SS MS F Significance F Regression 1 58.03140097 58.0314 3.36516 0.116271 Residual 6 103.468599 17.24477 Total 7 161.5 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 86.20289855 5.407506432 15.94134 3.87E-06 72.97121 99.43459 72.97121 99.43459 TVAdv 2.995169082 1.632745425 1.834437 0.116271 -1.00002 6.990353 -1.00002 6.990353

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