Question

Use computer software packages, such as Minitab or Excel, to solve this problem.

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.

Use computer software packages, such as Minitab or Excel, to solve this problem. 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. Weekly Television Newspaper Gross Revenue Advertising Advertising ($1,000s) ($1,000s) ($1,000s) 105 5.0 1.5 90 2.0 2.0 95 4.0 1.5 92 2.5 2.5 94 3.0 3.3 94 3.5 2.3 94 2.5 4.2 98 3.0 2.5 a. Develop an estimated regression equation with the amount of television advertising as the independent variable (to 1 decimal). b. Develop an estimated regression equation with both television advertising and newspaper advertising as the independent variables (to 2 decimals). c. Is the estimated regression equation coefficient for television advertising expenditures the same in part (a) and in part (b)? d. Predict weekly gross revenue for a week when thousand is spent on television advertising and 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

Answer 1

a)

Regression Statistics
Multiple R	0.8516
R Square	0.7252
Adjusted R Square	0.6793
Standard Error	2.5817
Observations	8
ANOVA
	df	SS	MS	F	Significance F
Regression	1	105.5	105.5	15.83	0.0073
Residual	6	40.0	6.6651
Total	7	145.5
	Coefficients	Standard Error	t Stat	P-value	lower 95%	upper 95%
Intercept	82.377	3.362	24.504	0.0000	74.1508	90.60
X	4.039	1.015	3.979	0.0073	1.5549	6.5224

so, regression line is   Ŷ =   82.4   +   4.0   *x

b)

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.867745
R Square	0.752981
Adjusted R Square	0.654173
Standard Error	2.681092
Observations	8
ANOVA
	df	SS	MS	F	Significance F
Regression	2	109.5587	54.77936	7.620675	0.030327
Residual	5	35.94128	7.188256
Total	7	145.5
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	78.20258	6.566715	11.90893	7.36E-05	61.32231	95.08286	61.32231	95.08286
($1,000s)	4.568431	1.268661	3.600988	0.015527	1.307235	7.829626	1.307235	7.829626
($1,000s)	1.00426	1.338076	0.750525	0.486735	-2.43537	4.443893	-2.43537	4.443893

Y^=78.20+4.57*TV + 1.00*NEws

c)

NO

d) values are not given, provide values in comment section