Please create your output in Excel, Copy it to Microsoft Word and answer the que
ID: 3110863 • Letter: P
Question
Please create your output in Excel, Copy it to Microsoft Word and answer the questions below. Everything should be in one word file. Please copy and paste the excel output created as the last page of the assignment, after the answers to the questions.
The owner of Showtime Movie Theaters, Inc., would like to estimate weekly gross revenue as a function of advertising expenditures. Historical data for a sample of eight weeks follows.
Weekly
Gross Revenue
($1000s)
Television Advertising
($1000s)
Newspaper
Advertising
($1000)
96
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
94
3.0
2.5
How many independent variables are there?
List and label each independent variable (x, x, etc.)
Develop a simple linear regression equation using ONLY the amount of television as the independent variable. (Include this output)
Develop a simple linear regression equation using ONLY the newspaper advertising as the independent variable. (Include this output)
Develop a multiple regression equation using the amount of television and newspaper advertising as the independent variables. (Include this output)
Answer the following questions based on the multiple regression output ONLY!!
What is the proportion of variation in Weekly Gross Revenue due to television advertising and newspaper advertising?
What is the strength of the linear relationship between the amount of television, newspaper advertising and weekly gross revenue?
List the SSR, SSE, SST, MSR, MSE.
Give the value of F.
What is the p value for this regression model? P = (two decimal places)
Is this model useful? If so, why and if not, why not. If the model is useful, proceed to question 12.
If the model is useful, estimate the weekly gross revenue for a week when $3500 is spent on television advertising and $1800 is spent on newspaper advertising?
Are each of the variables good for the model? List their p values and explain your answer.
Weekly
Gross Revenue
($1000s)
Television Advertising
($1000s)
Newspaper
Advertising
($1000)
96
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
94
3.0
2.5
Explanation / Answer
There are 3 independent variables
Let x1=Weekly gross revenue
x2=television advertising
x3=Newspaper A dvertising
simple linear regression equation using ONLY the amount of television as the independent variable.
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.807807
R Square
0.652553
Adjusted R Square
0.594645
Standard Error
1.215175
Observations
8
ANOVA
df
SS
MS
F
Significance F
Regression
1
16.6401
16.6401
11.26881
0.015288
Residual
6
8.859903
1.476651
Total
7
25.5
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
88.63768
1.582367
56.01588
2.17E-09
84.76577
92.50959
84.76577
92.50959
X Variable 1
1.603865
0.477781
3.356905
0.015288
0.434777
2.772952
0.434777
2.772952
Regression equation is: y= 88.63768a+1.603865b
simple linear regression equation using ONLY the newspaper advertising as the independent variable.
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.02053
R Square
0.000421
Adjusted R Square
-0.16617
Standard Error
2.061118
Observations
8
ANOVA
df
SS
MS
F
Significance F
Regression
1
0.010748
0.010748
0.00253
0.961517
Residual
6
25.48925
4.248209
Total
7
25.5
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Intercept
93.85641
2.237446
41.94801
1.23E-08
88.38157
99.33124
88.38157
X Variable 1
-0.04299
0.854728
-0.0503
0.961517
-2.13444
2.048452
-2.13444
Regression equation: y= 93.86a-0.043b
multiple regression equation using the amount of television and newspaper advertisingas the independent variables
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.958663
R Square
0.919036
Adjusted R Square
0.88665
Standard Error
0.642587
Observations
8
ANOVA
df
SS
MS
F
Significance F
Regression
2
23.43541
11.7177
28.37777
0.001865
Residual
5
2.064592
0.412918
Total
7
25.5
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
83.23009
1.573869
52.88248
4.57E-08
79.18433
87.27585
79.18433
87.27585
X Variable 1
2.290184
0.304065
7.531899
0.000653
1.508561
3.071806
1.508561
3.071806
X Variable 2
1.300989
0.320702
4.056697
0.009761
0.476599
2.125379
0.476599
2.125379
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.807807
R Square
0.652553
Adjusted R Square
0.594645
Standard Error
1.215175
Observations
8
ANOVA
df
SS
MS
F
Significance F
Regression
1
16.6401
16.6401
11.26881
0.015288
Residual
6
8.859903
1.476651
Total
7
25.5
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
88.63768
1.582367
56.01588
2.17E-09
84.76577
92.50959
84.76577
92.50959
X Variable 1
1.603865
0.477781
3.356905
0.015288
0.434777
2.772952
0.434777
2.772952
Regression equation is: y= 88.63768a+1.603865b
simple linear regression equation using ONLY the newspaper advertising as the independent variable.
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.02053
R Square
0.000421
Adjusted R Square
-0.16617
Standard Error
2.061118
Observations
8
ANOVA
df
SS
MS
F
Significance F
Regression
1
0.010748
0.010748
0.00253
0.961517
Residual
6
25.48925
4.248209
Total
7
25.5
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Intercept
93.85641
2.237446
41.94801
1.23E-08
88.38157
99.33124
88.38157
X Variable 1
-0.04299
0.854728
-0.0503
0.961517
-2.13444
2.048452
-2.13444
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