b. Use a = .05 to test the significance of B₁. Compute the t test statistic (to 2 decimals). What is the p-value? Less than .01 What is your conclusion? Significant relationship between television advertising and revenue Should +₁ be dropped from the model? No, x1 should not be dropped from the model

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1
23456780 HARGA NNENK
9
10
14
15
16
17
18
11
12 Predicted Revenue
13
100.2570861
19
20
21
22
23
24
A
25
Weekly Gross
Revenue (1000s)
96
91
95
92
95
95
95
94
B
Television
Advertising
($1000s)
5
2
4
2.5
3
3.5
2.5
3
5.2
C
Newspaper
Advertising
($1000s)
1.5
2
1.5
min
2.5
a 3 3 3
3.3
2.3
4.2
2.5
3.9
D
SUMMARY OUTPUT
Regression Statistics
Multiple R
E
R Square
Adjusted R Square
Standard Error
Observations
ANOVA
Regression
Residual
Total
Intercept
Television Advertising ($1000s)
Newspaper Advertising ($1000s)
F
0.965731
0.932636
0.905691
0.530324
df
8
G
H
SS
MS
2 19.46878 9.734392
5 1.406216 0.281243
7 20.875
F
|
J
Significance F
34.612 0.001178
K
L
M
Coefficient Standard It Stat P-value Lower 95% Upper 95% Lower 99% Upper 99%
84.11544 1.298905 64.75872 1.66E-08 80.7765 87.45439 78.87807 89.35281
2.081583 0.250943 8.295049 0.000416 1.436514 2.726651 1.069745 3.09342
1.363439 0.264673 5.151407 0.003611 0.683075 2.043804 0.296239 2.43064
Transcribed Image Text:1 23456780 HARGA NNENK 9 10 14 15 16 17 18 11 12 Predicted Revenue 13 100.2570861 19 20 21 22 23 24 A 25 Weekly Gross Revenue (1000s) 96 91 95 92 95 95 95 94 B Television Advertising ($1000s) 5 2 4 2.5 3 3.5 2.5 3 5.2 C Newspaper Advertising ($1000s) 1.5 2 1.5 min 2.5 a 3 3 3 3.3 2.3 4.2 2.5 3.9 D SUMMARY OUTPUT Regression Statistics Multiple R E R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept Television Advertising ($1000s) Newspaper Advertising ($1000s) F 0.965731 0.932636 0.905691 0.530324 df 8 G H SS MS 2 19.46878 9.734392 5 1.406216 0.281243 7 20.875 F | J Significance F 34.612 0.001178 K L M Coefficient Standard It Stat P-value Lower 95% Upper 95% Lower 99% Upper 99% 84.11544 1.298905 64.75872 1.66E-08 80.7765 87.45439 78.87807 89.35281 2.081583 0.250943 8.295049 0.000416 1.436514 2.726651 1.069745 3.09342 1.363439 0.264673 5.151407 0.003611 0.683075 2.043804 0.296239 2.43064
b. Use a = .05 to test the significance of ₁. Compute the t test statistic (to 2 decimals).
What is the p-value?
Less than .01
What is your conclusion?
Significant relationship between television advertising and revenue
Should *₁ be dropped from the model?
No, x1 should not be dropped from the model
c. Use α = .05 to test the significance of 2. Compute the t test statistic (to 2 decimals).
What is the p-value?
Less than .01
What is your conclusion?
Significant relationship between newspaper advertising and revenue
Should x2 be dropped from the model?
No, x2 should not be dropped from the model ✔
d. The owner plans to spend (in $1000s) $5.2 on television advertising and $3.9 on newspaper
advertising. What is the expected revenue (in $1000s) for this advertising combination (to the nearest
whole number)?
LA
Transcribed Image Text:b. Use a = .05 to test the significance of ₁. Compute the t test statistic (to 2 decimals). What is the p-value? Less than .01 What is your conclusion? Significant relationship between television advertising and revenue Should *₁ be dropped from the model? No, x1 should not be dropped from the model c. Use α = .05 to test the significance of 2. Compute the t test statistic (to 2 decimals). What is the p-value? Less than .01 What is your conclusion? Significant relationship between newspaper advertising and revenue Should x2 be dropped from the model? No, x2 should not be dropped from the model ✔ d. The owner plans to spend (in $1000s) $5.2 on television advertising and $3.9 on newspaper advertising. What is the expected revenue (in $1000s) for this advertising combination (to the nearest whole number)? LA
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