Pete Zaria would like to study the relationship between pizza sales and advertis
ID: 3154735 • Letter: P
Question
Pete Zaria would like to study the relationship between pizza sales and advertising. The following is the result of a regression analysis Pete conducted for monthly sales of pizza and advertising (both in thousands of dollars) The exercise involves filling in the values for the shaded cells bellow. SUMMARY OUTPUT Regression Statistics Multiple R 0.3070 R Square Adjusted R Square 0.0754 Standard Error Observations ANOVA df SS MS F Significance F Regression 1 3.787117275 0.055498056 Residual 48 38.5738145 Total 49 2044.1752 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 72.56070102 2.145887781 33.81383765 2.56285E-52 68.24610447 76.87529758 ADVERT 2.305215477 (x x)² = 36.2498 23 The predicted sales when $2 (thousand) is spent on advertising is: a 59.20 b 69.65 c 73.31 d 77.17 24 The value for SSE is, a 1925.60482 b 1851.543096 c 1777.481372 d 1706.382117 25 The observed SALES (y) deviate from the predicted SALES (y), on average, by, a 6.770 b 6.211 c 4.969 d 3.975 26 The fraction of the variations is SALES explained by advertising is, a 0.094 b 0.179 c 0.519 d 0.906 27 Given (x x)² = 36.2498, the value of the standard error of the slope coefficient in (6) is: a 3.685 b 1.950 c 1.032 d 0.743 28 The value of the t Stat to test the hypothesis that advertising has no impact on sales is, a 1.788 b 2.235 c 2.682 d 2.950 29 The p-Value for the above hypothesis is (approximately), a 0.07 b 0.05 c 0.03 d 0.01 30 The lower and upper ends of a 95% confidence interval for the population slope parameter are: a 1.285 3.325 b 0.985 3.626 c 0.613 3.997 d 0.231 4.380 Pete Zaria would like to study the relationship between pizza sales and advertising. The following is the result of a regression analysis Pete conducted for monthly sales of pizza and advertising (both in thousands of dollars) The exercise involves filling in the values for the shaded cells bellow. SUMMARY OUTPUT Regression Statistics Multiple R 0.3070 R Square Adjusted R Square 0.0754 Standard Error Observations ANOVA df SS MS F Significance F Regression 1 3.787117275 0.055498056 Residual 48 38.5738145 Total 49 2044.1752 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 72.56070102 2.145887781 33.81383765 2.56285E-52 68.24610447 76.87529758 ADVERT 2.305215477 (x x)² = 36.2498 23 The predicted sales when $2 (thousand) is spent on advertising is: a 59.20 b 69.65 c 73.31 d 77.17 24 The value for SSE is, a 1925.60482 b 1851.543096 c 1777.481372 d 1706.382117 25 The observed SALES (y) deviate from the predicted SALES (y), on average, by, a 6.770 b 6.211 c 4.969 d 3.975 26 The fraction of the variations is SALES explained by advertising is, a 0.094 b 0.179 c 0.519 d 0.906 27 Given (x x)² = 36.2498, the value of the standard error of the slope coefficient in (6) is: a 3.685 b 1.950 c 1.032 d 0.743 28 The value of the t Stat to test the hypothesis that advertising has no impact on sales is, a 1.788 b 2.235 c 2.682 d 2.950 29 The p-Value for the above hypothesis is (approximately), a 0.07 b 0.05 c 0.03 d 0.01 30 The lower and upper ends of a 95% confidence interval for the population slope parameter are: a 1.285 3.325 b 0.985 3.626 c 0.613 3.997 d 0.231 4.380Explanation / Answer
23. The predicted sales when $2 (thousand) is spent on advertising is: 72.56070102+2*2.305215477 = 77.17113. Or option d.
24. The value for SSE is,38.5738145*48 = 1851.543, or option b.
25.
The observed SALES (y) deviate from the predicted SALES (y), on average, by, 3.975, or option d.
26.
The fraction of the variations is SALES explained by advertising is,
0.3070
27.
Given (x x)² = 36.2498, the value of the standard error of the slope coefficient in (6) is: 1.032.
Obtained by sqrt(38.5738145/36.2498).
28.
The value of the t Stat to test the hypothesis that advertising has no impact on sales is,
2.235
(Obtained by 2.305215477/1.032)
29.
The p-Value for the above hypothesis is (approximately), 0.01.
30.
The lower and upper ends of a 95% confidence interval for the population slope parameter are:
0.231
4.380
The lower and upper ends of a 95% confidence interval for the population slope parameter are:
23. The predicted sales when $2 (thousand) is spent on advertising is: 72.56070102+2*2.305215477 = 77.17113. Or option d.
24. The value for SSE is,38.5738145*48 = 1851.543, or option b.
25.
The observed SALES (y) deviate from the predicted SALES (y), on average, by, 3.975, or option d.
26.
The fraction of the variations is SALES explained by advertising is,
0.3070
27.
Given (x x)² = 36.2498, the value of the standard error of the slope coefficient in (6) is: 1.032.
Obtained by sqrt(38.5738145/36.2498).
28.
The value of the t Stat to test the hypothesis that advertising has no impact on sales is,
2.235
(Obtained by 2.305215477/1.032)
29.
The p-Value for the above hypothesis is (approximately), 0.01.
30.
The lower and upper ends of a 95% confidence interval for the population slope parameter are:
0.231
4.380
The lower and upper ends of a 95% confidence interval for the population slope parameter are:
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