In order to determine whether or not the number of automobiles sold per day (y) is related to price (x1 in $1000) and the number of advertising spots (x2), data were gathered for 7 days. Part of the regression results is shown below.
Coefficients
Standard Error
Constant0.8051
x10.4977
0.4617
x20.4733
0.0387
ANOVA df
SS
MS
FRegression 40.700
Error 1.016
?
a.Determine the least squares regression function relating y to x1 and x2.b.If the company charges $20,000 for each car and uses 10 advertising spots, how many cars would you expect them to sell in a day?c.At ? = .05, test to determine if the fitted equation developed in Part a represents a significant relationship between the independent variables and the dependent variable.d.Using ? = .05, test to see if price is a significant variable.e.At the 5% level, test to see if the number of advertising spots is a significant variable.f.Determine the multiple coefficient of determination.
What will be an ideal response?
| a. |  = .8051 - .4977x1 + .4733x2 |
| b. | 15.49, round to 16. |
| c. | F = 80.12; p-value < .01 (almost zero); reject H0; model is significant. |
| d. | t = 1.08; p-value is between .2 and .4; do not reject H0; no evidence that price is a significant variable. |
| e. | t = 12.23; p-value < .01; reject H0; the number of advertising spots is a significant variable. |
| f. | .9756 |
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What will be an ideal response?
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