The value of R-square indicates the amounts of variation in the dependent variable that is explained by the independent variable
You have been provided with the following regression output. The dependent variable is unit sales of a product, and the independent variable is the dollar value of newspaper advertising for that product. The unit of analysis is weeks; that is, each of the observations in the underlying sample data represents a separate week of sales and newspaper advertising.
Regression Analysis: Sales versus Newspaper
The regression equation is
Sales = 6514 + 0.0386 Newspaper
|
Predictors
|
Co-eff
|
SECo-eff
|
T
|
P
|
|
Constant
|
6514
|
1138
|
5.72
|
0.000
|
|
Newspaper
|
0.038625
|
0.005494
|
7.03
|
0.00
|
S = 5630.70 R-Sq = 24.3% R-Sq(adj) = 23.8%
Analysis of Variance
|
Source
|
D F
|
S S
|
M S
|
F
|
P
|
|
Regression
|
1
|
1567134272
|
1567134272
|
49.43
|
0.000
|
|
Residual Error
|
154
|
4882532724
|
31704758
|
|
|
|
Total
|
155
|
6449666996
|
|
|
|
How much variability would you have if you predict sales with this model?
What would be your best point estimate for average Sales in weeks for which Newspaper advertising were $1 million? What would be a suitable interval estimate for this? (Use 95% limits.)