Excel Results for multiple linear regression
SUMMARY OUTPUT
|
Regression Statistics |
|
Multiple R |
0.899159 |
| R
Square |
0.808488 |
|
Adjusted R Square |
0.776569 |
|
Standard Error |
7.453419 |
|
Observations |
15 |
|
ANOVA |
|
|
|
|
|
|
|
df |
SS |
MS |
F |
Significance F |
|
Regression |
2 |
2814.292 |
1407.146 |
25.32959 |
4.93E-05 |
|
Residual |
12 |
666.6414 |
55.55345 |
|
|
|
Total |
14 |
3480.933 |
|
|
|
|
|
Coefficients |
Standard Error |
t Stat |
P-value |
|
Intercept |
284.8645 |
22.03959 |
12.92513 |
2.11E-08 |
|
Price |
-46.6048 |
6.807572 |
-6.84603 |
1.78E-05 |
|
Competitor Price (Cprice) |
22.39825 |
6.962266 |
3.217092 |
0.007394 |
The regression equation is
Sales = 284.865 - 46.605 Price + 22.398 Cprice
Increase in 1 unit of Cprice increases the sales by 22.398 units
keeping Price constant.
Increase in 1 unit of Price the sales of computer decreases by
46.605 units keeping the Cprice constant.
R2 = 0.8085 which means the model explains 80.85%
variability in sales of computers with Price and Cprice as
independent variables.
