Regression Analysis for Predicting Units Sold per Store per Week

Computer Sciences and Information Technology
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Regression analysis
We ran a regression analysis to determine which of the factors predict the number of units sold
per store per week. As can be seen below, the value of R-squared is 0.6726; this implies that
approximately 67.26% of the variation in the number of units sold per store per week is
explained by the factors in the model.
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.8201
43
R Square
0.6726
35
Adjusted R
Square
0.6707
33
Standard Error
63.693
03
Observations 1386
In the ANOVA table below the p-value is 0.000 (a value greater than 5% level of significance),
we therefore reject the null hypothesis and conclude that the model is different from zero hence
fit to predict the dependent variable.
ANOVA
df SS MS F
Significance
F
Regression 8 11477979 1434747 353.6647 0
Residual 1377 5586216 4056.801
Total 1385 17064195
Coefficients
Standard
Error t Stat P-value
Lower
95%
Upper
95%
Intercept 298.4881 16.18309 18.44444 4.62E-68 266.7419 330.2343
Average Retail Price -28.5354 3.952153 -7.22021 8.56E-13 -36.2883 -20.7825
Sales Rep 77.43691 3.864453 20.03826 1.39E-78 69.85606 85.01777