Assignment
1. Cascade Pharmaceuticals Company developed the following regression model, using time-series data from the past 33 quarters, for one of its nonprescription cold remedies:
Dep. Var N R R2 Adjusted R2 Standard error of estimate
SALES (Y) 33 0.64(a) 0.615 0.604 27.87
Un-standardised Coefficients Standardised Coefficients
Model 1 B Std. Error Beta T p-value
Constant -1.041 1.513 0.635 -0.688 0.879
X1 0.239 0.032 0.480 7.469 0.001*
X2 -0.26 0.070 -0.202 -3.714 0.002*
a Predictors: (Constant), X1, X2
b Dependent Variable: Y (SALES)
*Significant at the p
Model Sum of Squares df Mean Square F p-value
Regression 275.83 2 90291.32 31.402 0.001*
Residual 195.28 30 903.81
Total 470.11 33
a Predictors: (Constant), X1,X2
b Dependent Variable: Y (SALES)
*Significant at the p
Where;
Y = quarterly sales (in thousands of cases) of the cold remedy
X1 = Cascade's quarterly advertising (multiply $1,000) for the cold remedy
X2= competitors' advertising for similar product (multiply $10,000)
a. Which of the independent variables (if any) appears to be statistically significant (at 0.05 level) in explaining sales of the cold remedy?
b. What proportion of the total variation in sales is explained by the regression equations?
c. Perform F-test (at 0.05 level) of the overall explanatory power of the model. Explain.
d. What additional statistical information (if any) would you find useful in the evaluation of this model? Explain.
The response should include a reference list. Double-space, using Times New Roman 12 pnt font, one-inch margins, and APA style of writing and citations.