Regression

Variables Entered/Removeda

Model Variables Entered Variables Removed Method

1 Warranty_Yearsb . Enter

a. Dependent Variable: Number_of_people_mentioned

b. All requested variables entered.

Model Summary

Model R R Square Adjusted R Square Std. Error of the Estimate

1 .503a .253 .251 .95930

a. Predictors: (Constant), Warranty_Years

ANOVAa

Model Sum of Squares df Mean Square F Sig.

1 Regression 80.590 1 80.590 87.574 .000b Residual 237.425 258 .920 Total 318.015 259

a. Dependent Variable: Number_of_people_mentioned

b. Predictors: (Constant), Warranty_Years

Coefficientsa

Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta

1 (Constant) 2.661 .303 8.793 .000 Warranty_Years .532 .057 .503 9.358 .000

a. Dependent Variable: Number_of_people_mentioned

4. a. What is the R square value? (5 pts)

b. What is your interpretation of this R square value? (5 pts.)

c. Add the related SPSS table (5 pts.)

5. a. Is the regression significant? Why/why not? (5 pts.)

b. What does the significance/non-significance of this regression mean? (5 pts.)

c. Add the related SPSS table (5 pts.)

6. a. What do the coefficients B0 and B1 refer to? (5 pts.)

b. According to the output, what are the values of these coefficients? (5 pts.)

c. Are the coefficients significant? (5 pts.)

d. What does the significance/non-significance of the coefficients mean? (5 pts.)

e. Add the related SPSS table (5 pts.)

7. What is the effect of the warranty time (years) on the number of people to whom customers tell about the retailer store? (5 pts.)

8. According to the results of the regression analysis, what is the regression equation to predict different values of number of people to whom customers mention about the retailer store based on the warranty time (years)? (5 pts.)

9. Predict the total number of people to whom the retailer store is mentioned when the warranty time (years) is:

a. 7 (5 pts.)

b. 8 (5 pts.)

c. 9 (5 pts.)

(Show all your work!)