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Regression Variables Entered/Removeda Model Variables Entered Variables Removed Method 1 Warranty_Yearsb . Enter a. Dependent Variable:...

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!)

business   Operations-Management   
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Homework Answers

Answer #1

a. What is the R square value?

From the output, it is can be seen that R-square value is 0.654.

What is your interpretation of this R square value?

The R square value indicates that 65.4% variation in the dependent variable (total products sold) is explained by the independent variables (products in a bundle).

Which one is the related SPSS table

The model summary

a. Is the regression significant? Why/why not?

Yes, the regression is significant because Sig value corresponding to F is less than 0.001

What does the significance/non-significance of this regression mean

The significance of this regression means that the regression model predicts the dependent variable significantly well.

Which one is the related SPSS table

ANOVA

a. What do the coefficients B0 and B1 refer to?

B0 refers to the y intercept of the regression equation.

B1 refers to the slope of the regression equation

According to the output, what are the values of these coefficients?

B0 = 0.972

B1 = 0.804

Are the coefficients significant?

B0 is significant as Sig. value corresponding to it is less than 0.001

B1 is significant as Sig. value corresponding to it is less than 0.001

What does the significance/non-significance of the coefficients mean?

The significance of the B1 mean that the number products in a bundle is significant predictor of total products sold.

Which one is the related SPSS table

Coefficients table

What is the effect of the number of products in a bundle on total products sold?

Since the value of regression coefficient corresponding to the number of products in a bundle is positive, it has positive impact on the total products sold.

According to the results of the regression analysis, what is the regression equation to predict different values of total products sold based on the number of products in a bundle?

Regression is given as follows:

Total products sold = 0.972 + 0.804*The number of products in a bundle

Predict the total number of products sold when the number of products bundled is:

If the number of products bundled is 10:

Estimated total products sold = 0.972 + 0.804*10

Total products sold = 9.012

If the number of products bundled is 15 is

Estimated total products sold = 0.972 + 0.804*15

Estimated total products sold = 13.032

If the number of products bundled is 20 is

Estimated total products sold = 0.972 + 0.804*20

Estimated total products sold = 17.052

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