Regression Case Study

MRP Biscuits – Regression Case Answers 1. Multiple regression was run through SPSS and the results of it in the form of tables have been shown below, named after their significance level. (0.1, 0.05, 0.01)

Multiple Regression analysis At 0.1 % significance level SPSS Output

Regression Equation of Preference Preference = 0.7329 + 0.2947*Nutrition Value + 0.1705*Taste + 0.5482*Preservation Quality

At 0.05 % significance level SPSS Output

Regression Equation of Preference Preference = 0.7329 + 0.2947*Nutrition Value + 0.1705*Taste + 0.5482*Preservation Quality

At 0.01 % significance level SPSS Output

Regression Equation of Preference Preference = 0.7329 + 0.2947*Nutrition Value + 0.1705*Taste + 0.5482*Preservation Quality 2. Regression results are as follows: Regression Statistics Multiple R 0.9275 R Square 0.8603 Adjusted R Square 0.8486 Standard Error 0.6992 Observations 40

3. The Partial Regression Coefficients are, Preference = 0.7329 + 0.2947*Nutrition Value + 0.1705*Taste + 0.5482*Preservation Quality Interpretation From the above regression equation we can infer that:   

With every 1% increase in nutrition value of the biscuit, there will be 0.2947% change in the preference of the customer. Also if we increase the taste of the biscuit by 1%, there will be 0.1705% change in the preference of the customer while buying. Similarly for every 1% increase in preservation quality, there will be a 0.5482% change in the preference.

4. Overall significance of the regression using Anova table,

Interpretation The p-value i.e., f-significant value obtained from the results is 0, which is lesser than our alpha value (0