Question

Question #1 Consider the following model that predicts X20 (Likely to recommend) Model Summaryb ModelR RSquare Adjusted R Square Std. Error of the Estimate 7270.529 0.511 0.7570 ANOVA' Sum of Squares df Mean SquareF Si | 30.753 | .000ь 123.346 10.013 233.359 17.621 0.573 sion Residual Total 192 199 Coefficients Unstandardized Coefficients StandardizedtSig Coefficients Model Std. Error 0.671 0.049 0.060 0.062 0.050 0.063 Beta (Constant) X6 - Product Quality X8 Technical Su X10- Advertisin X11 Product Line X12 Salesforce Ima X14 Warranty & Claims X17- Price Flexibilit 0.298 0.356 0.205 0.124 0.270 0.417 0.280 0.270 0.455 0.314 0.131 0.328 0.435 0.227 0.298 0.445 0.657 7.282 0.000 3.426 0.001 1.997 0.047 5.386 0.000 6.575 0.000 2.404 0.017 4.931 0.000 0.055 a. Dependent Variable: X20 Likely to Recommend Please answer the following questions Can we use this regression model to explain and predict X20? Explain why How accurate is this regression model? List the variables that have a significant impact on X20 Would you recommend your company to expand its advertising? What do customers value more: price flexibility or product quality? I. 2. 3. 4. 5. Note: please make sure to clearly organize your answers using numbered list

Answer 1

1. From the ANOVA table, we can see that the F statistic computed is 30.753 which has p-value as significant (<0.001). Hence, the regression model can be used to explain and predict X20 - Likely to recommend.

2. The accuracy of the regression model can be told by the adjusted R-square statistic:

Adjusted R-square value = 0.511

Hence, 51.1% variation can be explained by the model.

3. The variables which have a significant impact on X20 will have significant p-value. We can see from the table that the following variables are significant (at 95% confidence level) or having p-value<0.05 which is given in the last column under Sig.

X6 - Product Quality

X8 - Technical Support

X10 - Advertising

X11 - Product Line

X12 - Salesforce Image

X14 - Warranty and Claims

X17: Price flexibility

4. The advertising variable, X10 is significant and has a negative standardised coefficient = -0.131

This means that it is negatively related to the response variable - likely to recommend. Hence, if we increase the advertising capabilities, the response variable will decrease. Hence, the company should not increase its advertising capabilities.

5. Let's check out the coefficients of both the variables:

Price flexibility, X17 = 0.298

Product quality, X6 = 0.455

Hence, as the coefficient of product quality is more than the coefficient of price flexibility, we can say that there is a greater effect of product quality on the customers. Hence, they value product quality more than the price flexibility.