Question

Consider the two variables service quality and customer satisfaction. Service quality is independent variable and customer satisfaction is dependent variable. Perform regression analysis using SPSS and explain the results.

PLEASE USE SPSS ONLY AND PASTE THE OUTPUT OF SPSS. DO NOT USE EXCEL OR ANY OTHER SOFTWARE. SPSS ONLY PLEASE.

SERVICE QUALITY	CUSTOMER SATISFACTION
2	1
3	2
4	4
5	7
6	6
7	7
8	9
6	11
6	9
4	5
5	4
6	7
2	3
10	11
8	5
9	6
11	2
11	4
13	8
2	8
4	8
3	9
1	1
2	2
2	3
1	4
2	5
4	5
5	6
6	3
7	4
2	4
2	5
2	6
1	4
4	4
13	11
12	11
1	2
2	2
4	4
4	4
6	6
7	5
3	4
1	2
3	3
4	5
6	6
7	7
2	2
3	3
1	4
5	4
5	1

Answer 1

  REGRESSION ANALYSIS OUTPUT EXPLANATION

Model Summary
Model	R	R Square	Adjusted R Square	Std. An error of the Estimate	Change Statistics	Durbin-Watson
R Square Change	F Change	df1	df2	Sig. F Change
	1	.558a	.312	.298	2.684	.312	23.527	1	52	.000	.932
a. Predictors: (Constant), SATISFACTIONQUALITY
b. Dependent Variable: CUSTOMER SATISFACTION Analysis- By looking at the above regression table, it can be said that there is a significant relationship between satisfaction quality and customer satisfaction ie. customer satisfaction is dependent on the quality of satisfaction, a customer gets on using a product or service. The value of R represents the correlation value between the dependent and the independent variable. The value of .558 does not represent a high correlation, but a significant correlation. The R square value depicts that 31.2 % of the variation in customer satisfaction is explained by the satisfaction quality. The value of Durbin Watson shows that there is autocorrelation between the residuals. ANOVA Model Sum of Squares df Mean Square F Sig. 1 Regression 169.485 1 169.485 23.527 .000a Residual 374.608 52 7.204 Total 544.093 53 a. Predictors: (Constant), SATISFACTIONQUALITY b. Dependent Variable: CUSTOMER SATISFACTION This above ANOVA table predicts that the regression table significantly predicts the relation between satisfaction quality and customer satisfaction. Coefficients Model Unstandardized Coefficients Standardized Coefficients t Sig. Collinearity Statistics B Std. Error Beta Tolerance VIF 1 (Constant) 1.515 .782 1.937 .058 SATISFACTIONQUALITY .664 .137 .558 4.850 .000 1.000 1.000 a. Dependent Variable: CUSTOMER SATISFACTION This table shows that satisfaction quality has a significant relationship with customer relationships and has a significant contribution to the model formed by regression analysis. The VIF score of 1 indicates that there is a low multi collinearity among the variables as the score of VIF <3. The model of the equation is   Customer satisfaction = 1.515 + .664 (Satisfaction Quality)