Question

I am needing help understanding this practice multiple regression analysis. Any help is appreciated!

The output below is from a multiple regression analysis on the variable "Location". The variable "Location" is a dummy variable, where 0 = urban and 1 = rural. Please refer to the output below for parts a and b.

a) If the analysis were correct, what would be your interpretation of the results (please be thorough)?

b) What is wrong with the analysis? Which mistakes were made?

	Customer Satisfaction	Quality	Price	Location
Customer Satisfaction	1
Quality	0.35	1
Price	-0.22	0.55	1
Location	-0.27	0.07	-0.12	1

SUMMARY OUTPUT

Regression Statistics

Multiple R 0.30

R Square 0.39

Adjusted R Square 0.29

Standard Error 0.47

Observations 57

ANOVA   df SS   MS F     Significance F

Regression 3 2.40 0.80 4.44 0.04

Residual 53 9.54 0.18

Total 56 11.94

	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
Intercept	1.50	0.66	2.27	0.02	0.17	2.83
Customer Satisfaction	-0.16	0.08	-2.04	0.04	-0.32	-0.04
Quality	0.04	0.07	0.59	0.55	-0.10	0.20
Price	-0.03	0.04	-0.92	0.35	-0.12	0.04

Answer 1

A.

As we move from urban to rural location customers satisfaction get decreased by 0.16 units as well as price decreased by 0.03 units. However quality increased by 0.04 units.

B.

From correlation matrix we see that correlation between price and quality is 0.55 which is high it mean autocorrelation exists in the fitted regression equation. Also we see that variable "quality" is not statistically significant since it's p value is greater than 0.05. We have to remove this variable and again fit the model.