A property dealer wants to predict the selling price of a house using a multiple linear regression equation with living area, number of rooms, distance from airport as a predictor (independent) variables. A multiple linear regression analysis has been used on previous data of 100 flats. The following output was received.

Model Summary
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.868a	.755	.743	10.226979
a. Predictors: (Constant), distance_airport, living_area, no_of_rooms

ANOVAb
Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	102305.682	3	34101.894	7.116	.000a
	Residual	555915.458	116	4792.375
	Total	658221.140	119
a. Predictors: (Constant), distance_airport, living_area, no_of_rooms
b. Dependent Variable: selling_price

Coefficientsa
Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
		B	Std. Error	Beta
1	(Constant)	156.901	99.167		2.582	.000
	living_area	2.107	.698	.326	3.016	.003
	no_of_rooms	5.395	9.040	.076	4.597	.000
	distance_airport	1.261	.584	.234	2.161	.003
a. Dependent Variable: selling_price

QUESTIONS:

a) Estimate the regression equation and predict the selling price of a house with 1200 Square feet living area, 4 room and 5 km from airport.
b) Comment on goodness of model fit by interpreting R-square and ANOVA table.
c) Which variable is having highest impact on selling price of house? Why?

Answer rating: 100% (QA)

a Estimate the regression equation and predict the selling price of a house with 1200 Square feet li... View the full answer