Question

Statistical Methods of Business II – Case Study – Indiana Real Estate

Ann Perkins, a realtor in Brownsburg, Indiana, would like to use estimates from a multiple regression model to help prospective sellers determine a reasonable asking price for their homes. She believes that the following four factors influence the asking price (Price) of a house:

1. The square footage of the house (SQFT)
2. The number of bedrooms (Bed)
3. The number of bathrooms (Bath)
4. The lot size (LTSZ) in acres

She randomly collects online listings for 50 single-family homes. The data file is located in the Blackboard “Case Study Indiana Real Estate Data File Excel” within the Case Study folder.

Part 2 – Estimate and interpret a multiple regression model where the asking price is the response variable and the other four factors are the explanatory variables.

SUMMARY OUTPUT
Regression Statistics
Multiple R
R Square
Adj. R Square
Standard Error
Observations
ANOVA
	Df	SS	MS	F	Significance F
Regression
Residual
Total
	Coefficients	Standard Error	t Stat	P-value	Lower 95%		Upper 95%
Intercept
SQFT
Bed
Bath
LTSZ

Answer 1

By using this Indiana Real Estate Data