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:
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 |
By using this Indiana Real Estate Data
Get Answers For Free
Most questions answered within 1 hours.