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Regression analysis to estimate home prices

A real estate investor has devised a model to estimate home prices in a new suburban development. Data for a random sample of 30 homes were gathered on the selling price of the home ($ thousands), the home size (square feet), the lot size (thousands of square feet), and the number of bedrooms.

The following multiple regression output was generated:
Please see attachment.

a.Why is the coefficient for lot size a positive number?
b.Which is the most statistically significant variable? What evidence shows this?
c.Which is the least statistically significant variable? What evidence shows this?
d. For a 0.05 level of significance, should any variable be dropped from this model? Why or why not?
e.Predict the sales price of a 2134-square-foot home with a lot size of 13,400 square feet and three bedrooms.

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A real estate investor has devised a model to estimate home prices in a new suburban development. Data for a random sample of 30 homes were gathered on the selling price of the home ($ thousands), the home size (square feet), the lot size (thousands of square feet), and the number of bedrooms.

The following multiple regression output was generated:

Regression Statistics
Multiple R 0.9647
R ...

Solution Summary

The solution generates a regression analysis model to estimate home prices in a new suburban development. The solution predicts the sale price of a 2134-square-foot home with a lot size of 13,400 square feet and three bedrooms.

$2.19