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    Regression analysis to estimate home prices

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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:
    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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    https://brainmass.com/statistics/regression-analysis/regression-analysis-193083

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    Solution Preview

    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

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