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    Using a Regression Model: Predictive Equations In Home Prices

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    House Selling Price Square Footage Bedrooms Age
    1 $64,000 1,670 2 30
    2 $59,000 1,339 2 25
    3 $61,500 1,712 3 30
    4 $79,000 1,840 3 40
    5 $87,500 2,300 3 18
    6 $92,500 2,234 3 30
    7 $95,000 2,311 3 19
    8 $113,000 2,377 3 7
    9 $115,000 2,736 4 10
    10 $138,000 2,500 3 1
    11 $142,500 2,500 4 3
    12 $144,000 2,479 3 3
    13 $145,000 2,400 3 1
    14 $147,500 3,124 4 0
    15 $144,000 2,500 3 2
    16 $155,500 4,062 4 10
    17 $165,000 2,854 3 3
    Average $114,588 2,408 3 14

    Use the data and develop a regression model to predict selling price based on the square footage, number of bedrooms, and age. Use this to predict the selling price of a 10-year-old, 2,000-square-foot house with 3 bedrooms.

    1) State the linear equation.

    2) Explain the overall statistical significance of the model.

    3) Explain the statistical significance for each independent variable in the model

    4) Interpret the Adjusted R2.

    5) Is this a good predictive equation(s)? Which variables should be excluded (if any) and why? Explain.

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    https://brainmass.com/economics/regression/using-regression-model-predictive-equations-home-prices-628380

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    The Solution uses a regression model to predict home prices.

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