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    Forecasting - regression analysis

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    John Howard, a Mobile, Alabama, real estate developer, has devised a regression model to help determine residential housing prices in South Alabama. The model was developed using recent sales in a particular neighborhood. The price (Y) of the house is based on the size (square footage =X) of the house. The model is:
    Y = 13,473 + 38.50X

    The coefficient of correlation for the model is 0.65.

    Using the above model, the selling price of a house that is 1,880 square feet = $_____(enter a whole number)
    A 1,880-square-foot house recently sold for $98,000, which is different than the predicted value. This is a) possible or b) not possible as the forecast represents a) average or b) actual value
    To make this model more realistic, additional quantitative variables that could be included in a multiple regression model are (select the choice that has all the factors that are quantifiable):
    a) The age of the house, the number of bedrooms, and the size of the lot
    b) The size of the lot, the number of bedrooms, and the layout of the rooms
    c) The age of the house, the location of the house, and the size of the garage
    For the given model, the value of the coefficient of determination = ____ (round response to 3 decimal places)

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    Solution Summary

    This solution goes through the process of forecasting using a regression analysis.