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    4 basic assumption of multiple regression analysis

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    The appropriate use of multiple regression depends on being able to make four basic assumptions about the data being used to develop the regression model:

    that variables are normally distributed;
    that the relationship between an independent variable and the dependent variable is linear
    that the variance of errors is homoscedastic; and
    that there is no multicollinearity among independent variables.

    Pick two of these assumptions, and describe how you would check it for a dataset that you want to use to build a regression model.

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    that variables are normally distributed

    You can create a few histograms and see what the shape of the distribution looks ...

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    The expert examines the basic assumptions of multiple regression analysis.

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