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multiple regression question

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  • #51129

    elmore
    Participant

    Hi,
    I’m in the social sciences (psych) using cross sectional data. I’m completing a multiple hierarchical regression looking at interaction effects.
    If one of my predictor (non-moderator) variables that I’m using in the interaction is highly non-normal (skewness = 2.1 and kurtosis 4.7), should I transform it to improve it?  When is it ok to leave it alone? In terms of the other predictors I’m using, these pass the normality test and I’m centering them to minimize collinearity problems.
    Help!
    thanks …. 

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    #176762

    Remi
    Participant

    Gabrielle,
    Y=F(x1,x2,x3,..)+error   where the Xi are your predictors. The error for a certain combination of the Xi is called the Residual at that location.  It is not necessary for the regression that the Xi’s are Normal distributed! It is only necessary that the Residuals are normal distributed .
    Remi

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    #176764

    Craig
    Participant

    Think about this example with simple linear regression. You are studying the relationship of adult males height (X) and how that variable is related to weight (Y).  You study a range of X between 5 feet tall and 7 feet tall, and choose 5 ft, 5.5 ft, 6 ft, 6.5 ft, and 7 ft as the “settings” for X. You want to do lack of fit, so you randomly choose 5 men at each of the heights. Can you visualize these 25 height values as being normal or a uniform dist? It is only the residuals that have to be normal, not the Y or X values.

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