Mike:
It's really the error terms in the regression model that are required to
have normal distributions with constant variance. We check this by looking
at the properties of the residuals from the regression. You shouldn't expect
the response (dependent) variable to have a normal distribution with a fixed
mean since then you wouldn't be doing regression.
By the way, you have a fine Statistics Department at VPI. I am sure they
do excellent consulting.
Jon Cryer
At 06:39 PM 5/11/00 -0400, you wrote:
>I would like to obtain a prediction equation using linear regression for
>some data that I have collected. I have read in some stats books that
>linear regression has 4 assumptions, 2 of them being that 1) data is
>normally distributed and 2) constant variance. In SAS, I have run
>univariate analysis testing for normality on both my dependent and
>independent variable (n=147). Both variables have distributions that are
>skewed.
>
>For the dependent variable: skewness=0.69 and Kurtosis=0.25.
>For the independent variable: skewness=0.52 and Kurtosis= -0.47.
>
>The normality test (Shapiro-Wilk Statistic) states that both the dependent
>and independent variables are not normally distributed.
>
>I have also transformed the data (both dependent and independent variables)
>using log, arcsine, and square root transformations. When I run the
>normality tests on the transformed data, the test shows that even the
>transformed data is not normally distributed.
>
>I realize that I can use nonparametric tests for correlation (I will use
>Spearman), but is there a nonparametric linear regression? If not, is it
>acceptable to use linear regression analysis on data that is not normally
>distributed as a way to show there is a linear relationship?
>
>thanks in advance..Mike
>
>
>
>
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Jon Cryer [EMAIL PROTECTED] ( )
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