Homoscedasticity

The assumption of homoscedasticity is that the residuals are approximately equal for all predicted DV scores. Another way of thinking of this is that the variability in scores for your IVs is the same at all values of the DV. You can check homoscedasticity by looking at the same residuals plot talked about in the linearity and normality sections. Data are homoscedastic if the residuals plot is the same width for all values of the predicted DV. Heteroscedasticity is usually shown by a cluster of points that is wider as the values for the predicted DV get larger. Alternatively, you can check for homoscedasticity by looking at a scatterplot between each IV and the DV. As with the residuals plot, you want the cluster of points to be approximately the same width all over. Heteroscedasiticy may occur when some variables are skewed and others are not. Thus, checking that your data are normally distributed should cut down on the problem of heteroscedasticity. Like the assumption of linearity, violation of the assumption of homoscedasticity does not invalidate your regression so much as weaken it.