What is the value in examining a scatterplot for a regression analysis?
A residual scatterplot is a figure that shows one axis for predicted scores and one axis for errors of prediction. Initial visual examination can isolate any outliers, otherwise known as extreme scores, in the data set. Tabachnick and Fidell (2007) explain the residuals (the difference between the obtained DV and the predicted DV scores) should be normally distributed around the predicted DV scores (normality); should be in a straight-line relationship with the predicted DV scores (linearity); and the variance of the residuals should be the same for all predicted scores (homoscedasticity). If these are true, the assumptions are met and the scatterplot takes the shape of a rectangular; scores will be concentrated in the center and distributed in a rectangular pattern. More simply, scores will be randomly scattered about a horizontal line. In contrast, any systematic pattern or clustering of scores is considered a violation.
