Issues in the application of multiple regression analysis as a data analytic tool are discussed at some length. Included are discussions on component regression, factor regression, ridge regression, and systems of equations. (JKS)

The problems of two way analysis of variance designs with unequal and disproportionate cell sizes are discussed. A variety of solutions are discussed and a new solution is presented. (JKS)

Recent criticism in the literature of the use of inferential statistics in educational research is refuted. The authors focus on the defense of multiple regression analysis. (JKS)

The construction and interpretation of confidence intervals for the prediction of new cases in multiple regression analysis is explained. An example is provided. (JKS)

Problems associated with the use of gain scores, analysis of covariance, multicollinearity, part and partial correlation, and the lack of rectilinearity in regression are discussed. Particular attention is paid to the misuse of statistical techniques. (JKS)

With even the simplest bivariate regression, least-squares solutions are inappropriate unless one assumes a priori that reciprocal effects are absent, or at least implausible. While this discussion is limited to bivariate regression, the issues apply equally to multivariate regression, including stepwise regression. (Author/CTM)

Of 50 students taking a graduate course in multiple regression analysis at a particular university, they tended to use multiple regression in their dissertations. (Author/JKS)