Ridge regression is a technique used to ameliorate the problem of highly correlated independent variables in multiple regression analysis. This paper explains the fundamentals of ridge regression and illustrates its use. (JKS)

Commentary is presented on the preceding articles in this issue of the journal. Critical commentary is made article by article, and some general recommendations are made. (See TM 503 664 through 670). (JKS)

The problem of selecting regression variables using cost criteria is considered. A method is presented which approximates the optimal solution of one of several criterion functions which might be employed. Examples are given and the results are compared with the results of other methods. (Author/JKS)

The use of interaction and non-linear terms in multiple regression poses problems for determining parsimonious models. Several computer programs for using these terms are discussed. (JKS)

Path analysis is a data analytic technique for estimating the strengths of hypothesized relationships among a group of variables for a particular sample. Strategies for the use of path analysis are discussed in detail in this extensive article. (JKS)

Many methodologists are aware that parametric tests associated with the analysis of variance and the analysis of covariance can be computed using regression procedures. It is shown that multiple linear regression can also be employed to compute the Kruskal-Wallis nonparametric analysis of variance. (Author)

The author is generally critical of the previous article (TM 503 686), which concerned the use of multiple regression for nonparametric analysis of variance. (JKS)

Issues in analysis of covariance, multiple regression analysis, and the analysis of variance such as the assumption of independence and directional hypotheses are discussed. (JKS)

A data set from the area of clinical psychology was used to show how multiple regression analysis could be used where analysis of variance might more commonly be used. (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)

A statistical technique designed to highlight the contributions of two continuous predictor variables to a continuous criterion variable is described. The technique involves selecting subpopulations, called pockets, via regression techniques. An example using cognitive styles to predict performance on problem-solving tasks is discussed.…

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

The use of multiple regression analysis was compared to the use of discriminant function analysis in the prediction of college faculty rank. The multiple regression technique was shown to be generally superior in this instance. (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)