Ridge regression is an approach to ameliorating the problem of large standard errors of regression estimates when predictor variables are highly intercorrelated. An interactive computer program is presented which allows for investigation of the effects of using various ridge regression adjustment values. (JKS)

Ridge regression is an approach to the problem of large standard errors of regression estimates of intercorrelated regressors. The effect of ridge regression on the estimated squared multiple correlation coefficient is discussed and illustrated. (JKS)

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)

A technique is presented which applies the Neyman theory of confidence intervals to interval estimation of the squared multiple correlation coefficient. A computer program is presented which can be used to apply the technique. (Author/JKS)

Using a recent innovation described by Pedhazur, a simpler regression solution to the repeated measures design is shown. Use of the techniques is described and an example is presented. (Author/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 multiple regression analysis to detect sex-related salary discrimination is investigated via a simulation study. (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)

The type I error rate in stepwise regression analysis deserves serious consideration by researchers. The problem-wide error rate is the probability of selecting any variable when all variables have population regression weights of zero. Appropriate significance tests are presented and a Monte Carlo experiment is described. (Author/CTM)

Three methods of handling disproportionate cell frequencies in two-way analysis of variance are examined. A Monte Carlo approach was used to study the method of expected frequencies and two multiple regression approaches to the problem as disproportionality increases. (Author/JKS)

Fifteen examples using different formulas for calculating degrees of freedom for power analysis of multiple regression designs worked out by Cohen are presented, along with a more general formula for calculating such degrees of freedom. (Author/JKS)

The utility of a nonlinear transformation of the criterion variable in multiple regression analysis is established. A well-known law--the Pythagorean Theorem--illustrates the point. (Author/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)

A suppressor variable is a regressor in a multiple regression which contributes more to the squared multiple correlation than the magnitude of its simple correlation with the outcome variable. An example of such a situation is provided for teaching purposes. (JKS)