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)

A process is described for multiple comparisons when covariates are involved in the analysis. The method can be accomplished with considerable ease whenever pairwise comparisons are involved. More complex contrasts require the use of full and restricted models of variance. (CTM)

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)

Some of the more simplified methods for contrasts with equal sample sizes in multiple regression analysis are shown to result in erroneous calculations when applied to data sets with unequal sample sizes. An alternative method is provided. (Author/JKS)

This paper discusses how full model dummy variables can be used with partial correlation or multiple regression procedures to compute matrices of pooled within-group correlations. (Author/CTM)

Repeated measures designs often involve dichotomization of a continuous variable in order to be amenable to the analysis of variance nature of such designs. An alternative to that approach wherein the independent variable is kept continuous is presented. (JKS)

This paper relates common statistics from contingency table analysis to the more familiar R squared terminology in order to better understand the strength of the relation implied. The method of coding contingency tables was shown, as well as how R squared related to phi, V, and chi squared. (Author/CTM)

The application of multiple linear regression to the identification of appropriate criterion variables and the prediction of enrollment in college courses during a period of major rapid decline (1972-1978) are discussed. An example is presented. (Author/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.…

Repeated measures designs often involve dichotomization of a continuous variable in order to be amenable to the analysis of variance nature of such designs. An alternative to that approach wherein the independent variable is kept continuous is presented. (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)

A Monte Carlo simulation was employed to determine the accuracy with which the shrinkage in R squared can be estimated by five different shrinkage formulas. The study dealt with the use of shrinkage formulas for various sample sizes, different R squared values, and different degrees of multicollinearity. (Author/JKS)