Multiple linear regression is used to model the effects of violating statistical assumptions on the likelihood of making a Type I error. This procedure is illustrated for the student's t-test (for independent groups) using data from previous Monte Carlo studies in which the actual alpha levels associated with violations of the normality…

This article illustrates the role multiple linear regression can play in developing prediction equations by providing examples of regression models that could be used in answering questions relative to the importance of a single predictor variable, interactions between predictor variables, and the cross-validation and generalizability of…

Paper proposes a set of techniques for measuring the importance" of each independent variable in a multivariate causal law (i.e., a law showing the combined effect of several independent variables on a single dependent variable). (Authors)

Article applies multiple regression analytic technique to the CIPP model. (Authors/MB)

Setwise regression analysis is a new technique developed to allow a stepwise solution when the interest is in sets of variables rather than in single variables. (CK)

The use of segmented polynomial models is explained. Examples of design matrices of dummy variables are given for the least squares analyses of time series and discontinuity quasi-experimental research designs. Linear combinations of dummy variable vectors appear to provide tests of effects in the two quasi-experimental designs. (Author/BW)

Set correlation is a multivariate generalization of multiple regression/correlation analysis that features the employment of overall measures of association interpretable as proportions of variance and the use of set-partialled sets of variables. The statistical development of the theory and several examples are presented. (Author/JKS)

Redundancy analysis is an attempt to provide nonsymmetric measures of the dependence of one set of variables on another set. This paper attempts to clarify the nature of redundancy analysis and its relationships to canonical correlation and multivariate multiple linear regression. (Author/JKS)

It has been shown that the degree of restriction of range taken into account in testing the hypothesis that rho equals zero, entails risks of incorrect inferences. It is argued that an alternative is to disregard the restriction of range and to use the common t-statistics proposed by regression theory. (Author/JKS)

This model emphasizes the distinction between male and female earnings and the distinction between families with employed wives and those without as they affect the fertility rate. (Author/IRT)

The monotone regression function of Kruskal and the rank image of Guttman and Lingoes are fitted to bivariate normal samples and their statistical properties contrasted. Tables of results are presented. (Author/JKS)