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)

A family of solutions for linear relations among k sets of variables is proposed. Solutions are compared with respect to their optimality properties. For each solution the appropriate stationary equations are given. For one example it is shown how the determinantal equation of the stationary equations can be interpreted. (Author/BW)

Commonality components have been defined as a method of partitioning squared multiple correlations. The asymptotic joint distribution of all possible squared multiple correlations is derived. The asymptotic joint distribution of linear combinations of squared multiple correlations is obtained as a corollary. (Author/JKS)

An extension of Wollenberg's redundancy analysis (an alternative to canonical correlation) is proposed to derive Y-variates corresponding to the optimal X-variates. These variates are maximally correlated with the given X-variates, and depending upon the standardization chosen they also have certain properties of orthogonality. (Author/JKS)

Following Conger's revised definition of suppressor variables, the universe relationships among two predictors and a criterion is analyzed. A simple mapping of relationships, based on the correlation between two predictors and the ratio of their validities, is provided. The relation between suppressor and part correlation is also discussed.…

The Automatic Interaction Detector (AID) is discussed as to its usefulness in multiple regression analysis. The algorithm of AID-4 is a reversal of the model building process; it starts with the ultimate restricted model, namely, the whole group as a unit. By a unique splitting process maximizing the between sum of squares for the categories of…

A simple roof is presented: that the squared multiple correlation of a variable with the remaining variables in the set of variables is a lower bound to the communality of that variable. (Author/JKS)

When sample sizes and/or X intervals are unequal, the analysis of variance computations for trend analysis become quite complicated. This article shows how multiple regression/correlation analysis may be applied in order to accomplish with great simplicity trend analysis under "irregular" conditions. (Author/RL)

Common applications of the part correlation coefficient are in causal regression models and estimation of suppressor variable effects. However, there is no statistical test of the significance of the difference between a zero-order correlation and a part correlation, nor between a pair of part correlations. Hotelling's t is used for contrasting:…

This paper introduces a model for describing outliers (observations which are extreme in some sense or violate the apparent pattern of other observations) in linear regression which can be viewed as a mixture of a quadratic and a linear regression. The maximum likelihood estimators of the parameters in the model are derived and their asymptotic…

A definition of a suppressor variable is presented which is based on the relation of the semipartial correlation to the zero order correlation. Advantages of the definition are given. (Author/JKS)

An artifact encountered in regression models of human judgment is explored. The direction and magnitude of the artifactual effect is shown to depend upon the nature of the experimental task and task conditions. Use of an alternative index is recommended. (Author/JKS)

A method is proposed for overcoming the problem of multicollinearity in multiple regression equations where multiplicative independent terms are entered. The method is not a ridge regression solution. (JKS)

Interpretation of emergent variables on the basis of structure coefficients (zero order correlations between original and emergent variables) is potentially very misleading and should be avoided in favor of interpretation on the basis of scoring coefficients. This is most apparent in multiple regression analysis and its special case, two-group…