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Multiple Linear Regression… | 4 |
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Newman, Isadore | 4 |
Fraas, John | 1 |
Fraas, John W. | 1 |
Thomas, Jay | 1 |
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Newman, Isadore; Thomas, Jay – Multiple Linear Regression Viewpoints, 1979
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)
Descriptors: Hypothesis Testing, Mathematical Models, Multiple Regression Analysis, Power (Statistics)

Newman, Isadore; Fraas, John – Multiple Linear Regression Viewpoints, 1979
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)
Descriptors: Correlation, Factor Analysis, Multiple Regression Analysis, Research Design

Fraas, John W.; Newman, Isadore – Multiple Linear Regression Viewpoints, 1978
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)
Descriptors: Achievement Gains, Analysis of Covariance, Correlation, Data Analysis

Newman, Isadore; And Others – Multiple Linear Regression Viewpoints, 1979
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)
Descriptors: Computer Programs, Correlation, Goodness of Fit, Mathematical Formulas