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Analysis of Covariance: Johnson-Neyman Procedure; 3 Covariate Case

Steve M. Hunka
Organization: University of Alberta
Department: Professor Emeritus of Educational Psychology
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Analysis of covariance is used to assess the statistical significance of mean differences among experimental groups with an adjustment made for initial differences on one or more concomitant variables (covariates). When coefficients are not homogeneous, the effect of the adjustment will be different for different values of the covariate to which groups are equated. The Johnson-Neyman procedure in this Notebook accommodates analyses when the regression coefficients are not homogeneous for the 1-way ANCOVA case having 3 covariates. (For the case of 1 or 2 covariates, see item 605, "Analysis of Covariance: Johnson-Neyman Procedure.") The representations of regression planes, and regions of significance are made using 3D contour plots. Regions of significance for user input contrasts are also available. User options include the plotting range for the covariates and level of significance and testing the significance of an adjustment to a specific point set of the covariates.

*Mathematics > Probability and Statistics

heterogeneous regression, general linear model
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*Analysis of Covariance: Johnson-Neyman Procedure   [in MathSource: Packages and Programs]
*Analysis of Variance   [in MathSource: Packages and Programs]
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