Applied linear statistical models: regression, analysis of variance, and experimental designs

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R. D. Irwin, 1974 - Mathematics - 842 pages
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Some basic results in probability and statistics. basic regression analysis. Linear regression with one independent variable. Inferences in regression analysis. Aptness of model and remedial measures. Topics in regression analysis - I. General regression and correlation analysis. Matrix appreach to simple regression analysis. Multiple regression. Polymonial regression. Indicator variables. Topics in regression analysis - II. Search for "best" set of independent variables. Normal correlation models. Basic analysis of variance. Single - factor analysis of variance. Analysis of factor effects. Implementation of ANOVA model. Topics in analysis of variance - I. Multifactor analysis of variance. Two factor analysis of variance. Analysis of two - factor studies. To pics in analysis of variance - II. Multifactor studies. Experimental designs. Completely randomized designs. Analysis of covariance for completely randomized designs. Randomized block designs. Latin square designs.

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Contents

Some Basic Results in Probability and Statistics
1
Linear Regression with One Independent Variable
21
Inferences in Regression Analysis
53
Copyright

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About the author (1974)

Michael H. Kutner is a professor at Emory University in Atlanta.

Chris J. Nachtsheim is a professor at the University of Minnesota--Minneapolis.

John Neter is a professor at the University of Georgia in Athens.

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