Applied Regression Analysis and Other Multivariable Methods

By: Kleinbaum, David GContributor(s): Kupper, Lawrence L | Nizam, Azhar | Mullar, Keith EMaterial type: TextTextPublication details: Australia ; Belmont, CA : Brooks/Cole, 2008Edition: 4th edDescription: xxi, 906 pages : illustrationsISBN: 9780495384960 ; 0495384968Subject(s): Multivariate analysisDDC classification: 519.536
Contents:
Concepts and examples of research -- Classification of variables and the choice of analysis -- Basic statistics : a review -- Introduction to regression analysis -- Straight-line regression analysis -- The correlation coefficient and straight-line regression analysis -- The analysis-of-variance table -- Multiple regression analysis : general considerations -- Testing hypotheses in multiple regression -- Correlations : multiple, partial, and multiple partial -- Confounding and interaction in regression -- Dummy variables in regression -- Analysis of covariance and other methods for adjusting continuous data -- Regression diagnostics -- Polynomial regression -- Selecting the best regression equation -- One-way analysis of variance -- Randomized blocks : special case of two-way anova -- Two-way anova with equal cell numbers -- Two-way anova with unequal cell numbers -- The method of maximum likelihood -- Logistic regression analysis -- Polytomous and ordinal logistic regression -- Poisson regression analysis -- Analysis of correlated data part 1 : the general linear mixed model -- Analysis of correlated data part 2 : random effects and other issues -- Sample size planning for linear and logistic regression and analysis of variance.
Summary: This bestseller will help you learn regression-analysis methods that you can apply to real-life problems. It highlights the role of the computer in contemporary statistics with numerous printouts and exercises that you can solve using the computer. The authors continue to emphasize model development, the intuitive logic and assumptions that underlie the techniques covered, the purposes, advantages, and disadvantages of the techniques, and valid interpretations of those techniques.
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Index

Concepts and examples of research --
Classification of variables and the choice of analysis --
Basic statistics : a review --
Introduction to regression analysis --
Straight-line regression analysis --
The correlation coefficient and straight-line regression analysis --
The analysis-of-variance table --
Multiple regression analysis : general considerations --
Testing hypotheses in multiple regression --
Correlations : multiple, partial, and multiple partial --
Confounding and interaction in regression --
Dummy variables in regression --
Analysis of covariance and other methods for adjusting continuous data --
Regression diagnostics --
Polynomial regression --
Selecting the best regression equation --
One-way analysis of variance --
Randomized blocks : special case of two-way anova --
Two-way anova with equal cell numbers --
Two-way anova with unequal cell numbers --
The method of maximum likelihood --
Logistic regression analysis --
Polytomous and ordinal logistic regression --
Poisson regression analysis --
Analysis of correlated data part 1 : the general linear mixed model --
Analysis of correlated data part 2 : random effects and other issues --
Sample size planning for linear and logistic regression and analysis of variance.

This bestseller will help you learn regression-analysis methods that you can apply to real-life problems. It highlights the role of the computer in contemporary statistics with numerous printouts and exercises that you can solve using the computer. The authors continue to emphasize model development, the intuitive logic and assumptions that underlie the techniques covered, the purposes, advantages, and disadvantages of the techniques, and valid interpretations of those techniques.

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