Ronald Christensen

Analysis of Variance, Design, and Regression

Linear Modeling for Unbalanced Data, Second Edition

• Gebundenes Buch

Produktbeschreibung

This second edition focuses on modeling unbalanced data. It presents many new topics, including new chapters on logistic regression, log-linear models, and time-to-event data. It shows how to model main-effects and interactions and introduces nonparametric, lasso, and generalized additive regression models. The text carefully analyzes small unbalanced data by using tools that are easily scaled to big data. R, Minitab®, and SAS codes are available on the author's website.

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Produktdetails

• Chapman & Hall/CRC Texts in Statistical Science
• Verlag: Taylor & Francis Inc
• 2 ed
• Seitenzahl: 636
• Erscheinungstermin: 22. Dezember 2015
• Englisch
• Abmessung: 254mm x 178mm x 35mm
• Gewicht: 1294g
• ISBN-13: 9781498730143
• ISBN-10: 1498730140
• Artikelnr.: 43337034

Autorenporträt

Ronald Christensen is a professor of statistics in the Department of Mathematics and Statistics at the University of New Mexico. Dr. Christensen is a fellow of the American Statistical Association (ASA) and Institute of Mathematical Statistics. He is a past editor of The American Statistician and a past chair of the ASA's Section on Bayesian Statistical Science. His research interests include linear models, Bayesian inference, log-linear and logistic models, and statistical methods.

Inhaltsangabe

Introduction. One Sample. General Statistical Inference. Two Samples.
Contingency Tables. Simple Linear Regression. Model Checking. Lack of Fit
and Nonparametric Regression. Multiple Regression: Introduction.
Diagnostics and Variable Selection. Multiple Regression: Matrix
Formulation. One-Way ANOVA. Multiple Comparison Methods. Two-Way ANOVA.
ACOVA and Interactions. Multifactor Structures. Basic Experimental Designs.
Factorial Treatments. Dependent Data. Logistic Regression: Predicting
Counts. Log-Linear Models: Describing Count Data. Exponential and Gamma
Regression: Time-to-Event Data. Nonlinear Regression. Appendices.