Ronald Christensen

Analysis of Variance, Design, and Regression

Linear Modeling for Unbalanced Data, Second Edition

• Broschiertes Buch

Produktbeschreibung

Analysis of Variance, Design, and Regression: Linear Modeling for Unbalanced Data, Second Edition presents linear structures for modeling data with an emphasis on how to incorporate specific ideas (hypotheses) about the structure of the data into a linear model for the data. The book carefully analyzes small data sets by using tools that are easily scaled to big data. The tools also apply to small relevant data sets that are extracted from big data.

New to the Second Edition

Reorganized to focus on unbalanced data

Reworked balanced analyses using methods for unbalanced data

Introductions to nonparametric and lasso regression

Introductions to general additive and generalized additive models

Examination of homologous factors

Unbalanced split plot analyses

Extensions to generalized linear models

R, Minitab®, and SAS code on the author's website

The text can be used in avariety of courses, including a yearlong graduate course on regression and ANOVA or a data analysis course for upper-division statistics students and graduate students from other fields. It places a strong emphasis on interpreting the range of computer output encountered when dealing with unbalanced data.

Produktdetails

• Chapman & Hall/CRC Texts in Statistical Science
• Verlag: Taylor & Francis; Chapman And Hall/Crc
• 2. Aufl. 2020.
• Seitenzahl: 638
• Erscheinungstermin: 18. Dezember 2020
• Englisch
• Abmessung: 254mm x 178mm x 34mm
• Gewicht: 1180g
• ISBN-13: 9780367737405
• ISBN-10: 036773740X
• Artikelnr.: 69894870

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.

Rezensionen

Praise for the First Edition:
"... written in a clear and lucid style ... an excellent candidate for a beginning level graduate textbook on statistical methods ... a useful reference for practitioners."
-Zentralblatt für Mathematik

"Being devoted to students mainly, each chapter includes illustrative examples and exercises. The most important thing about this book is that it provides traditional tools for future approaches in the big data domain since, as the author says, the machine learning techniques are directly based on the fundamental statistical methods."
~Marina Gorunescu (Craiova)