Analysis of Variance, Design, and Regression: Linear Modeling for Unbalanced Dat

Description: Analysis of Variance, Design, and Regression by Ronald Christensen 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 unba FORMAT Paperback CONDITION Brand New Publisher Description 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 EditionReorganized to focus on unbalanced dataReworked balanced analyses using methods for unbalanced dataIntroductions to nonparametric and lasso regressionIntroductions to general additive and generalized additive modelsExamination of homologous factorsUnbalanced split plot analysesExtensions to generalized linear modelsR, Minitab®, and SAS code on the authors websiteThe text can be used in a variety 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. Author Biography 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 ASAs Section on Bayesian Statistical Science. His research interests include linear models, Bayesian inference, log-linear and logistic models, and statistical methods. Table of Contents 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. Review 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)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) Review Quote 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 f Details ISBN036773740X Author Ronald Christensen Publisher Taylor & Francis Ltd Year 2020 ISBN-10 036773740X ISBN-13 9780367737405 Publication Date 2020-12-18 UK Release Date 2020-12-18 Edition 2nd Format Paperback Place of Publication London AU Release Date 2020-12-18 NZ Release Date 2020-12-18 Pages 636 Series Chapman & Hall/CRC Texts in Statistical Science Subtitle Linear Modeling for Unbalanced Data, Second Edition Edition Description 2nd edition Alternative 9780367834098 DEWEY 519.536 Audience Professional & Vocational Imprint Chapman & Hall/CRC US Release Date 2020-12-18 Country of Publication United Kingdom We've got this At The Nile, if you're looking for it, we've got it. 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