Synopses & Reviews
Demystifies the Use of Advanced Statistcal Methods
Unlike other texts, Primer of Applied Regression and Analysis of Variance teaches both how to understand more advanced multivariate statistical methods, as well as how to use statistical software to get the correct results. This new edition offers the modern, intuitive approaches that won the first edition a wide following, while adding traditional methods for complete coverage of applied statistical methods.
FEATURES:
*Reader-friendly style that makes complicated material approachable and usable
*Practical guidelines for the correct application of statistical software
*Examples from biological and health sciences research that clarify key points
*End-of-chapter study problems that quickly test mastery of the material NEW IN THIS EDITION
*Expanded coverage of traditional ANOVA (analysis of variance)
*Expanded coverage of ANOVA extensions, assumptions, and workarounds for "problem" data
*Cox proportional hazard models
*Expanded coverage of repeated measures
*New examples from biological and health sciences research
*Expanded and revised coverage of statistical software
*Web site (http:www.vetmed.wsu.edu/AppliedRegression/) to support statistics instruction and facilitate use of example and problem data sets
Review
This is a solid reference work. Biostatisticians and epidemiologists will find it useful. 3 Stars."--Doody's Review Service
Review
This is a solid reference work. Biostatisticians and epidemiologists will find it useful."
Synopsis
Applicable for all statistics courses or practical use, teaches how to understand more advanced multivariate statistical methods, as well as how to use available software packages to get correct results. Study problems and examples culled from biomedical research illustrate key points. New to this edition: broadened coverage of ANOVA (traditional analysis of variance), the addition of ANCOVA (analysis of Co-Variance); updated treatment of available statistics software; 2 new chapters (Analysis of Variance Extensions and Mixing Regression and ANOVA: ANCOVA).
Table of Contents
Why Do Multivariate Analysis?. The First Step: Understanding Simple Linear Regression. Regression With Two or More Independent Variables. Do the Data Fit the Assumptions?. Multicollinearity and What to Do About It. Selecting the "Best" Regression Model. One-Way Analysis of Variance. Two-Way Analysis of Variance. Repeated Measures. Mixing Continuous and Categorical Variables: Analysis of Covariance. Nonlinear Regression. Regression With a Qualitative Dependent Variable. APPENDIX A. A Brief Introduction to Matrices and Vectors. APPENDIX B. Statistical Package Cookbook. APPENDIX C. Data for Examples. APPENDIX D. Data for Problems. APPENDIX E. Statistical Tables. APPENDIX F. Solutions to Problems.