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Informationen zum Autor KEITH E. MULLER, PhD, is Professor and Director of the Division of Biostatistics in the Department of Epidemiology and Health Policy Research in the College of Medicine at the University of Florida in Gainesville, as well as Professor Emeritus of Biostatistics at The University of North Carolina at Chapel Hill where the book was written. BETHEL A. FETTERMAN, PhD, is Research Associate Professor of Biostatistics at The University of North Carolina at Chapel Hill. Klappentext The information contained in this book has served as the basis for a graduate-level biostatistics class at the University of North Carolina at Chapel Hill. The book focuses in the General Linear Model (GLM) theory, stated in matrix terms, which provides a more compact, clear, and unified presentation of regression of ANOVA than do traditional sums of squares and scalar equations. The book contains a balanced treatment of regression and ANOVA yet is very compact. Reflecting current computational practice, most sums of squares formulas and associated theory, especially in ANOVA, are not included. The text contains almost no proofs, despite the presence of a large number of basic theoretical results. Many numerical examples are provided, and include both the SAS code and equivalent mathematical representation needed to produce the outputs that are presented. All exercises involve only "real" data, collected in the course of scientific research. The book is divided into sections covering the following topics: Basic Theory Multiple Regression Model Building and Evaluation ANOVA ANCOVA Zusammenfassung This set contains: 9780471469438 Regression and ANOVA: An Integrated Approach Using SAS(R) Software by Keith E. Muller! Bethel A. Fetterman and 9780471370383 Applied Statistics: Analysis of Variance and Regression! Third Edition by Ruth M. Mickey! Olive Jean Dunn! Virginia A. Clark. Inhaltsverzeichnis Regression and ANOVA: An Integrated Approach Using SAS Software Preface. Examples and Limits of the GLM. Statement of the Model, Estimation, and Testing. Some Distributions for the GLM. Multiple Regression: General Considerations. Testing Hypotheses in Multiple Regression. Correlations. GLM Assumption Diagnostics. GLM Computation Diagnostics. Polynomial Regression. Transformations. Selecting the Best Model. Coding Schemes for Regression. One-Way ANOVA. Complete, Two-Way Factorial ANOVA. Special Cases of Two-Way ANOVA and Random Effects Basics. The Full Model in Every Cell (ANCOVA as a Special Case). Understanding and Computing Power for the GLM. Appendix A. Matrix Algebra for Linear Models. Appendix B. Statistical Tables. Appendix C. Study Guide for Linear Model Theory. Appendix D. Homework and Example Data. Appendix E. Introduction to SAS/IML. Appendix F. A Brief Manual to LINMOD. Appendix G. SAS/IML Power Program User's Guide. Appendix H. Regression Model Selection Data. References. Index. Applied Statistics: Analysis of Variance and Regression, 3rd Edition Preface. 1. Data Screening. 1.1 Variables and Their Classification. 1.2 Describing the Data. 1.3 Departures from Assumptions. 1.4 Summary. 2. One-Way Analysis of Variance Design. 2.1 One-Way Analysis of Variance with Fixed Effects. 2.2 One-Way Analysis of Variance with Random Effects. 2.3 Designing an Observational Study or Experiment. 2.4 Checking if the Data Fit the One-Way ANOVA Model. 2.5 What to Do if the Data Do Not Fit the Model. 2.6 Presentation and Interpretation of Results. 2.7 Summary. 3. Estimation and Simultaneous Inference. 3.1 Estimation for Single Population Means.<...
