Regression and ANOVA: An Integrated Approach Using SAS Software + Applied Statistics: Analysis of Variance and Regression, Third Edition Set

.,."very useful to applied scientists and for graduate level courses in areas of non-mathematical statistics..." ("Zentralblatt Math," Vol.1039, No.8, 2004) " ... very useful to applied scientists and for graduate level courses in areas of non-mathematical statistics... " ("Zentralblatt Math," Vol.1039, No.8, 2004)

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.

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. 3.2 Estimation for Linear Combinations of Population Means. 3.3 Simultaneous Statistical Inference. 3.4 Inference for Variance Components. 3.5 Presentation and Interpretation of Results. 3.6 Summary. 4. Hierarchical or Nested Design. 4.1 Example. 4.2 The Model. 4.3 Analysis of Variance Table and F Tests. 4.4 Estimation of Parameters. 4.5 Inferences with Unequal Sample Sizes. 4.6 Checking If the Data Fit the Model. 4.7 What to Do If the Data Don't Fit the Model. 4.8 Designing a Study. 4.9 Summary. 5. Two Crossed Factors: Fixed Effects and Equal Sample Sizes. 5.1 Example. 5.2 The Model. 5.3 Interpretation of Models and Interaction. 5.4 Analysis of Variance and F Tests. 5.5 Estimates of Parameters and Confidence Intervals. 5.6 Designing a Study. 5.7 Presentation and Interpretation of Results. 5.8 Summary. 6 Randomized Complete Block Design. 6.1 Example. 6.2 The Randomized Complete Block Design. 6.3 The Model. 6.4 Analysis of Variance Table and F Tests. 6.5 Estimation of Parameters and Confidence Intervals. 6.6 Checking If the Data Fit the Model. 6.7 What to Do if the Data Don't Fit the Model. 6.8 Designing a Randomized Complete Block Study. 6.9 Model Extensions. 6.10 Summary. 7. Two Crossed Factors: Fixed Effects and Unequal Sample Sizes. 7.1 Example. 7.2 The Model. 7.3 Analysis of Variance and F Tests. 7.4 Estimation of Parameters and Confidence Intervals. 7.5 Checking If the Data Fit the Two-Way Model. 7.6 What To Do If the Data Don't Fit the Model. 7.7 Summary. 8. Crossed Factors: Mixed Models. 8.1 Example. 8.2 The Mixed Model. 8.3 Estimation of Fixed Effects. 8.4 Analysis of Variance. 8.5 Estimation of Variance Components. 8.6 Hypothesis Testing. 8.7 Confidence Intervals for Means and Variance Components. 8.8 Comments on Available Software. 8.9 Extensions of the Mixed Model. 8.10 Summary. 9. Repeated Measures Designs. 9.1 Repeated Measures for a Single Population. 9.2 Repeated Measures with Several Populations. 9.3 Checking if the Data Fit the Repeated Measures Model. 9.4 What to Do if the Data Don't Fit the Model. 9.5 General Comments on Repeated Measures Analyses. 9.6 Summary. 10. Li