Mehr lesen
Informationen zum Autor Ruth M. Mickey , PhD, is Professor of Mathematics and Statistics at the University of Vermont. The late Olive Jean Dunn , PhD, was Professor Emerita of Biostatistics and Biomathematics at the University of California, Los Angeles. Virginia A. Clark , PhD, is Professor Emerita of Biostatistics and Biomathematics at the University of California, Los Angeles. Klappentext This work has been thoughtfully designed so that it serves equally well as a reference for the practitioner and as a self-contained textbook for the advanced student.* Rewritten to maintain clarity and brevity while expanding the coverage of previous editions.* Changes to design-related topics include increased discussion of mixed models and random effects, greater emphasis on regression and data screening, and more use of graphs throughout.* Includes both graded and challenging exercises.* Liberal computer discussions now supplemented with SAS and SPSS. Zusammenfassung This work has been thoughtfully designed so that it serves equally well as a reference for the practitioner and as a self-contained textbook for the advanced student. Rewritten to maintain clarity and brevity while expanding the coverage of previous editions. Inhaltsverzeichnis 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...
