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This reader-friendly book focuses on building linear statistical models and developing skills for implementing regression analysis in real-life situations. It includes applications for a range of fields including engineering, sociology, and psychology, as well as traditional business applications.The authors use the latest material available from news articles, magazines, professional journals, the Internet, and actual consulting problems to illustrate real business situations and how to solve them using the tools of regression analysis. In addition, this book emphasizes model building and multiple regression models and pays special attention to model validation and spline regression.For professionals in any number of fields, including engineering, sociology, and psychology, who would benefit from learning how to use regression analysis to solve problems.
Table of Contents
1. A Review of Basic Concepts (Optional). 2. Introduction to Regression Analysis. 3. Simple Linear Regression. 4. Multiple Regression. 5. Model-Building. 6. Variable Screening Methods. 7. Some Regression Pitfalls. 8. Residual Analysis. 9. Special Topics in Regression (Optional). 10. Time Series Modeling and Forecasting. 11. Principles of Experimental Design. 12. The Analysis of Variance for Designed Experiments.
Case Studies 13. Modeling the Sale Prices of Residential Properties in Four Neighborhoods.
14. An Analysis of Rain Levels in California.
15. Reluctance to Transmit Bad News: The MUM Effect.
16. An Investigation of Factors Affecting the Sale Price of Condominium Units Sold at Public Auction.
17. Modeling Daily Peak Electricity Demands.
Appendix A: The Mechanics of a Multiple Regression Analysis.
Appendix B: A Procedure for Inverting a Matrix.
Appendix C: Statistical Tables.
Appendix D: SAS for Windows Tutorial.
Appendix E: SPSS for Windows Tutorial.
Appendix F: MINITAB for Windows Tutorial.
Appendix G: Sealed Bid Data for Fixed and Competitive Highway Construction Contracts.
Appendix H: Real Estate Appraisals and Sales Data for Six Neighborhoods in Tampa, Florida.
Appendix I: Condominium Sales Data.
Answers to Odd-Numbered Exercises.
OVERVIEWThis text is designed for two types of statistics courses. The early chapters, combined with a selection of the case study chapters, are designed for use in the second half of a two-semester (or two-quarter) introductory statistics sequence for undergraduates with statistics or non-statistics majors. Or, the text can be used for a course in applied regression analysis for masters or Ph.D. students in other fields.At first glance, these two uses for the text may seem inconsistent. How could a text be appropriate for both undergraduate and graduate students? The answer lies in the content. In contrast to a course in statistical theory, the level of mathematical knowledge required for an applied regression analysis course is minimal. Consequently, the difficulty encountered in learning the mechanics is much the same for both undergraduate and graduate students. The challenge is in the application-diagnosing practical problems, deciding on the appropriate linear model for a given situation, and knowing which inferential technique will answer the researcher's practical question. This takes experience, and it explains why a student with a non-statistics major can take an undergraduate course in applied regression analysis and still benefit from covering the same ground in a graduate course. Introductory Statistics CourseIt is difficult to identify the amount of material that should be included in the second semester of a two-semester sequence in introductory statistics. Optionally, a few lectures should be devoted to Chapter 1 (A Review of Basic Concepts) to make certain that all students possess a common background knowledge of the basic concepts covered in a first-semester (first-quarter) course. Chapter 2 (Introduction to Regression Analysis), Chapter 3 (Simple Linear Regression), Chapter 4 (Multiple Regression Models), Chapter 5 (Model Building), Chapter 6 (Variable Screening Methods), Chapter 7 (Some Regression Pitfalls), and Chapter 8 (Residual Analysis) provide the core for an applied regression analysis course. These chapters could be supplemented by the addition of Chapter 10 (Introduction to Time Series Modeling and Forecasting), Chapter 11 (Principles of Experimental Design), or Chapter 12 (The Analysis of Variance for Designed Experiments). Applied Regression for GraduatesIn our opinion, the quality of an applied graduate course is not measured by the number of topics covered or the amount of material memorized by the students. The measure is how well they can apply the techniques covered in the course to the solution of real problems encountered in their field of study. Consequently, we advocate moving on to new topics only after the students have demonstrate ability (through testing) to apply the techniques under discussion. In-class consulting sessions, where a case study is presented and the students have the opportunity to diagnose the problem and recommend an appropriate method of analysis, are very helpful in teaching applied regression analysis. This approach is particularly useful in helping students master the difficult topic of model selection and model building (Chapters 4-8) and relating questions about the model to real-world questions. The case study chapters (Chapters 13-17) illustrate the type of material that might be useful for this purpose.A course in applied regression analysis for graduate students would start in the same manner as the undergraduate course, but would move more rapidly over the review material and would more than likely be supplemented by Appendix A (The Mechanics of a Multiple Regression Analysis), one of the statistical software Windows tutorials in Appendices D, E, or F (SAS, SPSS, or MINITAB), Chapter 9 (Special Topics in Regression), and other chapters selected by the instructor. in the undergraduate course, we recommend the use of case studies and in-class consulting sessions to help students develop