A Second Course in Statistics: Regression Analysis (5th Edition)

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9780133968217: A Second Course in Statistics: Regression Analysis (5th Edition)

Appropriate for either the second term of an introductory statistics course or for a course in applied regression analysis for graduate students in all disciplines. This text focuses on building linear statistical models and on developing skills for implementing regression analysis in real life situations. The fifth edition now includes applications for engineering, sociology, psychology, etc., as well as traditional business applications. The authors use material from news articles, magazines, professional journals, and actual consulting problems to illustrate real business problems and how to solve them by using the tools of regression analysis.

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From the Publisher:

This text focuses on building linear statistical models and on developing skills for implementing regression analysis in real life situations. The fifth edition now includes applications for engineering, sociology, psychology, etc., as well as traditional business applications. The authors use material from news articles, magazines, professional journals, and actual consulting problems to illustrate real business problems and how to solve them by using the tools of regression analysis.

Excerpt. © Reprinted by permission. All rights reserved.:

OVERVIEW

This 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 Course

It 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 Graduates

In 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 an ability to formulate appropriate statistical models and to interpret the results of their analyses.

FEATURES

  1. Readability. We have purposely tried to make this a teaching (rather than a reference) text. Concepts are explained in a logical intuitive manner using worked examples.
  2. Emphasis on model building. The formulation of an appropriate statistical model is fundamental to any regression analysis. This topic is treated Chapters 4-8 and is emphasized throughout the text.
  3. Emphasis on developing regression skills. In addition to teaching the basic concepts and methodology of regression analysis, this text stresses its use, as tool, in solving applied problems. Consequently, a major objective of the text is to develop a skill in applying regression analysis to appropriate real-life situations.
  4. Numerous real data-based examples and exercises. The text contains many worked examples that illustrate important aspects of model construction, data analysis, and the interpretation of results. Nearly every exercise is based on data and a problem extracted from a news article, magazine, or journal. Exercises are located at the ends of key sections and at the ends of chapters.
  5. Case study chapters. The text contains five case study chapters, each of which addresses a real-life research problem. The student can see how regression analysis was used to answer the practical questions posed by the problem, proceeding with the formulation of appropriate statistical models to the analysis and interpretation of sample data.
  6. Data sets. The text contains four complete data sets that are associated with the case studies (Chapters 13-17). These can be used by instructors and students to practice model-building and data analyses.
  7. Extensive use of statistical software. Tutorials on how to use any of three popular statistical software packages, SAS, SPSS, and MINITAB, are provided in Appendices D, E, and F, respectively. The printouts of the respective software packages are presented and discussed throughout the text.

NEW TO THE SIXTH EDITION

Although the scope and coverage remain the same, the sixth edition contains several substantial changes, additions, and enhancements:

  1. More computer printouts. A SAS, SPSS, or MINITAB printout now accompanies every statistical technique presented, allowing the instructor to emphasize interpretations of the statistical results rather than the calculations required to obtain the results.
  2. Statistical software tutorials. The Appendix now includes basic instructions on how to use the Windows versions of SAS, SPSS, and MINITAB. Step-by-step instructions and screen shots for each method presented in the text are shown.
  3. Describing qualitative data. Anew section (Sec. 1.3) on graphical and numerical methods of describing qualitative data has been added to Chapter 1.
  4. Paired comparisons for means. New material on comparing two population means using a paired difference experiment is now included in Chapter 1 (Sec. 1.10).
  5. Reorganization of multiple regression models. The multiple regression models presented in Chapter 4 have been reorganized according to order and complexity. First-order models are presented first, followed by interaction and second-order models.
  6. Model validation. The section on external model validation (previously presented as a special topic in Chapter 9) has been moved to the model building chapter (Chapter 5). Several new examples are presented.
  7. Variable screening methods. Stepwise regression and the all-possible-regressions-selection procedure are now included in a separate chapter (Chapter 6).
  8. Spline regression. Spline regression methods are now discussed in the section on robust regression (Sec. 9.8) in Chapter 9: Special Topics.
  9. Case study 13: Residential property sale price data updated. The data set for the case study on predicting sale prices of residential properties has been updated to reflect current economic trends.

Numerous less obvious changes in details have been made throughout the text in response to suggestions by current users of the earlier editions.

SUPPLEMENTS

The text is also accompanied by the following supplementary material:

  1. Student's solutions manual. (by Mark Dummeldinger). A student's exercise solutions manual presents the full solutions to the odd exercises contained in the text.
  2. Instructor's solutions manual. (by Mark Dummeldinger). The instructor's exercise solutions manual presents the full solutions to the other half (the even) exercises contained in the text. For adopters, the manual is complimentary from the publisher.
  3. Data CD. The text is accompanied by a CD that contains files for all data sets marked with a CD icon in the text. These include data sets for text examples, exercises, and case studies. The data files are saved in ASCII format for easy importing into statistical software (SAS, SPSS, and MINITAB).

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