A Second Course in Statistics: Regressing Analysis 7/e

　　A Second Course in Statistics: Regression Analysis, Seventh Edition, focuses on building linear statistical models and developing skills for implementing regression analysis in real situations. This text offers applications for engineering, sociology, psychology, science, and business. The authors use real data and scenarios extracted from news articles, journals, and actual consulting problems to show how to apply the concepts. In addition, seven case studies, now located throughout the text after applicable chapters, invite students to focus on specific problems, and are suitable for class discussion.

New To This Edition

　　1. New and updated case studies: two new case studies (Case Study 1: Legal Advertising–Does it Pay? and Case Study 3: Deregulation of the Trucking Industry) have been added, and another (Case Study 2: Modeling Sale Prices of Residential Properties) has been updated with current data.

　　2. Real data exercises have been increased and updated, based on contemporary studies and developments in a variety of fields.

　　3. Technology Tutorials on CD:Statistical software instruction includes the latest software packages: SAS?, SPSS?, MINITAB?, and, new to the text, R.

　　4. More emphasis on p-values reflects the tendency of regression analysts to rely on statistical software to fit and assess models in practice. This prepares students for testing statistical hypothesis theories with technology used by professional statisticians.

　　5. Updated examples in Chapter 9, Special Topics in Regression, include new examples on piecewise regression, weighted least squares, logistic regression, and ridge regression.

　　6.Redesigned end-of-chapter summaries are easier to use when reviewing and studying.
　　．Flow charts aid in the selection of the appropriate statistical method.
　　．Important points are reinforced through boxed notes with key words, formulas, definitions, lists, and key concepts.

Ch 1 A Review of Basic Concepts (Optional)
Ch 2 Introduction to Regression Analysis
Ch 3 Simple Linear Regression
Case Study 1: Legal Advertising--Does It Pay?
Ch 4 Multiple Regression Models
Case Study 2: Modeling the Sale Prices of Residential Properties in Four Neighborhoods
Ch 5 Principles of Model Building
Ch 6 Variable Screening Methods
Case Study 3: Deregulation of the Intrastate Trucking Industry
Ch 7 Some Regression Pitfalls
Ch 8 Residual Analysis
Case Study 4: An Analysis of Rain Levels in California
Case Study 5: An Investigation of Factors Affecting the Sale Price of Condominium Units Sold at Public Auction
Ch 9 Special Topics in Regression (Optional)
Ch10 Introduction to Time Series Modeling and Forecasting
Case Study 6: Modeling Daily Peak Electricity Demands
Ch11 Principles of Experimental Design
Ch12. The Analysis of Variance for Designed Experiments
Case Study 7: Reluctance to Transmit Bad News: The MUM Effect

Appendix A: Derivation of the Least Squares Estimates of β0 and β1 in Simple Linear Regression
Appendix B: The Mechanics of a Multiple Regression Analysis
Appendix C: A Procedure for Inverting a Matrix
Appendix D: Useful Statistical Tables
Appendix E: File Layouts for Case Study Data Sets

Answers to Selected Odd-Numbered Exercises
Index
Technology Tutorials: SAS, SPSS, MINITAB, and R (on CD)