Part I. The Multiple Linear Regression Model -- 1. Multiple Linear Regression -- 2. Model Building -- Part Ii. Addressing Violations Of Assumptions -- 3. Diagnostics For Unusual Observations -- 4. Transformations And Linearizable Models -- 5. Time Series Data And Autocorrelation -- Part Iii. Categorical Predictors -- 6. Analysis Of Variance -- 7. Analysis Of Covariance -- Part Iv. Other Regression Models -- 8. Logistic Regression -- 9. Multinomial Regression -- 10. Count Regression -- 11. Nonlinear Regression. Samprit Chatterjee, New York University, Jeffrey S. Simonoff, New York University. Written By Two Established Experts In The Field, The Purpose Of This Handbook Is To Provide A Practical, One-stop Reference On Regression Analysis. The Focus Is On The Tools That Both Practitioners And Researchers Use In Real Life. It Is Intended To Be A Comprehensive Collection Of The Theory, Methods, And Applications Of The Subject Matter, But It Is Deliberately Written At An Accessible Level. The Handbook Will Provide A Quick And Convenient Reference Or Refresher On Ideas And Methods That Are Useful For The Accurate Analysis Of Data And Its Resulting Interpretations. Students Can Use The Book As An Introduction To And/or Summary Of Key Concepts In Regression And Related Course Work (such As Linear, Nonlinear, And Nonparametric Regressions). Plentiful References Are Supplied For The More Motivated Readers. Theory Is Presented When Necessary, And Always Supplemented By Hands-on Examples. Software Routines Are Available Via An Author-maintained Web Site--provided By Publisher. Includes Bibliographical References (pages 227-230) And Index.