Modern Regression Methods

Name: Modern Regression Methods
Price: 23.55 USD
Availability: InStock
ISBN: 9780470081860

ISBN-10: 0470081864

ISBN-13: 9780470081860

Edition: 2nd 2009

Authors: Thomas P. Ryan

List price: $184.95

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Description:

Regression is a popular research area, and regression analysis is an ever-changing collection of techniques. This book offers a synthesis of state-of-the-art regression methodology. Featuring a strong data analysis orientation, it presents regression diagnostics in a uniquely comprehensive manner. Each chapter contains a brief section for SAS, Minitab, SPSS, BMDP, and Systat users, though particular software packages are not emphasized overall. Enhancements to the second edition include a unification and expansion of subject areas addressed in the first edition, as well as an increased emphasis on applications. Modern Regression Methods offers a peerless resource for students, researchers,…

Book details

List price: $184.95
Edition: 2nd
Copyright year: 2009
Publisher: John Wiley & Sons, Incorporated
Publication date: 11/10/2008
Binding: Hardcover
Pages: 672
Size: 6.40" wide x 9.70" long x 1.70" tall
Weight: 2.442
Language: English



Preface



Introduction



Simple Linear Regression Model



Uses of Regression Models



Graph the Data!



Estimation of 0 and



Inferences from Regression Equations



Regression Through the Origin



Additional Examples



Correlation



Miscellaneous Uses of Regression



Fixed Versus Random Regressors



Missing Data



Spurious Relationships



Software



Summary


Appendix


References


Exercises



Diagnostics and Remedial Measures



Assumptions



Residual Plots



Transformations



Influential Observations



Outliers



Measurement Error



Software



Summary


Appendix


References


Exercises



Regression with Matrix Algebra



Introduction to Matrix Algebra



Matrix Algebra Applied to Regression



Summary


Appendix


References


Exercises



Introduction to Multiple Linear Regression



An Example of Multiple Linear Regression



Centering And Scaling



Interpreting Multiple Regression Coefficients



Indicator Variables



Separation or Not?



Alternatives to Multiple Regression



Software



Summary


References


Exercises



Plots in Multiple Regression



Beyond Standardized Residual Plots



Some Examples



Which Plot?



Recommendations



Partial Regression Plots



Other Plots For Detecting Influential Observations



Recent Contributions to Plots in Multiple Regression



Lurking Variables



Explanation of Two Data Sets Relative to R



Software



Summary


References


Exercises



Transformations in Multiple Regression



Transforming Regressors



Transforming Y



Further Comments on the Normality Issue



Box-Cox Transformation



Box-Tidwell Revisited



Combined Box-Cox and Box-Tidwell Approach



Other Transformation Methods



Transformation Diagnostics



Software



Summary


References


Exercises



Selection of Regressors



Forward Selection



Backward Elimination



Stepwise Regression



All Possible Regressions



Newer Methods



Examples



Variable Selection for Nonlinear Terms



Must We Use a Subset?



Model Validation



Software



Summary


Appendix


References


Exercises



Polynomial and Trigonometric Terms



Polynomial Terms



Polynomial-Trigonometric Regression



Software



Summary


References


Exercises



Logistic Regression



Introduction



One Regressor



A Simulated Example



Detecting Complete Separation, Quasicomplete Separation and Near Separation



Measuring the Worth of the Model



Determining the Worth of the Individual Regressors



Confidence Intervals



Exact Prediction