Rudolf J. Freund, William J. Wilson, Ping Sa

Regression Analysis (eBook, PDF)

• Format: PDF

Produktbeschreibung

Regression Analysis provides complete coverage of the classical methods of statistical analysis. It is designed to give students an understanding of the purpose of statistical analyses, to allow the student to determine, at least to some degree, the correct type of statistical analyses to be performed in a given situation, and have some appreciation of what constitutes good experimental design.

• Examples and exercises contain real data and graphical illustration for ease of interpretation
• Outputs from SAS 7, SPSS 7, Excel, and Minitab are used for illustration, but any major statistical software package will work equally well

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Produktdetails

• Verlag: Elsevier Science & Techn.
• Seitenzahl: 480
• Erscheinungstermin: 30. Mai 2006
• Englisch
• ISBN-13: 9780080522975
• Artikelnr.: 38118434

Autorenporträt

Rudolf J. Freund works at Texas A&M University in the USA.

Inhaltsangabe

1. The Analysis of Means: A Review of Basics and an Introduction to Linear Models 2. Simple Linear Regression:Linear Regression with One Independent Variable 3. Multiple Regression 4. Problems with Observations 5. Multicollinearity 6. Problems with the Model 7. Curve Fitting 8. Introduction to Nonlinear Models 9. Indicator Variables 10. Categorical Response Variables 11. Generalized Linear Models

Appendix A: Statistical Tables B: A Brief Introduction to Matrices C: Estimation Procedures

Rezensionen

"...is well-written , well-organized, and succeeds in making regression analysis understandable, without being overly technical." --Donice McCune, Stephen F. Austin University

"I would say that this book is excellent from both a pedagogical perspective and a learning perspective (by the student). The instructor will enjoy discussing various concepts and then illustrating the concepts through the thorough examples. 6. This textbook will help give the students additional mathematical maturity for handling other statistics courses, especially applied courses like Analysis of Variance." --Steven Garren, James Madison University