SPSS 17. 0 Advanced Statistical Procedures Companion available in Paperback
Key Message: SPSS® 17.0: Advanced Statistical Procedures Companion contains valuable tips, warnings, and examples that will help you take advantage of SPSS and better analyze data. This book offers clear and concise explanations and examples of advanced statistical procedures in the SPSS Advanced and Regression modules.
Key Topics: Model Selection Loglinear Analysis; Logit Loglinear Analysis; Multinomial Logistic Regression; Ordinal Regression; Probit Regression; Kaplan-Meier Survival Analysis; Life Tables; Cox Regression; Variance Components; Linear Mixed Models; Generalized Linear Models; Generalized Estimating Equations; Nonlinear Regression; Two-Stage Least-Squares Regression; Weighted Least-Squares Regression; Multidimensional Scaling
Market: for all readers interested in SPSS.
|Edition description:||New Edition|
|Product dimensions:||6.00(w) x 1.25(h) x 9.00(d)|
About the Author
Marija Norušis earned a PhD in biostatistics from the University of Michigan. She was SPSS's first professional statistician. During this time, she wrote her first book, The SPSS Introductory Guide. Since then she has written numerous volumes of highly acclaimed SPSS documentation, and textbooks that demystify statistics and SPSS. Dr. Norušis has been on the faculties of the University of Chicago and Rush Medical College, teaching statistics to diverse audiences. When not working on SPSS guides, Marija analyzes real data as a statistical consultant.
For more detailed information about Dr. Norušis and her SPSS guides, visit her website at www.norusis.com.
Table of Contents
1. Model Selection Loglinear Analysis
2. Logit Loglinear Analysis
3. Multinomial Logistic Regression
4. Ordinal Regression
5. Probit Regression
6. Kaplan-Meier Survival Analysis
7. Life Tables
8. Cox Regression
9. Variance Components
10. Linear Mixed Models
11. Generalized Linear Models
12. Generalized Estimating Equations
13. Nonlinear Regression
14. Two-Stage Least-Squares Regression
15. Weighted Least-Squares Regression
16. Multidimensional Scaling