SPSS 16.0 Advanced Statistical Procedures Companion
Published on 18. February 2008
Book
Paperback/Softback
432 pages
978-0-13-606140-3 (ISBN)
Description
SPSS (R) 16.0: 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.
More details
Language
English
Place of publication
United States
Publishing group
Pearson Education (US)
Target group
College/higher education
Dimensions
Height: 235 mm
Width: 191 mm
Weight
644 gr
ISBN-13
978-0-13-606140-3 (9780136061403)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Previous edition
Marija Norusis
SPSS 15.0 Advanced Statistical Procedures Companion
Book
04/2007
Pearson
€47.03
Article exhausted; check for reprint
Person
Marija Norusis earned a Ph.D. in biostatistics from the University of Michigan. She was SPSS's first professional statistician. She has written numerous volumes of highly acclaimed SPSS documentation, and textbooks that demystify statistics and SPSS. Dr. Norusis 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.
Content
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
Bibliography
Index
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
Bibliography
Index