- Rudolf Freund, Texas A&M University, U.S.A.
- Donna Mohr
- William Wilson, University of North Florida, Jacksonville, Florida, U.S.A.
Statistical Methods, 3e provides students with a working introduction to statistical methods offering a wide range of applications that emphasize the quantitative skills useful across many academic disciplines. This text takes a classic approach emphasizing concepts and techniques for working out problems and intepreting results. The book includes research projects, real-world case studies, numerous examples and data exercises organized by level of difficulty. This text requires that a student be familiar with algebra.
New to this edition:
- NEW expansion of exercises applying different techniques and methods
- NEW examples and datasets using current real-world data
- New text organization to create a more natural connection between regression and the Analysis of the Variance
- NEW material on generalized linear models
- NEW expansion of nonparametric techniques
- NEW student research projects
- NEW case studies for gathering, summarizing, and analyzing data
- NEW companion website with downloadable data sets and additional resources including live links to statistical software such as SAS and SPSS
- Student Solutions Manual - to come
- Instructor Manual - to come
- Sample chapter - http://www.elsevierdirect.com/product.jsp?isbn=9780123749703
Advanced undergraduates majoring or minoring in statistics, math or graduate students in an applied field requiring statistical methods: psychology, public administration, economics, sociology, education, public health, engineering, agriculture.
Hardbound, 824 Pages
Published: July 2010
Imprint: Academic Press
- 1. Data and statistics; 2. Probability and sampling distributions; 3. Principles of inference; 4. Inferences on a single population; 5. Inferences for two populations; 6. Inferences for two or more means; 7. Linear regression; 8. Multiple regression; 9. Linear models; 10. Factorial experiments; 11. Design of experiments;12. Categorical data; 13. Generalized linear models; 14. Nonparametric methods