Synopses & Reviews
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
Supplements:
- 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
- Integrates the classical conceptual approach with modern day computerized data manipulation and computer applications
- Accessibile to students who may not have a background in probability or calculus
- Offers reader-friendly exposition, without sacrificing statistical rigor
- Includes many new data sets in various applied fields such as Psychology, Education, Biostatistics, Agriculture, Economics
Table of Contents
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