Applied Regression Analysis and Other Multivariable Methods
- 1,074 pages
- English
- PDF
- Available on iOS & Android
Applied Regression Analysis and Other Multivariable Methods
About this book
This bestseller will help you learn regression-analysis methods that you can apply to real-life problems. It highlights the role of the computer in contemporary statistics with numerous printouts and exercises that you can solve using the computer. The authors continue to emphasize model development, the intuitive logic and assumptions that underlie the techniques covered, the purposes, advantages, and disadvantages of the techniques, and valid interpretations of those techniques. Available with InfoTrac® Student Collections http://gocengage.com/infotrac.
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Key takeaways
Apply straight-line and multiple regression models to real-life problems, including fitting models, performing statistical inference, and interpreting results.
Employ advanced regression techniques such as dummy variables, confounding, and interaction to refine models. Evaluate model assumptions and diagnose potential problems to select the most appropriate regression equation.
Apply analysis of variance (ANOVA) for various experimental designs and utilize generalized linear models, including logistic and Poisson regression, for different types of outcome data. Analyze correlated data using general linear mixed models.
Information
Table of contents
- Dedications
- Contents
- Preface
- Ch 1: Concepts and Examples of Research
- Ch 2: Classification of Variables and the Choice of Analysis
- Ch 3: Basic Statistics: A Review
- Ch 4: Introduction to Regression Analysis
- Ch 5: Straight-Line Regression Analysis
- Ch 6: The Correlation Coefficient and Straight-Line Regression Analysis
- Ch 7: The Analysis-of-Variance Table
- Ch 8: Multiple Regression Analysis: General Considerations
- Ch 9: Statistical Inference in Multiple Regression
- Ch 10: Correlations: Multiple, Partial, and Multiple Partial
- Ch 11: Confounding and Interaction in Regression
- Ch 12: Dummy Variables in Regression
- Ch 13: Analysis of Covariance and Other Methods for Adjusting Continuous Data
- Ch 14: Regression Diagnostics
- Ch 15: Polynomial Regression
- Ch 16: Selecting the Best Regression Equation
- Ch 17: One-Way Analysis of Variance
- Ch 18: Randomized Blocks: Special Case of Two-Way ANOVA
- Ch 19: Two-Way ANOVA with Equal Cell Numbers
- Ch 20: Two-Way ANOVA with Unequal Cell Numbers
- Ch 21: The Method of Maximum Likelihood
- Ch 22: Logistic Regression Analysis
- Ch 23: Polytomous and Ordinal Logistic Regression
- Ch 24: Poisson Regression Analysis
- Ch 25: Analysis of Correlated Data Part 1: The General Linear Mixed Model
- Ch 26: Analysis of Correlated Data Part 2: Random Effects and Other Issues
- Ch 27: Sample Size Planning for Linear and Logistic Regression and Analysis of Variance
- Appendix A: Tables
- Appendix B: Matrices and Their Relationship to Regression Analysis
- Appendix C: SAS Computer Appendix
- Appendix D: Answers to Selected Problems
- Index
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