Book contents
- Frontmatter
- Contents
- Preface
- Chapter 1 Varieties of Count Data
- Chapter 2 Poisson Regression
- Chapter 3 Testing Overdispersion
- Chapter 4 Assessment of Fit
- Chapter 5 Negative Binomial Regression
- Chapter 6 Poisson Inverse Gaussian Regression
- Chapter 7 Problems with Zeros
- Chapter 8 Modeling Underdispersed Count Data – Generalized Poisson
- Chapter 9 Complex Data: More Advanced Models
- Appendix: SAS Code
- Bibliography
- Index
Preface
Published online by Cambridge University Press: 05 August 2014
- Frontmatter
- Contents
- Preface
- Chapter 1 Varieties of Count Data
- Chapter 2 Poisson Regression
- Chapter 3 Testing Overdispersion
- Chapter 4 Assessment of Fit
- Chapter 5 Negative Binomial Regression
- Chapter 6 Poisson Inverse Gaussian Regression
- Chapter 7 Problems with Zeros
- Chapter 8 Modeling Underdispersed Count Data – Generalized Poisson
- Chapter 9 Complex Data: More Advanced Models
- Appendix: SAS Code
- Bibliography
- Index
Summary
Modeling Count Data is written for the practicing researcher who has a reason to analyze and draw sound conclusions from modeling count data. More specifically, it is written for an analyst who needs to construct a count response model but is not sure how to proceed.
A count response model is a statistical model for which the dependent, or response, variable is a count. A count is understood as a nonnegative discrete integer ranging from zero to some specified greater number. This book aims to be a clear and understandable guide to the following points:
• How to recognize the characteristics of count data
• Understanding the assumptions on which a count model is based
• Determining whether data violate these assumptions (e.g., overdispersion), why this is so, and what can be done about it
• Selecting the most appropriate model for the data to be analyzed
• Constructing a well-fitted model
• Interpreting model parameters and associated statistics
• Predicting counts, rate ratios, and probabilities based on a model
• Evaluating the goodness-of-fit for each model discussed
There is indeed a lot to consider when selecting the best-fitted model for your data. I will do my best in these pages to clarify the foremost concepts and problems unique to modeling counts. If you follow along carefully, you should have a good overview of the subject and a basic working knowledge needed for constructing an appropriate model for your study data.
- Type
- Chapter
- Information
- Modeling Count Data , pp. xi - xviPublisher: Cambridge University PressPrint publication year: 2014