
- English
- ePUB (mobile friendly)
- Available on iOS & Android
The Association Graph and the Multigraph for Loglinear Models
About this book
This practical guide teaches nonstatisticians how to analyze and interpret loglinear models using the multigraph
The Association Graph and the Multigraph for Loglinear Models will help students, particularly those studying the analysis of categorical data, to develop the ability to evaluate and unravel even the most complex loglinear models without heavy calculations or statistical software. This supplemental text reviews loglinear models, explains the association graph, and introduces the multigraph to students who may have little prior experience of graphical techniques, but have some familiarity with categorical variable modeling. The author presents logical step-by-step techniques from the point of view of the practitioner, focusing on how the technique is applied to contingency table data and how the results are interpreted.
Frequently asked questions
- Essential is ideal for learners and professionals who enjoy exploring a wide range of subjects. Access the Essential Library with 800,000+ trusted titles and best-sellers across business, personal growth, and the humanities. Includes unlimited reading time and Standard Read Aloud voice.
- Complete: Perfect for advanced learners and researchers needing full, unrestricted access. Unlock 1.4M+ books across hundreds of subjects, including academic and specialized titles. The Complete Plan also includes advanced features like Premium Read Aloud and Research Assistant.
Please note we cannot support devices running on iOS 13 and Android 7 or earlier. Learn more about using the app.
Information
Table of contents
- Cover
- Title
- Copyright
- Contents
- About the Author
- Series Editor’s Introduction
- Dedication
- 1. Introduction
- 2. Structures of Association
- 3. Loglinear Model Review
- 4. Association Graphs for Loglinear Models
- 5. Collapsibility Conditions and the Association Graph
- 6. The Generator Multigraph
- 7. Fundamental Conditional Independencies for Nondecomposable Loglinear Models
- 8. Conclusions and Additional Examples
- Data Sets
- References
- Author Index
- Subject Index