Log-Linear Models and Logistic Regression
This book examines statistical models for frequency data. The primary focus is on log-linear models for contingency tables but also includes extensive discussion of logistic regression. Topics such as logistic discrimination, generalized linear models, and correspondence analysis are also explored....
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| Format: | Electronic eBook |
| Language: | English |
| Published: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2025.
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| Edition: | 3rd ed. 2025. |
| Series: | Springer Texts in Statistics,
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| Subjects: | |
| Online Access: | https://doi.org/10.1007/978-3-031-69038-9 |
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| 100 | 1 | |a Christensen, Ronald. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Log-Linear Models and Logistic Regression |h [electronic resource] / |c by Ronald Christensen. |
| 250 | |a 3rd ed. 2025. | ||
| 264 | 1 | |a Cham : |b Springer Nature Switzerland : |b Imprint: Springer, |c 2025. | |
| 300 | |a 1 online resource (XXI, 545 p. 74 illus.). |b 2 illus. in color. | ||
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| 490 | 1 | |a Springer Texts in Statistics, |x 2197-4136 | |
| 505 | 0 | |a Two-Dimensional Tables and Simple Logistic Regression -- Three-Dimensional Tables -- Logistic Regression, Logit Models, and Logistic Discrimination -- Independence Relationships and Graphical Models -- Model Selection Methods and Model Evaluation -- Models for Factors with Quantitative Levels -- Fixed and Random Zeros -- Generalized Linear Models -- The Matrix Approach to Log-Linear Models -- The Matrix Approach to Logit Models -- Maximum Likelihood Theory for Log-Linear Models -- Bayesian Binomial Regression. Exact Conditional Tests. - Correspondence Analysis. | |
| 520 | |a This book examines statistical models for frequency data. The primary focus is on log-linear models for contingency tables but also includes extensive discussion of logistic regression. Topics such as logistic discrimination, generalized linear models, and correspondence analysis are also explored. The treatment is designed for readers with prior knowledge of analysis of variance and regression. It builds upon the relationships between these basic models for continuous data and the analogous log-linear and logistic regression models for discrete data. While emphasizing similarities between methods for discrete and continuous data, this book also carefully examines the differences in model interpretations and evaluation that occur due to the discrete nature of the data. Numerous data sets from fields as diverse as engineering, education, sociology, and medicine are used to illustrate procedures and provide exercises. A major addition to the third edition is the availability of a companion online manual providing R code for the procedures illustrated in the book. The book begins with an extensive discussion of odds and odds ratios as well as concrete illustrations of basic independence models for contingency tables. After developing a sound applied and theoretical basis for frequency models analogous to ANOVA and regression, the book presents, for contingency tables, detailed discussions of the use of graphical models, of model selection procedures, and of models with quantitative factors. It then explores generalized linear models, after which all the fundamental results are reexamined using powerful matrix methods. The book then gives an extensive treatment of Bayesian procedures for analyzing logistic regression and other regression models for binomial data. Bayesian methods are conceptually simple and unlike traditional methods allow accurate conclusions to be drawn without requiring large sample sizes. The book concludes with two new chapters: one on exact conditional tests for small sample sizes and another on the graphical procedure known as correspondence analysis. | ||
| 532 | 8 | |a Accessibility summary: This PDF does not fully comply with PDF/UA standards, but does feature limited screen reader support, described non-text content (images, graphs), bookmarks for easy navigation and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. | |
| 532 | 8 | |a No reading system accessibility options actively disabled | |
| 532 | 8 | |a Publisher contact for further accessibility information: accessibilitysupport@springernature.com | |
| 588 | |a Print version record. | ||
| 650 | 0 | |a Regression analysis. | |
| 650 | 0 | |a Statistics . | |
| 650 | 0 | |a Biometry. | |
| 650 | 1 | 4 | |a Linear Models and Regression. |
| 650 | 2 | 4 | |a Bayesian Inference. |
| 650 | 2 | 4 | |a Biostatistics. |
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| 830 | 0 | |a Springer Texts in Statistics, |x 2197-4136 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/978-3-031-69038-9 |
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