Logistic Regression Models

Logistic Regression Models
ISBN-10
1138106712
ISBN-13
9781138106710
Pages
656
Language
English
Published
2017-05-18
Publisher
CRC Press
Author
Joseph M. Hilbe

Description

Logistic Regression Modelspresents an overview of the full range of logistic models, including binary, proportional, ordered, partially ordered, and unordered categorical response regression procedures. Other topics discussed include panel, survey, skewed, penalized, and exact logistic models. The text illustrates how to apply the various models to health, environmental, physical, and social science data. Examples illustrate successful modeling The text first provides basic terminology and concepts, before explaining the foremost methods of estimation (maximum likelihood and IRLS) appropriate for logistic models. It then presents an in-depth discussion of related terminology and examines logistic regression model development and interpretation of the results. After focusing on the construction and interpretation of various interactions, the author evaluates assumptions and goodness-of-fit tests that can be used for model assessment. He also covers binomial logistic regression, varieties of overdispersion, and a number of extensions to the basic binary and binomial logistic model. Both real and simulated data are used to explain and test the concepts involved. The appendices give an overview of marginal effects and discrete change as well as a 30-page tutorial on using Stata commands related to the examples used in the text. Stata is used for most examples while R is provided at the end of the chapters to replicate examples in the text. Apply the models to your own data Data files for examples and questions used in the text as well as code for user-authored commands are provided on the book's website, formatted in Stata, R, Excel, SAS, SPSS, and Limdep. See Professor Hilbe discuss the book.

Get the book

Amazon.com
Amazon.com
Search the book
eBay.com
eBay.com
Search the book

Other editions

Similar books

  • Interpretable Machine Learning
    Interpretable Machine Learning
    By Christoph Molnar

    This book is about making machine learning models and their decisions interpretable.

  • Logistic Regression Models for Ordinal Response Variables
    Logistic Regression Models for Ordinal Response Variables
    By Ann A. O'Connell

    Ordinal measures provide a simple and convenient way to distinguish among possible outcomes. The book provides practical guidance on using ordinal outcome models.

  • Practical Guide to Logistic Regression
    Practical Guide to Logistic Regression
    By Joseph M. Hilbe

    Practical Guide to Logistic Regression covers the key points of the basic logistic regression model and illustrates how to use it properly to model a binary response variable.

  • Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis
    Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis
    By Frank E. Harrell

    This text realistically deals with model uncertainty and its effects on inference to achieve "safe data mining".

  • Applied Logistic Regression
    Applied Logistic Regression
    By Stanley Lemeshow, David W. Hosmer, Jr.

    ... MATHIOWETZ, and SUDMAN - Measurement Errors in Surveys COCHRAN - Sampling Techniques, Third Edition COUPER, BAKER, BETHLEHEM, CLARK, MARTIN, NICHOLLS, and O'REILLY (editors) - Computer Assisted Survey Information Collection COX, ...

  • Log-Linear Models and Logistic Regression
    Log-Linear Models and Logistic Regression
    By Ronald Christensen

    The primary focus here is on log-linear models for contingency tables, but in this second edition, greater emphasis has been placed on logistic regression.

  • Applied Logistic Regression
    Applied Logistic Regression
    By Stanley Lemeshow, Rodney X. Sturdivant, David W. Hosmer

    ... Noel A. C. Cressie, Garrett M. Fitzmaurice, Harvey Goldstein, Iain M. Johnstone, Geert Molenberghs, David W. Scott, Adrian F. M. Smith, Ruey S. Tsay, Sanford Weisberg Editors Emeriti: Vic Barnett, J. Stuart Hunter, Joseph B. Kadane, ...

  • Solutions Manual Logistic Regression Models
    By Taylor & Francis Group

    Apply the models to your own dataFrom binary to multinomial, panel, survey, and exact models, this text covers all types of logistic regression models.

  • Logistic Regression Using SAS: Theory and Application
    Logistic Regression Using SAS: Theory and Application
    By Paul David Allison, SAS Institute

    Informal and nontechnical, this book both explains the theory behind logistic regression, and looks at all the practical details involved in its implementation using SAS. Several real-world examples are included in full detail.

  • Logistic Regression: A Self-Learning Text
    Logistic Regression: A Self-Learning Text
    By David G. Kleinbaum, Mitchel Klein

    They often say the book reads like they are listening to an outstanding lecturer. This edition includes three new chapters, an updated computer appendix, and an expanded section about modeling guidelines that consider causal diagrams.