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Logistic Regression: A Primer, Second Edition
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Comment from the Stata technical group
This book provides an excellent introduction to logistic regression from first principles. It is an ideal tutorial for those who are familiar with standard linear regression and wish to branch out for the first time into more complex generalized linear models, for which logistic regression (regression with a binary response) is a good starting point. Making the jump from linear regression to logistic regression introduces many issues, including model fitting (least squares versus maximum likelihood), interpretability (odds ratios), interpretation of marginal effects (which are not constant for logistic regression), and regression diagnostics. These topics are covered fully in the context of logistic regression. Where appropriate, the comparison between linear and logistic regression is made. This discussion is also extended to ordinal logistic, multinomial logistic, and probit models. The book's companion website includes data and commands in Stata, SPSS, and R that allow you to reproduce all examples worked in the book.
Table of contents
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Series Editor Introduction
About the Author
Chapter 1: The Logic of Logistic Regression
Regression With a Binary Dependent Variable
Transforming Probabilities Into Logits
Linearizing the Nonlinear
Chapter 2: Interpreting Logistic Regression Coefficients
Group and Model Comparisons of Logistic Regression Coefficients
Chapter 3: Estimation and Model Fit
Maximum Likelihood Estimation
Tests of Significance Using Log Likelihood Values
Model Goodness of Fit
Chapter 4: Probit Analysis
Another Way to Linearize the Nonlinear
The Probit Transformation
Maximum Likelihood Estimation
Chapter 5: Ordinal and Multinomial Logistic Regression
Ordinal Logistic Regression
Multinomial Logistic Regression
The Logic of Logarithms
Properties of Logarithms
Classroom and web training
Teaching with Stata
Statalist: The Stata Forum
Last updated: 16 November 2022
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