Logistic Regression
Online Workshops
The Craft of Statistical Analysis
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Binary, Ordinal, and Multinomial Logistic Regression for Categorical Outcomes
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Understanding Probability, Odds, and Odds Ratios in Logistic Regression
Stati
stically Speaking Trainings
Logistic Regression for Count and Proportion Data
A Primer on Exponents and Logarithms for the Data Analyst
Analysis of Ordinal Variables–Options Beyond Nonparametrics
Types of Regression Models and When to Use Them
Articles at The Analysis Factor
Get Started with Logistic Regression Concepts
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Introduction to Logistic Regression
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What is a Logit Function and Why Use Logistic Regression?
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Chi-square test vs. Logistic Regression: Is a fancier test better?
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Why use Odds Ratios in Logistic Regression
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Logistic Regression Analysis: Understanding Odds and Probability
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When Dependent Variables Are Not Fit for Linear Models, Now What?
Advanced Topics in Logistic Regression
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How to Interpret Odd Ratios when a Categorical Predictor Variable has More than Two Levels
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Generalized Linear Models in R, Part 1: Calculating Predicted Probability in Binary Logistic Regression
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Generalized Linear Models in R, Part 2: Understanding Model Fit in Logistic Regression Output
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SPSS Procedures for Logistic Regression
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Logistic Regression Models: Reversed odds ratios in SAS Proc Logistic–Use ‘Descending’
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Effect Size Statistics in Logistic Regression
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How to Get Standardized Regression Coefficients When Your Software Doesn’t Want To Give Them To You
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Measures of Predictive Models: Sensitivity and Specificity
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Explaining Logistic Regression Results to Non-Statistical Audiences
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Models for Repeated Measures Continuous, Categorical, and Count Data
