## Online Workshops

## The Craft of Statistical Analysis

- Binary, Ordinal, and Multinomial Logistic Regression for Categorical Outcomes
- Understanding Probability, Odds, and Odds Ratios in Logistic Regression

## Statistically Speaking Trainings

- Those Darn Ratios!
- Logistic Regression for Count and Proportion Data
- Generalized Linear Models
- A Primer on Exponents and Logarithms for the Data Analyst
- Analysis of Ordinal Variables–Options Beyond Nonparametrics
- ROC Curves
- Types of Regression Models and When to Use Them

## Articles at The Analysis Factor

#### Get Started with Logistic Regression Concepts

- Introduction to Logistic Regression
- What is a Logit Function and Why Use Logistic Regression?
- Chi-square test vs. Logistic Regression: Is a fancier test better?
- Why use Odds Ratios in Logistic Regression
- Logistic Regression Analysis: Understanding Odds and Probability
- When Dependent Variables Are Not Fit for Linear Models, Now What?

#### Advanced Topics in Logistic Regression

- How to Interpret Odd Ratios when a Categorical Predictor Variable has More than Two Levels
- Generalized Linear Models in R, Part 1: Calculating Predicted Probability in Binary Logistic Regression
- Generalized Linear Models in R, Part 2: Understanding Model Fit in Logistic Regression Output
- SPSS Procedures for Logistic Regression
- Logistic Regression Models: Reversed odds ratios in SAS Proc Logistic–Use ‘Descending’
- Effect Size Statistics in Logistic Regression
- How to Get Standardized Regression Coefficients When Your Software Doesn’t Want To Give Them To You
- Measures of Predictive Models: Sensitivity and Specificity
- Explaining Logistic Regression Results to Non-Statistical Audiences
- Models for Repeated Measures Continuous, Categorical, and Count Data

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