This course teaches you how to analyze discrete (or categorical) data or outcomes using association, contingency tables, stratification, correspondence analysis, logistic regression, generalized linear models, partitioning, and artificial neural network models.

Learn how to

• examine associations among variables
• perform chi-square and Fisher exact tests
• perform stratified analysis
• perform correspondence analysis
• perform logistic regression
• interpret logistic regression output
• fit a binary response and a count of events with generalized linear models (GLM)
• fit a decision tree model
• fit an artificial neural network model.

Who should attend

Analysts, researchers, technicians, and any others filling similar roles who want to analyze a response with discrete levels or a count of events and who have at least some statistical training

Formats available	Standard Duration (duration can vary, see event schedule for details)
Classroom:	2.0 days
Live Web Classroom:	4 half-day session(s)	System Requirements

Before attending this course, it is recommended that you complete the JMP® Software: A Case Study Approach to Data Exploration and JMP® Software: Statistical Decisions Using ANOVA and Regression courses or have equivalent experience.

Associations

• introduction to categorical data
• examining associations among variables
• correspondence analysis

Logistic Regression

• likelihood approach
• binary logistic regression
• nominal and ordinal logistic regression models

Generalized Linear Models

• the generalization
• logistic regression revisited
• Poisson regression

Recursive Partitioning

• partition platform

Artificial Neural Network Models

• neural platform