Summary: 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.
Duration: 7 hours of content.
Modalities:
- live online with instructor -- This course is available periodically (but infrequently) in our public course schedule. The public courses are an opportunity to learn this content with a live instructor, but they are currently only offered in English and at times most convenient to a US audience (because most of our instructors are in US time zones). Don't see what you are looking for? Let us know.
- through a third-party training vendor -- Any course in our JMP Curriculum could be taught by a licensed training vendor, including through the training department at your own company. Contact your JMP representative to learn more.
Prerequisites: Before attending this course, it is recommended that you complete the JMP® Software: A Case Study Approach to Data Exploration and JMP®: Statistical Decisions Using ANOVA and Regression courses or have equivalent experience.
Learning Objectives:
- 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.
Course Outline:
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
Artificial Neural Network Models