JMP - Analyzing Discrete Responses Training Course and Workshop in Bangalore, Mysore, Chennai, Hyderabad, Pune, Mumbai, Delhi, Noida, Gurgaon, Kolkata

This JMP - Analyzing Discrete Responses training course provides skills 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.

By attending JMP - Analyzing Discrete Responses workshop, delegates will learn 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.

Attend a training on JMP Software: Data Exploration and JMP Software: ANOVA and Regression or have equivalent experience.

The JMP - Analyzing Discrete Responses class is ideal for:

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 knowledge.

Associations

Recognizing the difference between continuous and categorical data
Examining associations among variables
Conducting hypothesis tests of association
Interpreting a correspondence plot

Logistic regression

Introducing likelihood and maximum likelihood estimation
Introducing logit transformation
Performing logistic regression including odds ratio analysis
Fitting ordinal logistic regression models
Fitting nominal logistic regression models

Generalized linear models

Introducing generalized linear models
Using a glm for a binary response
Using a glm for a probit analysis
Using a glm for counts (poisson loglinear model)

Recursive partitioning

Performing recursive partitioning for a decision tree model

Artificial neural network models

Fitting predictive models for discrete outcomes using a neural network model

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