Regression Modelling Techniques for Categorical Data - Statistical Solutions by Creascience

Linear regression is inappropriate to model binary responses such as pass/fail, alive/dead.

Learn the principle of logistic regression, its similarities with linear regression and its specific tools. Good practices for model-building are presented.

Introduction to Logistic Regression

Goal: To Study the Relationship between a Categorical Variable and a Set of Explanatory Variables
Why Does Ordinary Multiple Linear Regression Fail for the Analysis of a Categorical Response Variable?

Refresher on Multiple Linear Regression

Definition and Estimation of the Model
Interpretation of model coefficients
Goodness-of-Fit and Validation Techniques

Classical Case: a Binary Response Variable

Basic Principle: Modeling the probability of observing a given value of the reponse variable
Example
Interpretation of Statistical Software Output: Coefficients and Mathematical Transformations, Odds Ratios, Statistical Testing of Model Coefficients
Comparison of Logistic Regression Software Output with Multiple Linear Regression
Goodness-of-Fit Measures: Nested Models, Cross-Validation Techniques
Using the Model for Predictive Purposed

Overview of the Case of an Ordinal and Nominal Response Variables Practical Considerations

Procedures Available in Statistical Software
Implementation and Interpretation

This module is aimed at all scientific staff who collect categorical data and who must make decisions based on them. The regression techniques covered in this session will be particularly useful for people who deal with qualitative response variables (measurements) in finance, epidemiology, medicine, genetics, social sciences, econometrics and marketing. Participants must have attended the following modules.

- Fundamental Tools in Statistics

- Linear Regression Techniques

or possess an equivalent background.

Upon completion of this module, participants will be able to:

Understand the context of use of logistic regression
Understand why ordinary regression fails for the modeling of categorical variables
Construct a logistic regression model
Assess the goodness-of-fit of the model to the data
Identify common issues in logistic regression, diagnose problems and fix them
Interpret statistical software output

Recommended Duration: 1 day(s)

Course Materials:

TBD

Case Studies:

N/A