Logistic Regression

Instructor: Dr. Aric LaBarr

Introduction

Logistic regression is a fundamental statistical analysis technique for data science and analytics. It part of a class of modeling techniques known as classification models since they are trying to predict categorical target variables. Classification is one of, if not the, most common type of business problems that need solving. Every field needs to solve these kinds of problems. Some examples include target marketing, churn prediction, probability of default, and fraud detection to just name a few.

Logistic regression is a form of supervised classification since the target variable is known. Other variables are used to predict or explain this known target variable.

This target variable can be binary, ordinal, or even nominal in its structure. This markdown file contains information on how to perform logistic regression and all of its components in R, Python, and SAS. In each section you are able to toggle back and forth between the code and output you desire to view.

The libraries for R and Python will be loaded as we go throughout the code. This isn’t needed for the SAS sections of the code.