Problems on Binary Logistic Regression
Q1. Predicting the probability of the customer buying a product based on the demographic information.
Q2. Estimating the likelihood of the patient having a specific disease based on the medical test results.
Q3. Analyzing the factors influencing employee attrition in a company.
Q4. Assessing the risk of credit card fraud based on the transaction patterns.
Binary Logistic Regression
Binary logistic regression is a statistical method to model the relationship between the binary outcome variable and one or more predictor variables. It is a fundamental technique in statistics and data analysis with wide-ranging applications in various fields such as healthcare, finance, marketing and social sciences.
In this article, we will learn about binary logistic regression discussing its definition, importance, methodology, interpretation, practical applications, and others in detail.
Table of Content
- What is Regression Analysis?
- What is Binary Logistic Regression?
- Logistic Regression
- Mathematics Behind Binary Logistic Regression
- Probability and Odds in Logistic Regression
- Model Fitting in Binary Logistic Regression
- Model Evaluation and Validation
- Binary Vs Multinomial Logistic Regression
- Practical Applications of Binary Logistic Regression