Practical Applications of Binary Logistic Regression
Binary logistic regression finds applications in the various fields such as:
- Predicting the likelihood of the disease occurrence based on the risk factors in the medical research.
- Assessing the probability of the default on the loan in financial analysis.
- Determining customer churn in the marketing analytics.
- Identifying sentiment polarity in the text classification.
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