What is the defintion of Logistic Multiple Regression Analysis?
Regression analysis is a statistical procedure used to find the relationship between a dependent variable and one or more independent variables. Multiple regression analysis requires there be two or more independent variables. Under a logit regression, the dependent variable is not a continuous variable, but is categorical (e.g., yes or no, or 0 or 1) A logit estimates the probability of the dependent variable being true.
Logistic multiple regression analysis, also known as logistic regression, is a statistical method used to model and predict the relationship between a binary dependent variable and one or more independent variables. The goal of logistic regression is to determine the probability of a categorical outcome occurring based on the values of the predictor variables.
To fully explain the definition of logistic multiple regression analysis, let's break it down:
1. Logistic regression: It is a type of regression analysis that is used when the dependent variable or outcome is categorical. Unlike linear regression, which predicts continuous outcomes, logistic regression predicts the probability of certain categories or events occurring.
2. Multiple regression: It refers to a regression analysis that involves more than one independent variable. In logistic multiple regression analysis, we consider multiple independent variables to predict the probability of the binary outcome.
3. Binary dependent variable: In logistic regression, the dependent variable (or outcome) is binary, meaning it has two categories or levels. For example, it could be a yes/no outcome, presence/absence, or success/failure.
4. Independent variables: These are the predictor variables used in the analysis. They can be continuous, categorical, or a mix of both. The purpose is to examine how these independent variables influence the probability of a particular outcome category.
Overall, logistic multiple regression analysis is a statistical technique used to predict the probability of a binary outcome based on the values of one or more independent variables. It is a valuable tool in various fields, such as medical research, social sciences, marketing, and finance, where predicting categorical outcomes is of interest.