Logistic Regression: An Introduction

Logistic regression is a special case of regression analysis and is used when the dependent variable is nominally scaled or ordinally scaled. This is the case, for example, with the variable purchase decision with the two characteristic values "buys a product" and "does not buy a product". Logistical regression analysis is thus the counterpart of linear regression, in which the dependent variable of the regression model must at least be interval-scaled. With logistic regression, it is now possible to explain the dependent variable or estimate the probability of occurrence of the characteristic values of the variable.

What is a logistic regression?
In the basic form of logistic regression, dichotomous variables (0 or 1) can be predicted. For this purpose, the probability of the occurrence of characteristic 1 (=characteristic exists) is estimated. In medicine, for example, a frequent application is to find out which variables have an influence on a disease. In this case, 0 could stand for not ill and 1 for ill and the influence of age, sex and smoker status on this particular disease is investigated.

More Information on logistic regression:
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Regression Analysis: An introduction to Linear and Logistic Regression
Regression Analysis: An introduction ...
Simple and Multiple Linear Regression
Simple and Multiple Linear Regression
Assumptions of Linear Regression
Assumptions of Linear Regression
Logistic Regression: An Introduction
Logistic Regression: An Introduction
Dummy Variables in Multiple Regression
Dummy Variables in Multiple Regression
Regression with categorical independent variables
Regression with categorical independe...
Multicollinearity
Multicollinearity (in Regression Anal...
Causality, Correlation and Regression
Causality, Correlation and Regression