Logistic regression is preferrable over a simpler statistical test such as chi-squared test or Fisher’s exact test as it can incorporate more than one explanatory variable and deals with possible ...

Logistic regression can be thought of as an extension to, or a special case of, linear regression. If the outcome variable is a continuous variable, linear regression is more suitable.

I predict you'll find this logistic regression example with R to be helpful for gleaning useful information from common binary classification problems.

Logistic regression is a machine learning technique for binary classification. For example, you might want to predict the sex of a person (male or female) based on their age, state where they live, ...

Logistic regression enables you to investigate the relationship between a categorical outcome and a set of explanatory variables. The outcome, or response, can be dichotomous (yes, no) or ordinal (low ...

The simplest form of regression in Python is, well, simple linear regression. With simple linear regression, you're trying to ...

Linear and logistic regression models are essential tools for quantifying the relationship between outcomes and exposures. Understanding the mathematics behind these models and being able to apply ...

The CATMOD procedure can perform linear regression and logistic regression of response functions for data that can be represented in a contingency table. See Chapter 5, "Introduction to Categorical ...

Learn the difference between linear regression and multiple regression and how investors can use these types of statistical analysis.

S. Le Cessie, J. C. Van Houwelingen, Ridge Estimators in Logistic Regression, Journal of the Royal Statistical Society. Series C (Applied Statistics), Vol. 41, No. 1 ...