Logistic regression. Linear regression. Outcome variable . Models binary outcome variables. Models continuous outcome variables. Regression line. Fits a non-linear S-curve using the sigmoid function .

Unlike standard linear regression models, logistic regression does not require a linear relationship between the independent and dependent variables.

A new study investigated how logistic regression model training affects performance, and which features are best to include when examining datasets from individuals suffering from COVID-19.

Proper handling of continuous variables is crucial in healthcare research, for example, within regression modelling for descriptive, explanatory, or predictive purposes. However, inadequate methods ...

In this investigation, we present simulations to compare the performance of linear and logistic regression models under alternative weighting schemes in a fixed-effects meta-analysis framework, ...

If the signal to noise ratio is low (it is a ‘hard’ problem) logistic regression is likely to perform best. In technical terms, if the AUC of the best model is below 0.8, logistic very clearly ...

Nicholas J. Horton, Nan M. Laird, Maximum Likelihood Analysis of Logistic Regression Models with Incomplete Covariate Data and Auxiliary Information, Biometrics ... 765-769) provides a general method ...

Robert D. Gibbons, Donald Hedeker, Random Effects Probit and Logistic Regression Models for Three-Level Data, Biometrics, Vol. 53, No. 4 (Dec., 1997), pp. 1527-1537 Link account to institutional ...