Stuart R. Lipsitz, Garrett M. Fitzmaurice, Geert Molenberghs, Lue Ping Zhao, Quantile Regression Methods For Longitudinal Data with Drop-Outs: Application to CD4 Cell Counts of Patients Infected with ...

Course Topics"Logistic and Poisson Regression," Wednesday, November 5: The fourth LISA mini course focuses on appropriate model building for categorical response data, specifically binary and count ...

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.

Recently, median regression models have received increasing attention. When continuous responses follow a distribution that is quite different from a normal distribution, usual mean regression models ...

Regression is a method to estimate parameters in mathematical models of biological systems from experimental data. To ensure the validity of a model for a given data set, pre-regression and post ...

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

Course TopicsLinear models, generalized linear models, and nonlinear models are examples of parametric regression models because we know the function that describes the relationship between the ...

Before building my model, I want to step back to offer an easy-to-understand definition of linear regression and why it’s vital to analyzing data. What is linear regression?