The adjusted r-squared is helpful for multiple regression and corrects for erroneous regression, giving you a more accurate ...

Many response variables are handled poorly by regression models when the errors are assumed to be normally distributed. For example, modeling the state damaged/not damaged of cells after treated with ...

In this module, we will introduce generalized linear models (GLMs) through the study of binomial data. In particular, we will motivate the need for GLMs; introduce the binomial regression model, ...

We introduce a fast stepwise regression method, called the orthogonal greedy algorithm (OGA), that selects input variables to enter a p-dimensional linear regression model (with p ≫ n, the sample size ...

1. Sampling error in annual estimates of population size creates two widely recognized problems for the analysis of population growth. First, if sampling error is ...

As the coronavirus disease 2019 (COVID-19) pandemic has spread across the world, vast amounts of bioinformatics data have been created and analyzed, and logistic regression models have been key to ...

Health Care, Physics, and Finance: The Complex Equation of Outpatient CAR T-Cell Therapy A total of 824,535 patient records were analyzed. Resulting MSCs were 50 in pulmonary, 31 in esophageal, 31 in ...