This project involves using R to examine a dataset containing information about heart attack cases. In my analysis, I generated a logistic regression model to predict the likelihood of a patient dying ...

Project Description Stroke Prediction using Logistic Regression This project focuses on building a Logistic Regression model to predict the likelihood of a stroke based on patient data. The dataset ...

One aim of robust regression is to find estimators with high finite sample breakdown points. Although various robust estimators have been proposed in logistic regression models, their breakdown points ...

In epidemiological studies, continuous covariates often are measured with error and categorical covariates often are misclassified. Using the logistic regression ...

eSpeaks host Corey Noles sits down with Qualcomm's Craig Tellalian to explore a workplace computing transformation: the rise of AI-ready PCs. Matt Hillary, VP of Security and CISO at Drata, details ...

Logistic regression is a powerful statistical method that is used to model the probability that a set of explanatory (independent or predictor) variables predict data in an outcome (dependent or ...