Objective We aimed to estimate prevalence and identify determinants of hypertension in adults aged 15–49 years in Tanzania.

This study combined linear discriminant analysis (LDA) and multivariate logistic regression models to systematically analyze key indicators in flood prediction, aiming to identify factors that are ...

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 ...

We will again illustrate the concepts of ordinal logistic regression model interpretation using an example from the ESCAPE-NA1 trial.14 Online supplemental table 2 shows the results of an ordinal ...

In addition, to address the issue of multicollinearity among the clinical parameters used in the multivariable analysis, the second multivariable regression model was developed after removing clinical ...

Before we learn how to perform multivariate regression in Excel, it is important to have a refresher on regression as a whole and multivariate regression in particular. One of the hallmarks of ...

A Comparison of Logistic Regression Against Machine Learning Algorithms for Gastric Cancer Risk Prediction Within Real-World Clinical ... correlations and multivariable associations: (A) correlations ...

Citation: Harlow ER, Sasala LM, Talbot CE, Desai BJ, Ina J and Miskovsky S (2021) Prevalence and Morphology of the Coracoclavicular Joint: An Osteological Study of 2,724 Subjects Using Univariable and ...

Multivariable logistic regression model, weighted quantile sum (WQS) regression, and Bayesian kernel machine regression (BKMR) models were employed to more comprehensively explore the combined effects ...