Previous methods struggle to incorporate real-time data or account for nonlinear interactions among macroeconomic variables.

Department of Mathematics, Statistics and Actuarial Science, Faculty of Health, Natural Resources and Applied Sciences, Namibia University of Science and Technology, Windhoek, Namibia. Food insecurity ...

Learn how to use logistic regression to predict the probability of a binary outcome based on explanatory variables, and understand the assumptions and interpretations of the model.

Compared to separate binary regression models, one of the advantages of ordinal logistic regression is that it includes fewer unknown quantities, here odds ratios, in the model. This results in a ...

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

Background Stillbirths and associated outcomes remain a significant concern in sub-Saharan Africa (SSA), with approximately 41% of global stillbirths. Design Our cross-sectional analysis included a ...

In binary logistic regression, data is first analyzed, then the probability of individual events is estimated by observing previous data, and then a binary classification model is created. An ...

Implementing binary / multiple logistic regression models, for the well known mnist dataset while also creating the support vector machine(SVM) models . support-vector-machines radial-basis-function ...