Logistic regression is a powerful and versatile tool for modeling binary outcomes, such as yes/no, success/failure, or positive/negative. In this article, you will learn how to use logistic ...

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

When the outcome variable has only two levels or categories, standard binary logistic regression can be used. When it has three or more, we can use other variants of logistic regression. Let us start ...

An important problem in the field of bioinformatics is to identify interactive effects among profiled variables for outcome prediction. In this paper, a logistic regression model with pairwise ...

Two logistic-regression models (M1 and M4) have been developed to predict the outcomes of PULs, based on the results of two serum hCG levels taken at 0 and 48h, respectively, ( Table 1 ). [21, 22 ...

Researchers at EPFL have created a mathematical model that helps explain how breaking language into sequences makes modern AI ...

How binary options work Binary options are incredibly simple and easy to understand. They're also generally very short-lived, so you'll know quickly if you've won or lost with the option you've ...

Therefore, in this paper, a logistic quantile regression (QR) model is provided to fill this gap and deal with continuous bounded outcomes with crash rate prediction. The crash data set from 2003 to ...

Primary and secondary outcome measures Participants underwent audiological investigations and tinnitus pitch and loudness matching measurements, followed by intensive sound masking therapy. The ...

Model I modelled the early evolution of COVID-19 as a logistic function with 6 parameters: where σ (x,θ) represents the number of positive cases at x -th day after the 24th of February. I exploited ...