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

“Multivariate” refers to multiple outcome variables assessed at once, this contrasts with “multiple” or “multivariable,” which usually refers to multiple explanatory variables. Other key techniques ...

We explore how combining multi-disciplinary cutting-edge tools, including multivariate analysis, can help in identifying multi-gene targets and improve patient outcomes through precision medicine. For ...

Why binary analysis? Binary analysis forms the cornerstone of the transparency and continuous visibility needed for a robust and effective product security testing framework. Binary analysis exposes ...

Multivariate models—like the Monte Carlo model—are popular statistical tools that use multiple variables to forecast possible outcomes. When employing a multivariate model, a user changes the ...

Multivariate analysis is a powerful tool in data science that allows you to understand the complex relationships between multiple variables simultaneously. By analyzing more than one outcome ...

This model can estimate binary causal effects more accurately and in less time. Two cyclic structures were added to the model. Data correction method was introduced and improved to transform discrete ...

Multivariate analysis is a broad term that covers different methods of analyzing data with more than one variable. For example, if you have a dataset of customers with attributes like age, gender ...

First, thank you very much for this fantastic package! I've had much benefit from it. I wonder, however, how does one use multlcmm() for multivariate binary outcome data? On this page it is stated ...

They used multivariate logistic regression analysis to evaluate the relationships among working hours, social engagement (predictor variables), and depressive symptoms (binary outcome variable).