Variables were entered into multivariate stepwise binary logistic regression in order of univariate significance, with IPO the dependent variable. Variables with p<0.05 in multivariate analysis, or ...

Objective We aimed to estimate prevalence and identify determinants of hypertension in adults aged 15–49 years in Tanzania. Design We analysed cross-sectional survey data from the 2022 Tanzania ...

Multivariate analysis of variance (MANOVA) is an extension of the commonly used analysis of variance (ANOVA) method, allowing statistical comparisons across three or more groups of data and involving ...

Binary analysis supports these efforts by enabling deeper visibility into software components, helping companies meet and exceed, and show their commitment to these evolving regulatory standards. The ...

Multivariate analysis directly influences the complexity of predictive models. With more variables considered, models can become more intricate and potentially more accurate.

Who Uses Multivariate Models Multivariate models—like the Monte Carlo model—are popular statistical tools that use multiple variables to forecast possible outcomes.

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 ...

Causal effect estimation of individual heterogeneity is a core issue in the field of causal inference, and its application in medicine poses an active and challenging problem. In high-risk ...

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 ...