Topics include counting techniques, discrete and continuous univariate and multivariate random variables & common distributions, probability, expectation, variance, confidence intervals, the Central ...

This study develops a multivariate, nonnormal density function that can accurately and separately account for skewness, kurtosis, heteroskedasticity, and the correlation among the random variables of ...

‘High-dimensional’ data are multivariate phenotypic data that use more variables to describe a phenotype than the number of phenotypes to analyze. High-dimensional data present a roadblock for ...

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 ...

The analysis of multiple outcomes is becoming increasingly common in modern biomedical studies. It is wellknown that joint statistical models for multiple outcomes are more flexible and more powerful ...

Multivariate Analysis in R Multivariate Analysis in R Course Topics Multivariate analysis is commonly used when we have more than one outcome variables for each observation. For instance, a survey of ...

How to Run a Multivariate Regression in Excel. Multivariate regression enables you to relate one dependent variable to multiple independent variables you've derived from surveys or measurements.

Course TopicsMultivariate analysis in statistics is a set of useful methods for analyzing data when there are more than one variables under consideration. Multivariate analysis techniques may be used ...

When using multivariate analysis, the things you want to examine are usually called the dependent variables, while the factors that influence what you're examining are the independent variables.