Multivariate analysis is commonly used when we have more than one outcome variables for each observation. For instance, a survey of American adults’ physical and mental health might measure each ...

The Unscrambler® is a complete Multivariate Analysis and Experimental Design (DoE) software solution that is equipped with powerful methods, including PCA, Multivariate Curve Resolution (MCR), PLS ...

In today's data-driven world, statistical analysis plays a critical role in uncovering insights, validating hypotheses, and driving decision-making across industries. R, a powerful programming ...

The model-based approach to inference from multivariate data with missing values is reviewed. Regression prediction is most useful when the covariates are predictive of the missing values and the ...

Here are 11 free NPTEL data science and analytics courses from leading IITs cover graph theory, Bayesian modelling, Python, R ...

Sartorius Stedim Biotech (SSB), a leading international partner of the biopharmaceutical industry today announced the new SIMCA ® 16 software for multivariate data analytics is available from its ...

Before you start analyzing, you might want to take a look at your data object’s structure and a few row entries. If it’s a 2-dimensional table of data stored in an R data frame object with rows and ...

Multivariate 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 for several ...