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 ...

This course is available on the MRes in Management (Marketing), MSc in Data Science, MSc in Health Data Science, MSc in Marketing, MSc in Statistics, MSc in Statistics (Financial Statistics), MSc in ...

Missing data are common in survey data sets. Enrolled subjects do not often have data recorded for all variables of interest. The inappropriate handling of them may negatively affect the inferences ...

In semiconductor manufacturing, especially in electrical test data, but also in other parameters, there are often sets of parameters that are very highly correlated. Even a change in the correlation ...

This video gives a demonstration on the application of multivariate statistical processes to various data sets applicable to forensics at Pittcon 2013. Examples comprise infrared spectra of controlled ...

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 ...

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 ...

Here is a list of the best free statistical analysis software for Windows 11/10. If you have a large dataset of numerical data and want to evaluate and analyze it, this guide is for you. In this ...