Multivariate count data arise in settings where two or more non‐negative integer outcomes are observed jointly. Such data feature prominently in fields as diverse as ecology (species abundances across ...

High-dimensional model selection in multivariate statistics addresses the challenge of choosing an appropriate statistical model when both the number of variables and the sample size can grow large ...

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 ...

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 ...

Larry Hatcher, Ph.D. and Edward J. Stepanski, Ph.D. Introduction: The Basics of One-Way ANOVA, Between-Groups Design Example with Significant Differences between Experimental Conditions Understanding ...

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 ...

Multivariate data analysis (MVDA) is being used to effectively handle complex datasets generated by process analytical technology (PAT) in biopharmaceutical process development and manufacturing.

A volume in the Chapman and Hall/CRC Statistics in the Social and Behavioural Sciences series. A copy of the book may be ordered from CRC Press (ISBN 13: 978-1584889601). Please use 20% discount code ...