Real-world problems and data sets are the backbone of this groundbreaking book. Applied Multivariate Statistics with SAS Software, Second Edition provides a unique approach to the topic, integrating ...

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 ...

Discover how multivariate models use multiple variables for investment forecasting, risk analysis, and decision-making in ...

Multivariate analyses such as principal component analysis were among the first statistical methods employed to extract information from genetic markers. From their early applications to current ...

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.

Let A and B be independent, central Wishart matrices in p variables with common covariance and having m and n degrees of freedom, respectively. The distribution of the largest eigenvalue of (A + B)⁻¹ ...

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 ...