The analysis of multiple-response, repeated-measurement or growth curve models with a multivariate random-effects covariance structure for individuals is considered. Results on estimation, hypothesis ...

The multivariate analysis of variance for a one-factor experiment in a randomized blocks design with several observations per cell usually assumes equality of the covariance matrices of the errors. A ...

MANOVA is a statistical test that extends the scope of the more commonly used ANOVA, that allows differences between three or more independent groups of explanatory (independent or predictor) ...

Analysis of covariance combines some of the features of both regression and analysis of variance. Typically, a continuous variable (the covariate) is introduced into the model of an ...

When several measurements are taken on the same experimental unit (person, plant, machine, and so on), the measurements tend to be correlated with each other. When the measurements represent ...

Given the drawbacks of implementing multivariate analysis for mapping multiple traits in genome-wide association study (GWAS), principal component analysis (PCA) has been widely used to generate ...

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 ...

Explore how covariance reveals relationships between variables, its role in financial planning, and its application in the stock market for better investment strategies.