Multivariate analysis of covariance (MANCOVA) is an extension of analysis of covariance (ANCOVA) methods to cover cases where there is more than one dependent...

In statistics, multivariate analysis of variance (MANOVA) is a procedure for comparing multivariate sample means. As a multivariate procedure, it is used...

Analysis of covariance (ANCOVA) is a general linear model that blends ANOVA and regression. ANCOVA evaluates whether the means of a dependent variable...

Multivariate statistics is a subdivision of statistics encompassing the simultaneous observation and analysis of more than one outcome variable, i.e.,...

statistics, sometimes the covariance matrix of a multivariate random variable is not known but has to be estimated. Estimation of covariance matrices then deals...

orthogonal coordinate system. The multivariate normal distribution is said to be "non-degenerate" when the symmetric covariance matrix Σ {\displaystyle {\boldsymbol...

probability theory and statistics, covariance is a measure of the joint variability of two random variables. The sign of the covariance, therefore, shows the tendency...

regression analysis, factor analysis, canonical correlation analysis, discriminant analysis, as well as more general families of models in the multivariate analysis...

eigendecomposition of the data covariance matrix or singular value decomposition of the data matrix. PCA is the simplest of the true eigenvector-based multivariate analyses...

discriminant analysis (QDA). LDA instead makes the additional simplifying homoscedasticity assumption (i.e. that the class covariances are identical...

of multivariate statistical analysis on metric spaces. It is named after the Swedish forestry statistician Bertil Matérn. It specifies the covariance...

components of the error covariance which, in the model equations have expected values of zero. This is to be contrasted with principal component analysis which...

trend estimation Mixed-design analysis of variance Multivariate analysis of covariance (MANCOVA) Permutational analysis of variance Variance decomposition...

and statistics, a covariance matrix (also known as auto-covariance matrix, dispersion matrix, variance matrix, or variance–covariance matrix) is a square...

calculating the multivariate location and scatter of data are also commonly used when calculating the Mahalanobis distance. The Minimum Covariance Determinant...

in space), and in multivariate random fields (to refer to the covariance of a variable with itself, as opposed to the cross covariance between two different...

It combines elements of classical time series analysis, multivariate statistics, multivariate geometry, dynamical systems and signal processing. Its roots...

observations instead of the variance of scalar observations. One version of this is to use covariance matrices as the multivariate measure of dispersion. Several...

the variance-covariance matrix implied by the proposed factor analysis model and R {\displaystyle R} is the observed variance-covariance matrix. That...

probability and each component mean and covariance is the component specific weighted average of the mean and covariance of the entire sample. Dempster also...

a sample, or two components of a multivariate random variable with a known distribution.[citation needed] Several types of correlation coefficient exist...

by taking the ratio of the covariance of the two variables in question of our numerical dataset, normalized to the square root of their variances. Mathematically...

analysis of the relationship between the two variables. Bivariate analysis is a simple (two variable) special case of multivariate analysis (where multiple...

generalization of Student's t-distribution. If the vector d {\displaystyle d} is Gaussian multivariate-distributed with zero mean and unit covariance matrix N...

is the vector of means for each variable and Σ {\displaystyle {\boldsymbol {\Sigma }}} is the covariance matrix. Unlike the multivariate normal distribution...

conjugate prior of the inverse covariance-matrix of a multivariate-normal random vector. Suppose G is a p × n matrix, each column of which is independently...

any set of N points in the desired domain of your functions, take a multivariate Gaussian whose covariance matrix parameter is the Gram matrix of your N...

samples from a multivariate normal distribution with mean zero and covariance matrix C {\displaystyle C} . We define a linear impact function of a spatial...

canonical-correlation analysis (CCA), also called canonical variates analysis, is a way of inferring information from cross-covariance matrices. If we have...

statistics, a copula is a multivariate cumulative distribution function for which the marginal probability distribution of each variable is uniform on...