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

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

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

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

the multivariate analysis of variance and covariance analyses (MANOVA, ANOVA, ANCOVA). In addition to being thought of as a form of multiple regression...

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

related to canonical correlation analysis (CCA). CCA defines coordinate systems that optimally describe the cross-covariance between two datasets while PCA...

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

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

MANOVA is a generalized form of univariate analysis of variance (ANOVA), although, unlike univariate ANOVA, it uses the covariance between outcome variables...

Estimation of covariance matrices Important publications in multivariate analysis Multivariate testing in marketing Structured data analysis (statistics)...

ANOVA-simultaneous component analysis Analysis of covariance (ANCOVA) Analysis of molecular variance (AMOVA) Analysis of rhythmic variance (ANORVA) Expected...

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

the variance of the other changes. Covariance can be difficult to interpret across studies because it depends on the scale or level of measurement used...

Matérn covariance, also called the Matérn kernel, is a covariance function used in spatial statistics, geostatistics, machine learning, image analysis, and...

Alignments of random points Almost surely Alpha beta filter Alternative hypothesis Analyse-it – software Analysis of categorical data Analysis of covariance Analysis...

with one another. Mathematically, the variance–covariance matrix of the errors is diagonal. A handful of conditions are sufficient for the least-squares...

of SEM is, "...a class of methodologies that seeks to represent hypotheses about the means, variances, and covariances of observed data in terms of a...

one or both are not) and covariance (which reflects the scale variables are measured on). The slope, in regression analysis, also reflects the relationship...

V {\displaystyle V} is the covariance matrix of those normal order statistics. There is no name for the distribution of W {\displaystyle W} . The cutoff...

(empirical average), and the sample covariance or empirical covariance are statistics computed from a sample of data on one or more random variables...

i\leq k} and 1 ≤ j ≤ k {\displaystyle 1\leq j\leq k} . The inverse of the covariance matrix is called the precision matrix, denoted by Q = Σ − 1 {\displaystyle...

discrimination threshold) of the detection probability in the y-axis versus the CDF of the false positive probability on the x-axis. ROC analysis provides tools...

theory and statistics, the covariance function describes how much two random variables change together (their covariance) with varying spatial or temporal...

linear correlation between two sets of data. It is the ratio between the covariance of two variables and the product of their standard deviations; thus,...

model that models the log-odds of an event as a linear combination of one or more independent variables. In regression analysis, logistic regression (or logit...

estimate the covariance or the spectrum of the process without assuming that the process has any particular structure. Methods of time series analysis may also...

of the normality assumption. The one-way ANOVA can be generalized to the factorial and multivariate layouts, as well as to the analysis of covariance...

average value. It is the second central moment of a distribution, and the covariance of the random variable with itself, and it is often represented by σ 2...

The standard deviation of the sum of two random variables can be related to their individual standard deviations and the covariance between them: σ ( X +...