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Multivariate analysis of variance compares mean vectors of several groups while accounting for correlations between their response variables. In the one-way normal model, independent observations $Y_{ri}\sim N_p({\mu }_r,\Sigma )$ share a positive-definite covariance matrix. Put $E={\sum }_{r,i}(Y_{ri}-{\bar{Y}}_r)(Y_{ri}-{\bar{Y}}_r{)}^T$ and $H={\sum }_r{n}_r({\bar{Y}}_r-\bar{Y})({\bar{Y}}_r-\bar{Y}{)}^T$. Under equal means, these matrices have independent Wishart distributions with degrees of freedom $N-g$ and $g-1$, respectively. This follows by separating the Gaussian data into orthogonal within-group and between-group projections. The Wilks lambda statistic tests equality of the means using both matrices, rather than independently testing each coordinate.

Multivariate analysis of variance tests differences between group mean vectors when each experimental unit has several correlated responses. For independent $p$-dimensional observations in $g$ groups, use a common positive-definite covariance matrix $\Sigma$ and the multivariate normal model $Y_{ri}\sim N_p({\mu }_r,\Sigma )$. The null hypothesis is equality of all $g$ means.

Write $n={\sum }_r{n}_r$, group means ${\bar{y}}_r$, and the size-weighted grand mean $\bar{y}$. The within-group and between-group scatter matrices areExpanding deviations about the group means yields total scatter $T=E+H$, with zero cross terms. Under the null, orthogonal normal projections give independent Wishart distributions $E\sim W_p(\Sigma ,n-g)$ and $H\sim W_p(\Sigma ,g-1)$.

With nonsingular $E$, the Wilks lambda statistic iswhere the nonnegative ${\theta }_j$ are eigenvalues of $E^{-1/2}H{E}^{-1/2}$. Maximizing the common covariance under the two mean models gives the likelihood ratio ${\Lambda }^{n/2}$, so small $\Lambda$ indicates large group separation relative to within-group spread. For fixed $p,g$ and large group sizes, the Wilks theorem gives $-n\log \Lambda \Rightarrow {\chi }_{p(g-1)}^2$ under the null; a finite-sample reference distribution can also be used.

The lower-right sketch shows two elongated groups whose centroid difference lies nearly across, rather than along, their long axes. Their correlation makes this multivariate separation much clearer than either marginal mean shift alone. Nonsingular linear rescaling changes $E$ and $H$ by congruence, leaving the determinant ratio unchanged. MANOVA measures mean separation against the full within-group covariance geometry. Its interpretation requires independent observations and an adequate common-covariance model; separate coordinatewise tests discard that geometry and raise multiple-testing issues.

Let $n={\sum }_{\nu =1}^g{n}_{\nu }$, ${\bar{x}}_{\nu }=n_{\nu }^{-1}{\sum }_j{x}_j^{(\nu )}$ and $\bar{x}=n^{-1}{\sum }_{\nu }{n}_{\nu }{\bar{x}}_{\nu }$. Define the within-group and between-group scatter matrices byThe within-group and between-group scatter decomposition gives total scatter $T=W+B$: in each group the residuals about ${\bar{x}}_{\nu }$ sum to zero, so all cross terms vanish. Under unrestricted group means, the maximum-likelihood estimators are ${\hat{\mu }}_{\nu }={\bar{x}}_{\nu }$ and $\hat{V}=W/n$. Under the common-mean null hypothesis, they are $\hat{\mu }=\bar{x}$ and ${\hat{V}}_0=(W+B)/n$.

For either fitted scatter matrix $A$, substitution into the log-likelihood givessince $\mathrm{tr}[(A/n{)}^{-1}A]=np$. Thus the maximized likelihood ratio isA generalized likelihood-ratio test rejects for small ${\Lambda }_{\mathrm{LR}}$, equivalentlyThe cutoff $c$ is chosen for the desired significance level. The determinant ratio $|W|/|W+B|$ itself is the Wilks lambda statistic. The derivation assumes $W$ is positive-definite, which for continuous normal samples requires enough within-group degrees of freedom, $n-g\ge p$; singular $W$ does not support this ordinary determinant-based test without a modified model.

Multivariate analysis of variance extends analysis of variance to vector responses. For example, a treatment may affect several correlated physiological measurements jointly. Separate univariate tests do not use the correlation geometry and require adjustment if they are interpreted as one multiple-testing procedure. A joint test asks whether group mean vectors differ in any direction.

In a one-way model, take independent $Y_{ri}\sim N_p({\mu }_r,\Sigma )$, $r=1,\dots ,g$, $i=1,\dots ,n_r$, with a common positive-definite covariance. Put $N={\sum }_r{n}_r$, and let ${\bar{Y}}_r$ and $\bar{Y}$ be group and overall sample means. The within-group and between-group scatter matrices areExpanding around each group mean makes the cross terms vanish, giving total scatter $E+H$. To test $H_0:{\mu }_1=\cdots ={\mu }_g$, use the Wilks lambda statisticwhere ${\theta }_j$ solve the generalized eigenvalue problem $Hv=\theta Ev$ and $E$ is nonsingular. Under the alternative, maximize the normal likelihood at the group means and covariance $E/N$; under the null, use the overall mean and covariance $(E+H)/N$. Their likelihood ratio is ${\Lambda }^{N/2}$, so small $\Lambda$ rejects equal means. Under the null, orthogonal projections of the Gaussian data give independent $E\sim W_p(\Sigma ,N-g)$ and $H\sim W_p(\Sigma ,g-1)$, supplying its null calibration. Here $E$ is nonsingular almost surely when $N-g\ge p$. Merely having more total observations than responses is not enough if group fitting consumes too many statistical degrees of freedom.

The test is invariant under a nonsingular linear change of response coordinates: both determinants acquire the same factor $|A{|}^2$. The directions associated with large ${\theta }_j$ identify mean separation relative to residual variability. With one response it becomes the ordinary ANOVA F-test. With two groups, writing $S_p=E/(N-2)$ and $d={\bar{Y}}_1-{\bar{Y}}_2$, the version using Hotelling's T-squared statistic iswith $N>p+1$. This formula demonstrates explicitly how the covariance weights a group difference.

The sketch shows hypothetical groups with strongly positively correlated responses and mean separation along their low-variance contrast. Marginal comparisons can look weak even when the joint comparison is informative. The ellipses represent individual-response covariance, not confidence regions for the means. Independence of observations, approximate multivariate normality and common covariance are substantive assumptions; outliers and unequal covariance can invalidate the usual calibration. Follow-up contrasts can identify the affected responses or groups, with appropriate multiplicity control. MANOVA uses shared covariance to test a multivariate mean difference, rather than testing unrelated coordinates in isolation.