# statsmodels.multivariate.manova.MANOVA¶

class statsmodels.multivariate.manova.MANOVA(endog, exog, missing='none', hasconst=None, **kwargs)[source]

Multivariate analysis of variance The implementation of MANOVA is based on multivariate regression and does not assume that the explanatory variables are categorical. Any type of variables as in regression is allowed.

Parameters: endog (array_like) – Dependent variables. A nobs x k_endog array where nobs is the number of observations and k_endog is the number of dependent variables. exog (array_like) – Independent variables. A nobs x k_exog array where nobs is the number of observations and k_exog is the number of independent variables. An intercept is not included by default and should be added by the user. Models specified using a formula include an intercept by default.
endog

array – See Parameters.

exog

array – See Parameters.

References

Methods

 fit() Fit a model to data. from_formula(formula, data[, subset, drop_cols]) Create a Model from a formula and dataframe. mv_test([hypotheses]) Linear hypotheses testing predict(params[, exog]) After a model has been fit predict returns the fitted values.

Attributes

 endog_names Names of endogenous variables exog_names Names of exogenous variables