class_docstr: 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.
  
  Attributes
  ----------
  endog : ndarray
      See Parameters.
  exog : ndarray
      See Parameters.
  
  Notes
  -----
  MANOVA is used though the `mv_test` function, and `fit` is not used.
  
  The ``from_formula`` interface is the recommended method to specify
  a model and simplifies testing without needing to manually configure
  the contrast matrices.
  
  References
  ----------
  .. [*] ftp://public.dhe.ibm.com/software/analytics/spss/documentation/
     statistics/20.0/en/client/Manuals/IBM_SPSS_Statistics_Algorithms.pdf
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code: def __init__(
      self, endog, exog, missing="none", hasconst=None, **kwargs
  ):
      if len(endog.shape) == 1 or endog.shape[1] == 1:
          raise ValueError(
              "There must be more than one dependent variable"
              " to fit MANOVA!"
          )
      super().__init__(
          endog, exog, missing=missing, hasconst=hasconst, **kwargs
      )
      self._fittedmod = _multivariate_ols_fit(self.endog, self.exog)
  
file_path: statsmodels/statsmodels/multivariate/manova.py
imports: import numpy
  from statsmodels.compat.pandas import Substitution
  from statsmodels.base.model import Model
  from .multivariate_ols import MultivariateTestResults
  from .multivariate_ols import _multivariate_ols_fit
  from .multivariate_ols import _multivariate_ols_test, _hypotheses_doc
label_desc: Use image node_id 1 to create a new MANOVA object from inherited base classes: Model with example: obj = MANOVA(endog, exog, missing, hasconst)