class_docstr: _MultivariateOLS results class
class_docstr_tok: ["_MultivariateOLS","results","class"]
code: def __init__(self, fitted_mv_ols):
      if hasattr(fitted_mv_ols, "data") and hasattr(
          fitted_mv_ols.data, "design_info"
      ):
          self.design_info = fitted_mv_ols.data.design_info
      else:
          self.design_info = None
      self.exog_names = fitted_mv_ols.exog_names
      self.endog_names = fitted_mv_ols.endog_names
      self._fittedmod = fitted_mv_ols._fittedmod
  
imports: import numpy
  from numpy.linalg import eigvals, inv, solve, matrix_rank, pinv, svd
  from scipy import stats
  import pandas
  from patsy import DesignInfo
  from statsmodels.compat.pandas import Substitution
  from statsmodels.base.model import Model, LikelihoodModelResults
  import statsmodels.base.wrapper
  from statsmodels.regression.linear_model import RegressionResultsWrapper
  from statsmodels.iolib import summary2
  from statsmodels.tools.decorators import cache_readonly
label_desc: Use image node_id 1 to create a new _MultivariateOLSResults object from inherited base classes: LikelihoodModelResults with example: obj = _MultivariateOLSResults(fitted_mv_ols)