class_docstr: A class for conducting inference on multivariate means and correlation.
  
  Parameters
  ----------
  endog : ndarray
      Data to be analyzed
  
  Attributes
  ----------
  endog : ndarray
      Data to be analyzed
  
  nobs : float
      Number of observations
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docstr: Returns the confidence intervals for the correlation coefficient
  
  Parameters
  ----------
  sig : float
      The significance level.  Default is .05
  
  upper_bound : float
      Maximum value the upper confidence limit can be.
      Default is  99% confidence limit assuming normality.
  
  lower_bound : float
      Minimum value the lower confidence limit can be.
      Default is 99% confidence limit assuming normality.
  
  Returns
  -------
  interval : tuple
      Confidence interval for the correlation
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code: def ci_corr(self, sig=0.05, upper_bound=None, lower_bound=None):
      """
      Returns the confidence intervals for the correlation coefficient
  
      Parameters
      ----------
      sig : float
          The significance level.  Default is .05
  
      upper_bound : float
          Maximum value the upper confidence limit can be.
          Default is  99% confidence limit assuming normality.
  
      lower_bound : float
          Minimum value the lower confidence limit can be.
          Default is 99% confidence limit assuming normality.
  
      Returns
      -------
      interval : tuple
          Confidence interval for the correlation
      """
      endog = self.endog
      nobs = self.nobs
      self.r0 = chi2.ppf(1 - sig, 1)
      point_est = np.corrcoef(endog[:, 0], endog[:, 1])[0, 1]
      if upper_bound is None:
          upper_bound = min(
              0.999,
              point_est
              + 2.5 * ((1.0 - point_est**2.0) / (nobs - 2.0)) ** 0.5,
          )
  
      if lower_bound is None:
          lower_bound = max(
              -0.999,
              point_est
              - 2.5
              * (np.sqrt((1.0 - point_est**2.0) / (nobs - 2.0))),
          )
  
      llim = optimize.brenth(
          self._ci_limits_corr, lower_bound, point_est
      )
      ulim = optimize.brenth(
          self._ci_limits_corr, point_est, upper_bound
      )
      return llim, ulim
  
imports: import numpy
  from scipy import optimize
  from scipy.stats import chi2, skew, kurtosis
  from statsmodels.base.optimizer import _fit_newton
  import itertools
  from statsmodels.graphics import utils
label_desc: Use image node_id 5 for calling the DescStatMV obj's underlying member method code with example usage: obj.ci_corr(sig, upper_bound, lower_bound) and returns: llim,  ulim