code: def confint_mvmean(data, lin_transf=None, alpha=0.5, simult=False):
      """Confidence interval for linear transformation of a multivariate mean
  
      Either pointwise or simultaneous confidence intervals are returned.
  
      Parameters
      ----------
      data : array_like
          data with observations in rows and variables in columns
      lin_transf : array_like or None
          The linear transformation or contrast matrix for transforming the
          vector of means. If this is None, then the identity matrix is used
          which specifies the means themselves.
      alpha : float in (0, 1)
          confidence level for the confidence interval, commonly used is
          alpha=0.05.
      simult : bool
          If ``simult`` is False (default), then the pointwise confidence
          interval is returned.
          Otherwise, a simultaneous confidence interval is returned.
          Warning: additional simultaneous confidence intervals might be added
          and the default for those might change.
  
      Returns
      -------
      low : ndarray
          lower confidence bound on the linear transformed
      upp : ndarray
          upper confidence bound on the linear transformed
      values : ndarray
          mean or their linear transformation, center of the confidence region
  
      Notes
      -----
      Pointwise confidence interval is based on Johnson and Wichern
      equation (5-21) page 224.
  
      Simultaneous confidence interval is based on Johnson and Wichern
      Result 5.3 page 225.
      This looks like Sheffe simultaneous confidence intervals.
  
      Bonferroni corrected simultaneous confidence interval might be added in
      future
  
      References
      ----------
      Johnson, Richard A., and Dean W. Wichern. 2007. Applied Multivariate
      Statistical Analysis. 6th ed. Upper Saddle River, N.J: Pearson Prentice
      Hall.
      """
      x = np.asarray(data)
      nobs, k_vars = x.shape
      if lin_transf is None:
          lin_transf = np.eye(k_vars)
      mean = x.mean(0)
      cov = np.cov(x, rowvar=False, ddof=0)
  
      ci = confint_mvmean_fromstats(
          mean,
          cov,
          nobs,
          lin_transf=lin_transf,
          alpha=alpha,
          simult=simult,
      )
      return ci
  
file_path: statsmodels/statsmodels/stats/multivariate.py
imports: import numpy
  from scipy import stats
  from statsmodels.stats.moment_helpers import cov2corr
  from statsmodels.stats.base import HolderTuple
  from statsmodels.tools.validation import array_like
label_desc: Use image node_id 4 for calling a global function with example usage: confint_mvmean(data, lin_transf, alpha, simult) and returns: ci