code: def confint_mvmean_fromstats(
      mean, cov, nobs, lin_transf=None, alpha=0.05, simult=False
  ):
      """Confidence interval for linear transformation of a multivariate mean
  
      Either pointwise or simultaneous confidence intervals are returned.
      Data is provided in the form of summary statistics, mean, cov, nobs.
  
      Parameters
      ----------
      mean : ndarray
      cov : ndarray
      nobs : int
      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 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.
  
      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.
  
      """
      mean = np.asarray(mean)
      cov = np.asarray(cov)
      c = np.atleast_2d(lin_transf)
      k_vars = len(mean)
  
      if simult is False:
          values = c.dot(mean)
          quad_form = (c * cov.dot(c.T).T).sum(1)
          df = nobs - 1
          t_critval = stats.t.isf(alpha / 2, df)
          ci_diff = np.sqrt(quad_form / df) * t_critval
          low = values - ci_diff
          upp = values + ci_diff
      else:
          values = c.dot(mean)
          quad_form = (c * cov.dot(c.T).T).sum(1)
          factor = (nobs - 1) * k_vars / (nobs - k_vars) / nobs
          df = (k_vars, nobs - k_vars)
          f_critval = stats.f.isf(alpha, df[0], df[1])
          ci_diff = np.sqrt(factor * quad_form * f_critval)
          low = values - ci_diff
          upp = values + ci_diff
  
      return low, upp, values
  
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 5 for calling a global function with example usage: confint_mvmean_fromstats(mean, cov, nobs, lin_transf, alpha, simult) and returns: low,  upp,  values