code: def _compute_statistical_outputs(
      elementary_effects: np.ndarray,
      num_vars: int,
      num_resamples: int,
      conf_level: float,
      groups: np.ndarray,
      unique_group_names: List,
  ) -> ResultDict:
      """Computes the statistical parameters related to Morris method.
  
      Parameters
      ----------
      elementary_effects: np.ndarray
          Morris elementary effects.
      num_vars: int
          Number of problem's variables
      num_resamples: int
          Number of resamples
      conf_level: float
          Confidence level
      groups: np.ndarray
          Array defining the distribution of groups
      unique_group_names: List
          Names of the groups
  
      Returns
      -------
      Si: ResultDict
          Morris statistical parameters.
      """
  
      Si = ResultDict(
          (k, [None] * num_vars)
          for k in ["names", "mu", "mu_star", "sigma", "mu_star_conf"]
      )
      Si["names"] = unique_group_names
  
      mu = np.average(elementary_effects, 1)
      mu_star = np.average(np.abs(elementary_effects), 1)
      sigma = np.std(elementary_effects, axis=1, ddof=1)
      mu_star_conf = _compute_mu_star_confidence(
          elementary_effects, num_vars, num_resamples, conf_level
      )
  
      Si["mu"] = _compute_grouped_metric(mu, groups)
      Si["mu_star"] = _compute_grouped_metric(mu_star, groups)
      Si["sigma"] = _compute_grouped_sigma(sigma, groups)
      Si["mu_star_conf"] = _compute_grouped_metric(mu_star_conf, groups)
  
      return Si
  
imports: from typing import Dict, List
  import numpy
  from scipy.stats import norm
  from .None import common_args
  from ..util import read_param_file, compute_groups_matrix, ResultDict, _define_problem_with_groups, _compute_delta, _check_groups
label_desc: Use image node_id 2 for calling a global function with example usage: _compute_statistical_outputs(elementary_effects, num_vars, num_resamples, conf_level, groups, unique_group_names) and returns: Si