class_docstr: A class to compute confidence intervals and hypothesis tests involving
  mean, variance, kurtosis and skewness of a univariate random variable.
  
  Parameters
  ----------
  endog : 1darray
      Data to be analyzed
  
  Attributes
  ----------
  endog : 1darray
      Data to be analyzed
  
  nobs : float
      Number of observations
class_docstr_tok: ["A","class","to","compute","confidence","intervals","and","hypothesis","tests","involving","mean",",","variance",",","kurtosis","and","skewness","of","a","univariate","random","variable",".","Parameters","--","--","--","--","--","endog",":","1darray","Data","to","be","analyzed","Attributes","--","--","--","--","--","endog",":","1darray","Data","to","be","analyzed","nobs",":","float","Number","of","observations"]
docstr: Returns a plot of the confidence region for a univariate
  mean and variance.
  
  Parameters
  ----------
  mu_low : float
      Lowest value of the mean to plot
  
  mu_high : float
      Highest value of the mean to plot
  
  var_low : float
      Lowest value of the variance to plot
  
  var_high : float
      Highest value of the variance to plot
  
  mu_step : float
      Increments to evaluate the mean
  
  var_step : float
      Increments to evaluate the mean
  
  levs : list
      Which values of significance the contour lines will be drawn.
      Default is [.2, .1, .05, .01, .001]
  
  Returns
  -------
  Figure
      The contour plot
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code: def plot_contour(
      self,
      mu_low,
      mu_high,
      var_low,
      var_high,
      mu_step,
      var_step,
      levs=[0.2, 0.1, 0.05, 0.01, 0.001],
  ):
      """
      Returns a plot of the confidence region for a univariate
      mean and variance.
  
      Parameters
      ----------
      mu_low : float
          Lowest value of the mean to plot
  
      mu_high : float
          Highest value of the mean to plot
  
      var_low : float
          Lowest value of the variance to plot
  
      var_high : float
          Highest value of the variance to plot
  
      mu_step : float
          Increments to evaluate the mean
  
      var_step : float
          Increments to evaluate the mean
  
      levs : list
          Which values of significance the contour lines will be drawn.
          Default is [.2, .1, .05, .01, .001]
  
      Returns
      -------
      Figure
          The contour plot
      """
      fig, ax = utils.create_mpl_ax()
      ax.set_ylabel("Variance")
      ax.set_xlabel("Mean")
      mu_vect = list(np.arange(mu_low, mu_high, mu_step))
      var_vect = list(np.arange(var_low, var_high, var_step))
      z = []
      for sig0 in var_vect:
          self.sig2_0 = sig0
          for mu0 in mu_vect:
              z.append(self._opt_var(mu0, pval=True))
      z = np.asarray(z).reshape(len(var_vect), len(mu_vect))
      ax.contour(mu_vect, var_vect, z, levels=levs)
      return fig
  
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 6 for calling the DescStatUV obj's underlying member method code with example usage: obj.plot_contour(mu_low, mu_high, var_low, var_high, mu_step, var_step, levs) and returns: fig