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 -2 x log-likelihood and the p-value for the hypothesized
  kurtosis.
  
  Parameters
  ----------
  kurt0 : float
      Kurtosis value to be tested
  
  return_weights : bool
      If True, function also returns the weights that
      maximize the likelihood ratio. Default is False.
  
  Returns
  -------
  test_results : tuple
      The log-likelihood ratio and p-value of kurt0
docstr_tok: ["Returns","-2","x","log-likelihood","and","the","p-value","for","the","hypothesized","kurtosis",".","Parameters","--","--","--","--","--","kurt0",":","float","Kurtosis","value","to","be","tested","return_weights",":","bool","If","True",",","function","also","returns","the","weights","that","maximize","the","likelihood","ratio",".","Default","is","False",".","Returns","--","--","--","-","test_results",":","tuple","The","log-likelihood","ratio","and","p-value","of","kurt0"]
code: def test_kurt(self, kurt0, return_weights=False):
      """
      Returns -2 x log-likelihood and the p-value for the hypothesized
      kurtosis.
  
      Parameters
      ----------
      kurt0 : float
          Kurtosis value to be tested
  
      return_weights : bool
          If True, function also returns the weights that
          maximize the likelihood ratio. Default is False.
  
      Returns
      -------
      test_results : tuple
          The log-likelihood ratio and p-value of kurt0
      """
      self.kurt0 = kurt0
      start_nuisance = np.array([self.endog.mean(), self.endog.var()])
  
      llr = optimize.fmin_powell(
          self._opt_kurt, start_nuisance, full_output=1, disp=0
      )[1]
      p_val = chi2.sf(llr, 1)
      if return_weights:
          return llr, p_val, self.new_weights.T
      return llr, p_val
  
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 8 for calling the DescStatUV obj's underlying member method code with example usage: obj.test_kurt(kurt0, return_weights) and returns: llr,  p_val, llr,  p_val,  self