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"]
code: def __init__(self, endog):
      self.endog = np.squeeze(endog)
      self.nobs = endog.shape[0]
  
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 1 to create a new DescStatUV object from inherited base classes: _OptFuncts with example: obj = DescStatUV(endog)