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 the confidence interval for the variance.
  
  Parameters
  ----------
  lower_bound : float
      The minimum value the lower confidence interval can
      take. The p-value from test_var(lower_bound) must be lower
      than 1 - significance level. Default is .99 confidence
      limit assuming normality
  
  upper_bound : float
      The maximum value the upper confidence interval
      can take. The p-value from test_var(upper_bound) must be lower
      than 1 - significance level.  Default is .99 confidence
      limit assuming normality
  
  sig : float
      The significance level. Default is .05
  
  Returns
  -------
  Interval : tuple
      Confidence interval for the variance
  
  Examples
  --------
  >>> import numpy as np
  >>> import statsmodels.api as sm
  >>> random_numbers = np.random.standard_normal(100)
  >>> el_analysis = sm.emplike.DescStat(random_numbers)
  >>> el_analysis.ci_var()
  (0.7539322567470305, 1.229998852496268)
  >>> el_analysis.ci_var(.5, 2)
  (0.7539322567469926, 1.2299988524962664)
  
  Notes
  -----
  If the function returns the error f(a) and f(b) must have
  different signs, consider lowering lower_bound and raising
  upper_bound.
docstr_tok: ["Returns","the","confidence","interval","for","the","variance",".","Parameters","--","--","--","--","--","lower_bound",":","float","The","minimum","value","the","lower","confidence","interval","can","take",".","The","p-value","from","test_var","(","lower_bound",")","must","be","lower","than","1","-","significance","level",".","Default","is",".99","confidence","limit","assuming","normality","upper_bound",":","float","The","maximum","value","the","upper","confidence","interval","can","take",".","The","p-value","from","test_var","(","upper_bound",")","must","be","lower","than","1","-","significance","level",".","Default","is",".99","confidence","limit","assuming","normality","sig",":","float","The","significance","level",".","Default","is",".05","Returns","--","--","--","-","Interval",":","tuple","Confidence","interval","for","the","variance","Examples","--","--","--","--",">",">",">","import","numpy","as","np",">",">",">","import","statsmodels.api","as","sm",">",">",">","random_numbers","=","np.random.standard_normal","(","100",")",">",">",">","el_analysis","=","sm.emplike.DescStat","(","random_numbers",")",">",">",">","el_analysis.ci_var","(",")","(","0.7539322567470305",",","1.229998852496268",")",">",">",">","el_analysis.ci_var","(",".5",",","2",")","(","0.7539322567469926",",","1.2299988524962664",")","Notes","--","--","-","If","the","function","returns","the","error","f","(","a",")","and","f","(","b",")","must","have","different","signs",",","consider","lowering","lower_bound","and","raising","upper_bound","."]
code: def ci_var(self, lower_bound=None, upper_bound=None, sig=0.05):
      """
      Returns the confidence interval for the variance.
  
      Parameters
      ----------
      lower_bound : float
          The minimum value the lower confidence interval can
          take. The p-value from test_var(lower_bound) must be lower
          than 1 - significance level. Default is .99 confidence
          limit assuming normality
  
      upper_bound : float
          The maximum value the upper confidence interval
          can take. The p-value from test_var(upper_bound) must be lower
          than 1 - significance level.  Default is .99 confidence
          limit assuming normality
  
      sig : float
          The significance level. Default is .05
  
      Returns
      -------
      Interval : tuple
          Confidence interval for the variance
  
      Examples
      --------
      >>> import numpy as np
      >>> import statsmodels.api as sm
      >>> random_numbers = np.random.standard_normal(100)
      >>> el_analysis = sm.emplike.DescStat(random_numbers)
      >>> el_analysis.ci_var()
      (0.7539322567470305, 1.229998852496268)
      >>> el_analysis.ci_var(.5, 2)
      (0.7539322567469926, 1.2299988524962664)
  
      Notes
      -----
      If the function returns the error f(a) and f(b) must have
      different signs, consider lowering lower_bound and raising
      upper_bound.
      """
      endog = self.endog
      if upper_bound is None:
          upper_bound = ((self.nobs - 1) * endog.var()) / (
              chi2.ppf(0.0001, self.nobs - 1)
          )
      if lower_bound is None:
          lower_bound = ((self.nobs - 1) * endog.var()) / (
              chi2.ppf(0.9999, self.nobs - 1)
          )
      self.r0 = chi2.ppf(1 - sig, 1)
      llim = optimize.brentq(
          self._ci_limits_var, lower_bound, endog.var()
      )
      ulim = optimize.brentq(
          self._ci_limits_var, endog.var(), upper_bound
      )
      return llim, ulim
  
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 5 for calling the DescStatUV obj's underlying member method code with example usage: obj.ci_var(lower_bound, upper_bound, sig) and returns: llim,  ulim