class_docstr: Results class for the Jarque-Bera-test for nonnormality.
  
  Parameters
  ----------
  test_statistic : float
      The test's test statistic.
  crit_value : float
      The test's critical value.
  pvalue : float
      The test's p-value.
  df : int
      Degrees of freedom.
  signif : float
      Significance level.
class_docstr_tok: ["Results","class","for","the","Jarque-Bera-test","for","nonnormality",".","Parameters","--","--","--","--","--","test_statistic",":","float","The","test","'s","test","statistic",".","crit_value",":","float","The","test","'s","critical","value",".","pvalue",":","float","The","test","'s","p-value",".","df",":","int","Degrees","of","freedom",".","signif",":","float","Significance","level","."]
code: def __init__(self, test_statistic, crit_value, pvalue, df, signif):
      method = "Jarque-Bera"
      title = "normality (skew and kurtosis) test"
      h0 = "H_0: data generated by normally-distributed process"
      super().__init__(
          test_statistic,
          crit_value,
          pvalue,
          df,
          signif,
          method,
          title,
          h0,
      )
  
file_path: statsmodels/statsmodels/tsa/vector_ar/hypothesis_test_results.py
imports: import numpy
  from statsmodels.iolib.table import SimpleTable
label_desc: Use image node_id 1 to create a new NormalityTestResults object from inherited base classes: HypothesisTestResults with example: obj = NormalityTestResults(test_statistic, crit_value, pvalue, df, signif)