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scipy.stats.

# obrientransform

scipy.stats.obrientransform(*samples)

  Compute the O’Brien transform on input data (any number of arrays).

  Used to test for homogeneity of variance prior to running one-way stats. Each array in `*samples` is one level of a factor. If `f_oneway` is run on the transformed data and found significant, the variances are unequal. From Maxwell and Delaney [1], p.112.

  Parameters:
    sample1, sample2, …array_like

      Any number of arrays.

  Returns:
    obrientransformndarray

      Transformed data for use in an ANOVA. The first dimension of the result corresponds to the sequence of transformed arrays. If the arrays given are all 1-D of the same length, the return value is a 2-D array; otherwise it is a 1-D array of type object, with each element being an ndarray.

  Raises:
    ValueError

      If the mean of the transformed data is not equal to the original variance, indicating a lack of convergence in the O’Brien transform.

  References

  [1]

  S. E. Maxwell and H. D. Delaney, “Designing Experiments and Analyzing Data: A Model Comparison Perspective”, Wadsworth, 1990.

  Examples

  We’ll test the following data sets for differences in their variance.

      >>> x = [10, 11, 13, 9, 7, 12, 12, 9, 10]
      >>> y = [13, 21, 5, 10, 8, 14, 10, 12, 7, 15]

  Apply the O’Brien transform to the data.

      >>> from scipy.stats import obrientransform
      >>> tx, ty = obrientransform(x, y)

  Use `scipy.stats.f_oneway` to apply a one-way ANOVA test to the transformed data.

      >>> from scipy.stats import f_oneway
      >>> F, p = f_oneway(tx, ty)
      >>> p
      0.1314139477040335

  If we require that `p < 0.05` for significance, we cannot conclude that the variances are different.