confidence_interval#
- TukeyHSDResult.confidence_interval(confidence_level=0.95)[source]#
Compute the confidence interval for the specified confidence level.
- Parameters:
- confidence_levelfloat, optional
Confidence level for the computed confidence interval of the estimated proportion. Default is .95.
- Returns:
- ci
ConfidenceInterval
object The object has attributes
low
andhigh
that hold the lower and upper bounds of the confidence intervals for each comparison. The high and low values are accessible for each comparison at index(i, j)
between groupsi
andj
.
- ci
References
[1]NIST/SEMATECH e-Handbook of Statistical Methods, “7.4.7.1. Tukey’s Method.” https://www.itl.nist.gov/div898/handbook/prc/section4/prc471.htm, 28 November 2020.
[2]P. A. Games and J. F. Howell, “Pairwise Multiple Comparison Procedures with Unequal N’s and/or Variances: A Monte Carlo Study,” Journal of Educational Statistics, vol. 1, no. 2, pp. 113-125, Jun. 1976, doi: https://doi.org/10.3102/10769986001002113.
Examples
>>> from scipy.stats import tukey_hsd >>> group0 = [24.5, 23.5, 26.4, 27.1, 29.9] >>> group1 = [28.4, 34.2, 29.5, 32.2, 30.1] >>> group2 = [26.1, 28.3, 24.3, 26.2, 27.8] >>> result = tukey_hsd(group0, group1, group2) >>> ci = result.confidence_interval() >>> ci.low array([[-3.649159, -8.249159, -3.909159], [ 0.950841, -3.649159, 0.690841], [-3.389159, -7.989159, -3.649159]]) >>> ci.high array([[ 3.649159, -0.950841, 3.389159], [ 8.249159, 3.649159, 7.989159], [ 3.909159, -0.690841, 3.649159]])