Interfaces for the NAG Mark 29.2 anova Chapter.
anova - Analysis of Variance
This module is concerned with methods for analysing the results of designed experiments. The range of experiments covered include:
single factor designs with equal sized blocks such as randomized complete block and balanced incomplete block designs,
row and column designs such as Latin squares, and
complete factorial designs.
Further designs may be analysed by combining the analyses provided by multiple calls to functions or by using general linear model functions provided in submodule
This subpackage contains examples for the
anovamodule. See also the Examples subsection.
Analysis of variance for
General linear model
generate dummy variables and orthogonal polynomials:
Inferences on means
intraclass correlation (ICC):
For full information please refer to the NAG Library document
Intraclass correlation (ICC) for assessing rater reliability.
>>> main() naginterfaces.library.anova.icc Python Example Results. Interrater reliability for a one-factor design. Intraclass Correlation : 0.17 Lower Limit for 95.0% CI : -0.13 Upper Limit for 95.0% CI : 0.72 F statistic : 1.79 Degrees of Freedom 1 : 5.0 Degrees of Freedom 2 : 18.0 p-value : 0.165