Kruskal-Wallis rank sum test

Given that we found in the last exercise that the homogeneity of variance assumption of linear modeling was violated, we may want to try an alternative.

One non-parametric alternative to ANOVA is the Kruskal-Wallis rank sum test. For those with some statistics knowledge, it is an extension of the Mann-Whitney U test for when there are more than two groups, like with our grade variable. For us, the null hypothesis for this test would be that all of the int_rates have the same ranking by grade.

The Kruskal-Wallis rank sum test can be conducted using the kruskal.test() function, available in base R. Luckily for you, the use of this function is very similar to using lm() or aov(): you input a formula and a dataset, and a result is returned.

Diese Übung ist Teil des Kurses

Experimental Design in R

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Anleitung zur Übung

Use kruskal.test() to examine whether int_rate varies by grade when a non-parametric model is employed.

Interaktive Übung

Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.

# Conduct the Kruskal-Wallis rank sum test
kruskal.test(___,
             data = ___)

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