Kruskal-Wallis Test

A collection of data samples are independent if they come from unrelated populations and the samples do not affect each other. Using the Kruskal-Wallis Test, we can decide whether the population distributions are identical without assuming them to follow the normal distribution.

Example

In the built-in data set named airquality, the daily air quality measurements in New York, May to September 1973, are recorded. The ozone density are presented in the data frame column Ozone.

> head(airquality) 
  Ozone Solar.R Wind Temp Month Day 
1    41     190  7.4   67     5   1 
2    36     118  8.0   72     5   2 
    .....

Problem

Without assuming the data to have normal distribution, test at .05 significance level if the monthly ozone density in New York has identical data distributions from May to September 1973.

Solution

The null hypothesis is that the monthly ozone density are identical populations. To test the hypothesis, we apply the kruskal.test function to compare the independent monthly data. The p-value turns out to be nearly zero (6.901e-06). Hence we reject the null hypothesis.

> kruskal.test(Ozone ~ Month, data = airquality) 
 
        Kruskal-Wallis rank sum test 
 
data:  Ozone by Month 
Kruskal-Wallis chi-squared = 29.267, df = 4, p-value = 6.901e-06

Answer

At .05 significance level, we conclude that the monthly ozone density in New York from May to September 1973 are nonidentical populations.

Tags:

本站仅提供存储服务，所有内容均由用户发布，如发现有害或侵权内容，请点击举报。