Working with the Pearson's chi-squared tests

In this recipe, we introduced Pearson's chi-squared test, which is used to examine whether the distribution of categorical variables of two groups differ. We will discuss how to conduct Pearson's chi-squared Test in R.

Getting ready

In this recipe, we will use the chisq.test function that originated from the stat package.

How to do it…

Perform the following steps to conduct a Pearson's chi-squared test:

1. First, build a matrix containing the number of male and female smokers and nonsmokers:

   >mat<- matrix(c(2047, 2522, 3512, 1919), nrow = 2, dimnames = list(c("smoke","non-smoke"), c("male","female")))
   >mat
   malefemale
   smoke2047   3512
   non-smoke 2522   1919
   

2. Then, plot the portion of male and female smokers and nonsmokers in a mosaic plot:

   >mosaicplot(mat, main="Portion of male and female smokers/non-smokers", color = TRUE)
   

   

   Figure 9: The mosaic plot

3. Next, perform a Pearson's chi-squared test on the contingency table to test whether the factor...