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A Chi-Square Test of Independence is used to determine whether or not there is a significant association between two categorical variables. This tutorial explains how to perform a Chi-Square Test of Independence in R.
The “chisq.test ()” function is an in-built function of R that allows you to do this. It neatly tells you all you need to know about the independence of variables in a dataset to conclude whether they are related or not.
The chisq.test function in R conducts Pearson’s Chi-squared tests for independence, goodness-of-fit and homogeneity, analyzing categorical data relationships. The function also supports Yates’ correction and Monte Carlo simulation for p-values.
Learn how to create a contingency table and perform chi-square tests in R using the chisq.test() function. Explore practical applications and interpret results.
chisq.test performs chi-squared contingency table tests and goodness-of-fit tests. Usage. chisq.test(x, y = NULL, correct = TRUE, p = rep(1/length(x), length(x)), rescale.p = FALSE, simulate.p.value = FALSE, B = 2000) Arguments. x. a numeric vector or matrix. x and y can also both be factors. y. a numeric vector; ignored if x is a matrix.
In R Programming Language Chi-square statistics is used to investigate whether distributions of categorical variables differ from one another. The chi-square test is also useful while comparing the tallies or counts of categorical responses between two(or more) independent groups.
The chi-square test evaluates whether there is a significant association between the categories of the two variables. This article describes the basics of chi-square test and provides practical examples using R software.