Determining Chi-Square critical value in R
In order to determine Chi-Square critical value, R provides us qchisq() function that has the following syntax:
Syntax: qchisq(p, df, lower.tail=TRUE)
Parameters:
- p: The significance level to use
- df: The degrees of freedom
- lower.tail = TRUE: Then the probability to the left of p in the F distribution is returned
- lower.tail = FALSE: Then the probability to the right is returned.
- Note that by default is TRUE.
Return Type: Returns the critical-value from the Critical-Square distribution
Let us consider an example in which we need to determine the Chi-Square critical value for the following data:
- df = 7
- significance level = 0.01
R
# Determine the Chi-Square critical value qchisq (p = .01, df = 7, lower.tail = FALSE ) |
Output:
Hence, the Chi-Square critical value for a significance level of 0.01 and degrees of freedom = 7 comes out to be equal to 18.475. Hence, if the Chi-Square test statistic comes out to be greater than 18.475 then the results of the test would be considered statistically significant.
How to Find the Chi-Square Critical Value in R
In this article, we are going to see how to find the Chi-Square Critical Value in R programming language.
When the Chi-Square test is conducted, we get test statistics as an outcome. In order to find out whether the results of the Chi-Square are statistically significant, the test statistic is compared with the Chi-Square critical value. If the outcome of the test-statistic comes out to be greater than the Chi-Square statistic, the results of the test are considered statistically significant.
In order to Chi-Square critical value, we need the following data beforehand:
- A significance level
- Degrees of freedom