The Chi-Square Distribution in R

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The Chi-square distribution is a continuous probability distribution widely used in statistical inference and hypothesis testing. It’s the distribution of the sum of the squares of k independent standard normal random variables. The chi-square distribution is a special case of the gamma distribution, often applied in scenarios such as the chi-square test for goodness of fit, independence test, and in estimating variance of normally distributed populations.

In this detailed exploration, we’ll review the chi-square distribution, how to generate and work with chi-square distributions in R, the functions linked to chi-square distributions, and practical applications of the chi-square distribution in R.

Understanding the Chi-Square Distribution

A chi-square distribution is defined by its degrees of freedom, typically denoted by df or k, which equals the number of standard normal deviates being summed.

The probability density function of a chi-square distribution is given by:

P(X = x) = (1 / (2^(k/2) * Γ(k/2))) * x^(k/2 - 1) * e^(-x/2)

where Γ(k/2) is the gamma function at k/2.

The mean and variance of a chi-square distribution are k and 2k respectively, where k is the degree of freedom.

Chi-Square Distribution Functions in R

R provides four functions to work with the chi-square distribution:

  1. dchisq(x, df, ncp = 0, log = FALSE): The density function. This gives the height of the probability distribution at x. If log = TRUE, it returns the log-density.
  2. pchisq(q, df, ncp = 0, lower.tail = TRUE, log.p = FALSE): The distribution function. This gives the cumulative probability of q or less. If lower.tail = FALSE, it returns the survival function 1 - pchisq(q). If log.p = TRUE, it gives the log-cumulative probabilities.
  3. qchisq(p, df, ncp = 0, lower.tail = TRUE, log.p = FALSE): The quantile function. This gives the p quantile of the chi-square distribution.
  4. rchisq(n, df, ncp = 0): This generates n random numbers from a chi-square distribution.

Note: the ncp parameter, if non-zero, uses a non-central chi-square distribution, which is not used as frequently.

Generating a Chi-Square Distribution in R

You can generate a chi-square distribution in R using the rchisq() function. Here’s an example:

set.seed(123)  # for reproducibility
x <- rchisq(1000, df = 3)

This code generates a dataset x of 1000 observations drawn from a chi-square distribution with 3 degrees of freedom.

Visualizing a Chi-Square Distribution in R

You can visualize a chi-square distribution using a histogram or a density plot. Here’s an example:

hist(x, probability = TRUE, breaks = 30, 
     main = "Chi-Square Distribution",
     xlab = "Value", 
     ylab = "Density")

curve(dchisq(x, df = 3), add = TRUE, col = "blue", lwd = 2)

Computing Probability and Quantiles

You can calculate the probability of obtaining a certain value using the dchisq() function. Similarly, pchisq() and qchisq() can be used to find the cumulative probability and the value for a certain percentile (quantile), respectively.

Here’s an example:

# Density at x = 2
density <- dchisq(2, df = 3)

# Cumulative probability at x = 2
cum_prob <- pchisq(2, df = 3)

# 90th percentile of the chi-square distribution
quantile <- qchisq(0.90, df = 3)

Applications of Chi-Square Distribution in R

The chi-square distribution has numerous applications in R:

  1. Goodness-of-Fit Tests: You can use the chi-square test to determine if observed data fits a certain theoretical distribution.
  2. Independence Tests: The chi-square test is also used in contingency tables to check if two categorical variables are independent.
  3. Confidence Interval Estimation: Chi-square distribution is applied to construct confidence intervals for variance in a normally distributed population.


The chi-square distribution is a fundamental distribution in statistics that describes the distribution of the sum of the squares of k independent standard normal variables. Understanding the chi-square distribution and how to work with it in R is an essential skill for anyone involved in statistical analysis or data science. R provides powerful capabilities to work with chi-square distributions, making it a comprehensive tool for statistical modeling and hypothesis testing.

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