Continuous random variable

Chi-squared distribution

A chi-squared distributed random variable 𝑋 ∼𝜒2𝑛 is the sum of squares of 𝑛 independent and identically distributed {𝑍𝑖}𝑛𝑖=1 with standard normal distributions. #m/def/prob

𝑋=𝑛∑𝑖=1(𝑍𝑖)2𝑍𝑖iid∼N⁡(0,1)

This turns out to be a special case of the Gamma distribution, namely 𝑋 ∼Gamma(𝑛2,12).

Proof

Let 𝑋 =𝑍2 for 𝑍 ∼N⁡(0,1), i.e. 𝑋 ∼𝜒21. Then

𝐹𝑋(𝑥)=ℙ(𝑍2≤𝑥)=ℙ(−√𝑥<𝑍<√𝑥)=Φ(√𝑥)−Φ(−√𝑥)=2Φ(√𝑥)−1

thus

𝑓𝑋(𝑥)=𝜑(√𝑥)𝑥−1/2=(1/2)1/2Γ(1/2)𝑥1/2−1e−𝑥/2

so 𝑋 ∼Gamma(12,12). Thus by ^Q1, the claim is proven.

Properties

Additional properties

  1. Let {𝑋𝑗}𝑛𝑗=1 be a random sample of variable independently distributed according to the normal distribution N⁡(𝜇,𝜎2). Then the sample variance is distributed such that
(𝑛−1)𝑆2𝑛𝜎2∼𝜒2𝑛−1


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