Chi Square Distribution. In probability theory and statistics the chi squared distribution also referred as chi square or x2 distribution with k degrees of freedom is the distribution of a sum of squares of k independent standard regular normal variables. Chi distribution is a unique case of a gamma distribution and is among the most broadly applied probability distribution in inferential statistics.
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Two common examples are the chi square test for independence in an rxc contingency table and the chi square test to determine if the standard deviation of a population is equal to a pre specified value. The χ2 can never assume negative values. In probability theory and statistics the chi square distribution with k degrees of freedom is the distribution of a sum of the squares of k independent standard normal random variables.
The χ2 can never assume negative values.
This distribution is sometimes called the central chi square distribution a s. This distribution is sometimes called the central chi square distribution a s. More generally if chi i 2 are independently distributed according to a chi 2 distribution with r 1 r 2 r k degrees of freedom then sum j 1 kchi j 2 2 is distributed according to chi 2 with. Two common examples are the chi square test for independence in an rxc contingency table and the chi square test to determine if the standard deviation of a population is equal to a pre specified value.
