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The distribution was independently rediscovered by the English mathematician Karl Pearson in the context of goodness of fit, for which he developed his Pearson's chi-squared test, published in 1900, with computed table of values published in (Elderton 1902), collected in (Pearson 1914, pp. xxxi–xxxiii, 26–28, Table XII).
In probability theory and statistics, the chi distribution is a continuous probability distribution over the non-negative real line. It is the distribution of the positive square root of a sum of squared independent Gaussian random variables .
revert: user:Niceoboe had replaced the pdf with the cdf: 12:52, 26 April 2013: 600 × 400 (23 KB) Niceoboe: Changed path ids (more than one named curve-1l) to pass conformity test at validator.w3.org. 17:21, 31 March 2010: 600 × 400 (23 KB) Geek3: chi square distribution Category:Chi-square distribution
For the test of independence, also known as the test of homogeneity, a chi-squared probability of less than or equal to 0.05 (or the chi-squared statistic being at or larger than the 0.05 critical point) is commonly interpreted by applied workers as justification for rejecting the null hypothesis that the row variable is independent of the ...
Where and are the cdf and pdf of the corresponding random variables. Then Y = X 2 ∼ χ 1 2 . {\displaystyle Y=X^{2}\sim \chi _{1}^{2}.} Alternative proof directly using the change of variable formula
A chi-squared test (also chi-square or χ 2 test) is a statistical hypothesis test used in the analysis of contingency tables when the sample sizes are large. In simpler terms, this test is primarily used to examine whether two categorical variables ( two dimensions of the contingency table ) are independent in influencing the test statistic ...
where and are the same as for the chi-square test, denotes the natural logarithm, and the sum is taken over all non-empty bins. Furthermore, the total observed count should be equal to the total expected count: ∑ i O i = ∑ i E i = N {\displaystyle \sum _{i}O_{i}=\sum _{i}E_{i}=N} where N {\textstyle N} is the total number of observations.
This reduces the chi-squared value obtained and thus increases its p-value. The effect of Yates's correction is to prevent overestimation of statistical significance for small data. This formula is chiefly used when at least one cell of the table has an expected count smaller than 5. = =