It is the sum of squares of “n” independent standard normal variates, following the Chi-Square distribution with “n” degree of freedom. dev” respectively then the Chi-Square variate will be:Ĭhi Square = (X1-mean)2/Standard Deviation + = (X2-mean)2/Standard Deviation + = (Xn-mean)2/Standard Deviation. If X1, X2…….Xn are “n” are independent random variables following the normal distribution with mean “μ” and Std. The square of a standard normal variate (A variable quantity that is random) is called a Chi-Square variate with 1 degree of freedom (V=1), that is if “x” variable is normally distributed with a mean “μ´and standard deviation “б” then (X- μ)/ б is a Chi-Square variate with “V” equal to 1. The Chi-square curve will be on the positive side of the X-Axis because the Chi-Square values are always positive. Step 3 – Divide the values obtained in “Step 2” by the respective expected frequency “E” and add all the values to get the value according to the formula by Step 2 – Take the difference between observed and expected frequencies and obtain the squares of these differences (O-E)2. Step 1 – Calculate the expected frequencies. The expected frequencies are the calculated frequencies or The observed frequencies are the frequencies obtained from the observation, which are sample frequencies. Chi Square is one of the most important statistical tests used while doing hypothesis in your project when the data is in discrete.
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