There are several possible situations where chi – square distribution table can be used like checking truthfulness of observed value against theoretical one, checking for in- dependency of two standards of classification of qualitative subject, and in estimation of confidence intervals also we can use this distribution method for a value of standard deviation of a normal probability distribution from a given standard deviation.
Definition of chi – square table can be given as follows:
Suppose we have X1, X2, X3, X4 …………… Xn as our standard normal random variables with independent nature. Their summation of squares can be evaluated as follows:
Sum = sum (i = 1k X_ (i2) is distributed according to chi - square distribution table having “n” degrees of freedom. This is typically represented as:
Q sum chi2 (k) text or Q sum chi2 _k.
In general, if chi - square value is greater, standard deviations are more probable to be noteworthy, and data is less probable to satisfy our expectations.