The chi-square test is pronounced "keye-skwair" test.
Critical values of a chi-square test depend on the degrees of freedom.
it has reproductive property
Chi-Square Goodness-of-fit Test is used when you want to test if the sample observed follows an assumed theoretical distribution.
A reduced chi-square value, calculated after a nonlinear regression has been performed, is the is the Chi-Square value divided by the degrees of freedom (DOF). The degrees of freedom in this case is N-P, where N is the number of data points and P is the number of parameters in the fitting function that has been used. I have added a link, which explains better the advantages of calculating the reduced chi-square in assessing the goodness of fit of a non-linear regression equation. In fitting an equation to the data, it is possible to also "over fit", which is to account for small and random errors in the data, with additional parameters. The reduced chi-square value will increase (show a worse fit) if the addition of a parameter does not significantly improve the fit. You can also do a search on reduced chi-square value to better understand its importance.
The characteristics of the chi-square distribution are: A. The value of chi-square is never negative. B. The chi-square distribution is positively skewed. C. There is a family of chi-square distributions.
it can never have 4567897656
Never! In Tai Chi, for power, the hips should *never* be squared!
A Chi-square table is used in a Chi-square test in statistics. A Chi-square test is used to compare observed data with the expected hypothetical data.
i don't know. i think very complicated.
chi-square http://en.wikipedia.org/wiki/Chi-square_test
The chi-square test is pronounced "keye-skwair" test.
A chi-square statistic which is near zero suggests that the observations are exceptionally consistent with the hypothesis.
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the Chi Square distribution is a mathematical distribution that is used directly or indirectly in many tests of significance. The most common use of the chi square distribution is to test differences among proportions
The underlying principle is that the square of an independent Normal variable has a chi-square distribution with one degree of freedom (df). A second principle is that the sum of k independent chi-squares variables is a chi-squared variable with k df.
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