![]() ![]() Likelihood Ratio Chi-Square 2 24.2964 <. However, this is not used in your calculation of the df.Įdit: If you were doing an 'Goodness of Fit' then yes, it would be n-1 but you have a contigency table (r x c) where r or c not equal to 1 so you have to use the (r-1)(c-1)Įdit #2 for dimbo (I can't comment): Expected values should be calculated by (row total)(column total) / (total # of observations) : Thus the expected for r1,c1 position is (270)(159) / (539) which gives the values chi gave you.Įdit #: SAS code confirming Chi data question where Y 0 is a constant that depends on the number of degrees of freedom, 2 is the chi-square statistic, v n - 1 is the number. The chi-square distribution is defined by the following probability density function : Y Y 0 ( 2 ) ( v/2 - 1 ) e-2 / 2. For that of independence (in a crosstab table). Therefore, the probability that a chi-square random variable with 10 degrees of freedom is greater than 15.99 is 10.90, or 0.10. The distribution of the chi-square statistic is called the chi-square distribution. The actual product of r x c should = n (total # of observations) which is six. For the chi-squared test of goodness of fit, the degrees of freedom is one less than the number of categories. ![]()
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