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Relationships between distributions

t2 = approx. (for large df) z2 = chi-square on 1 degree of freedom

From here

chi-squared = (numerator degrees of freedom) * F.

For instance, if you tell me that you have an F(2,71) = 2.05, the corresponding chi-squared is 2 * 2.05 = 4.1 and, by the way, the tail probabilities are virtually the same:

        F(2,71) = 2.05      p = .1363
        chi2(2) = 4.1       p = .1287

This can be useful in checking the output from mixed models in R using lmer().

For example anova(modelA, modelA + extra predictor) gives a chi-square value on 1 df for the extra predictor (assuming the predictor is continuous). The t ratio for the regression coefficient in the lmer output when squared should approximately give the chi-square value outputted so that the p-value for regression coefficient of the fixed effect which is not outputted by lmer() is obtainable using anova().