To calculate mean squares, the corresponding sum of squares is divided by which

Question

To calculate mean squares, the corresponding sum of squares is divided by which corresponding value? In an ANOVA table, the calculated test statistic comes from an F distribution with two parameters: numerator degrees of freedom (ndf), and denominator degrees of freedom (ddf). If SSR has k df and SST has n - 1 df, what are the ndf and ddf, in terms of k and n? Consider using ANOVA to compare a "Full" model to a "Reduced" model. In performing the hypothesis test, which model does the null hypothesis support: the "Full" or "Reduced" one? Briefly explain why. h) If a test from part (g) produces in a small p-value, which model does the test suggest is preferred: the "Full" or "Reduced" one?

Explanation / Answer

(e) To calculate meansquares ,the corresponding sum of squares is divided by which corresponding value?

Ans:Mean squares=sum of squares /degrees of fredom

     The answer is degrees of fredom

(f) If SSR has K df and SSt has n-1 df what are the numerator degress of fredom and denomimator degress of fredom in terms of k and n

Ans:Given SSR=k df

                 SST=n-1 df

            Given SST=SSR+SSE

                      n-1=k+SSE

                     SSE=n-k-1

F=MSR/MSE

   =SSR/k/SSE/n-k-1

F stat follows an F distrubution with k numerator degrees of fredom and n-k-1 denominator degrees of fredom

                

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