<Dl> <Dd> F = M S Treatments M S Error = S S Treatments / (I − 1) S S Error / (n T − I) (\ displaystyle F = (\ frac (MS_ (\ text (Treatments))) (MS_ (\ text (Error)))) = ((SS_ (\ text (Treatments)) / (I - 1)) \ over (SS_ (\ text (Error)) / (n_ (T) - I)))) </Dd> </Dl> <Dd> F = M S Treatments M S Error = S S Treatments / (I − 1) S S Error / (n T − I) (\ displaystyle F = (\ frac (MS_ (\ text (Treatments))) (MS_ (\ text (Error)))) = ((SS_ (\ text (Treatments)) / (I - 1)) \ over (SS_ (\ text (Error)) / (n_ (T) - I)))) </Dd> <P> where MS is mean square, I (\ displaystyle I) = number of treatments and n T (\ displaystyle n_ (T)) = total number of cases </P> <P> to the F - distribution with I − 1 (\ displaystyle I - 1), n T − I (\ displaystyle n_ (T) - I) degrees of freedom . Using the F - distribution is a natural candidate because the test statistic is the ratio of two scaled sums of squares each of which follows a scaled chi - squared distribution . </P>

An anova conducted on a design in which there is only one factor is called a one-way anova