One often hears to say "more than 70% variability is explained by ..." What exactly is meant by this? Th proportion of the sum of squares (SSE), or mean sum of squares (MSE)? For example in the following anova table:
Df Sum Sq Mean Sq F value Pr(>F) as.factor(site) 444 8357 18.82 163.1 <2e-16 *** as.factor(year) 12 569 47.43 410.9 <2e-16 *** as.factor(month) 5 863 172.53 1494.8 <2e-16 *** as.factor(year):as.factor(month) 60 769 12.82 111.1 <2e-16 *** Residuals 34188 3946 0.12 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 7176 observations deleted due to missingness
could we say that most of the variability was explained by
site? We see that site covers most of the SSE but as there is a lot of sites, the MSE for site is almost the lowest in the table.
And how would I interpret this in practice? I want to know where is the variability, whether it varies mostly accross time or space. Is the
site actually the biggest source of variability, or is it a
year? Shall I read SSE or MSE column for this purpose?
PS: please note I am not a professional statistician, so if you are about to respond with a lot of math then please make also some simple summary for dummies :-)