Now one thing I forgot to mention, any hypothesis test, we need to have some type of significance level. And so let's say the significance level that we care about for our hypothesis test is 10%, is 0.10, which means that if we assume, if assuming the null hypothesis, there is less than a 10% chance of getting the result that we got, of getting this F statistic, then we will reject the null hypothesis. So what we want to do is figure out a critical F statistic value that getting that extreme of a value or greater is 10%, and if this is bigger than our critical F statistic value, then we're going to reject the null hypothesis. If it's less, we can't reject the null hypothesis. And so I'm not going to go a lot into the guts of the F statistic, but you can already appreciate that each of these sum of squares has a chi-square distribution. This has a chi-square distribution, and this has a different chi-square distribution. This is a chi-square distribution with 2 degrees of freedom.