This might be trivial and vague question, but I still don't understand why when creating test statistics or estimators we always divide by the degree of freedom. Just to give examples of what I'm talking about:
The F-test for linear regression for example: $F=(TSS-RSS/p)/(RSS/n-p-1)$ or with the residual standard error $RSE=sqrt(RSS/n-p-1)$ RSS is the sum of residuals squared.
Is it a way to make this statistics approachable by known statistics such as t and F statistics for example?
Here is an argument for why we divide by degrees of freedom, in a simple case. Let $X_1, X_2, \cdots X_n$ be independent and identically distributed with mean $\mu$ and variance $\sigma^2$. Consider the sample variance as an estimator for $\sigma^2$. $$S^2 = \frac{1}{n-1}\sum_{i=1}^n(X_i - \bar{X})^2 = \frac{1}{n-1}\left(\sum_{i=1}^nX_i^2 - n\bar{X}^2\right)$$ We can show that $S^2$ is unbiased for $\sigma^2$. \begin{align*} E(S^2) &= \frac{1}{n-1}\left(E\left(\sum X_i^2\right) - nE(\bar X^2)\right) \\ &= \frac{1}{n-1}(nE(X_i^2) - nE(\bar X^2)) \\ &= \frac{1}{n-1}(n(\mu^2 + \sigma^2) - n(\mu^2 + \sigma^2/n)) \\ &= \frac{1}{n-1}(n\sigma^2 - \sigma^2) = \sigma^2 \end{align*} Note that if we had divided by anything other than $n-1$, this estimator would be biased. In fact, it can be shown that $S^2$ has uniformly minimum variance of all unbiased estimators of $\sigma^2$ in many cases.
On the other hand, assume that $\mu$ is known (not very useful in practice). Now the estimator $$\hat\sigma^2 = \frac{1}{n}\sum_{i=1}^n(X_i-\mu)^2$$ is unbiased. This is justified, since we are not losing a degree of freedom to estimate $\bar{X}$.
This is just one simple case where dividing by degrees of freedom makes sense, but the justification works for other statistics as well.
