Dividing by degrees of freedom When estimating parameters such as (I don't care about this specific instance particularly) Variance of a random variable X, one usually adopts Bessel's correction, i.e. using the formula $\hat{Var}{(X)} = \frac{1}{n-1}\sum_i^n(x_i -\bar{x})^2$.
The justification given on Wikipedia and on all other sources I've found is either of the nature of:


*

*the $n-1$ factor arises from dividing by the degrees of freedom of the residual terms

*the $n-1$ factor ensures unbiasedness

*the $n-1$ factor arises to correct from underestimating the variance if we weren't to include it


However, why does it make sense to divide by the degrees of freedom?
In general, it seems pretty common to divide parameter estimates not by $n$, the number of sample points used to calculate them, but by $df$. Why does this generally make sense?
EDIT: to clarify my question, what I'm asking is whether in a general setting dividing a an uncorrected estimate by it's degrees of freedom will produce a unbiased estimator or an estimator with desirable properties. It seems like this procedure is common but I have not seen a general proof (and don't know if it exists) of why this would work generally. 
In particular, I think that the reason would be probably in terms of dimensions of subspaces or connecting back to the degrees of freedom of distributions (that seems closely related).
For individual estimates like sample variance or the MLR residual standard error $\frac{RSS}{n- k-1}$ I am aware that proofs of unbiasedness exist, but they are specific to the problem at hand.
 A: Bessel's correction is adopted to correct for bias in using the sample variance as an estimator of the true variance.  The bias in the uncorrected statistic occurs because the sample mean is closer to the middle of the observations than the true mean, and so the squared deviations around the sample mean systematically underestimates the squared deviations around the true mean.
To see this phenomenon algebraically, just derive the expected value of a sample variance without Bessel's correction and see what it looks like.  Letting $S_*^2$ denote the uncorrected sample variance (using $n$ as the denominator) we have:
$$\begin{equation} \begin{aligned}
S_*^2
&= \frac{1}{n} \sum_{i=1}^n (X_i - \bar{X})^2 \\[8pt]
&= \frac{1}{n} \sum_{i=1}^n (X_i^2 - 2 \bar{X} X_i + \bar{X}^2) \\[8pt]
&= \frac{1}{n} \Bigg( \sum_{i=1}^n X_i^2 - 2 \bar{X} \sum_{i=1}^n X_i + n \bar{X}^2 \Bigg) \\[8pt]
&= \frac{1}{n} \Bigg( \sum_{i=1}^n X_i^2 - 2 n \bar{X}^2 + n \bar{X}^2 \Bigg) \\[8pt]
&= \frac{1}{n} \Bigg( \sum_{i=1}^n X_i^2 - n \bar{X}^2 \Bigg) \\[8pt]
&= \frac{1}{n} \sum_{i=1}^n X_i^2 - \bar{X}^2.
\end{aligned} \end{equation}$$
Taking expectations yields:
$$\begin{equation} \begin{aligned}
\mathbb{E}(S_*^2) 
&= \frac{1}{n} \sum_{i=1}^n \mathbb{E}(X_i^2) - \mathbb{E} (\bar{X}^2) \\[8pt]
&= \frac{1}{n} \sum_{i=1}^n (\mu^2 + \sigma^2) - (\mu^2 + \frac{\sigma^2}{n}) \\[8pt]
&= (\mu^2 + \sigma^2) - (\mu^2 + \frac{\sigma^2}{n}) \\[8pt]
&= \sigma^2 - \frac{\sigma^2}{n} \\[8pt]
&= \frac{n-1}{n} \cdot \sigma^2 \\[8pt]
\end{aligned} \end{equation}$$
So you can see that the uncorrected sample variance statistic underestimates the true variance $\sigma^2$.  Bessel's correction replaces the denominator with $n-1$ which yields an unbiased estimator.  In regression analysis this is extended to the more general case where the estimated mean is a linear function of multiple predictors, and in this latter case, the denominator is reduced further, for the lower number of degrees-of-freedom.
