What is the relationship between the mean squared error and the residual sum of squares function? Looking at the Wikipedia definitions of:


*

*Mean Squared Error (MSE)

*Residual Sum of Squares (RSS)


It looks to me that 
$$\text{MSE} = \frac{1}{N} \text{RSS} = \frac{1}{N} \sum (f_i -y_i)^2$$
where $N$ is he number of samples and $f_i$ is our estimation of $y_i$.
However, none of the Wikipedia articles mention this relationship. Why? Am I missing something?
 A: But be aware that Sum of Squared Errors (SSE) and Residue Sum of Squares (RSS) sometimes are used interchangeably, thus confusing the readers. For instance, check this URL out.
Strictly speaking from statistic point of views, Errors and Residues are completely different concepts. Errors mainly refer to difference between actual observed sample values and your predicted values, and used mostly in the statistic metrics like Root Means Squared Errors (RMSE) and Mean Absolute Errors (MAE). In contrast, residues refer exclusively to the differences between dependent variables and estimations from linear regression.
A: Actually it's mentioned in the Regression section of Mean squared error in Wikipedia:

In regression analysis, the term mean squared error is sometimes used
  to refer to the unbiased estimate of error variance: the residual sum
  of squares divided by the number of degrees of freedom.

You can also find some informations here: Errors and residuals in statistics
It says the expression mean squared error may have different meanings in different cases, which is tricky sometimes.
A: I don´t think this is correct here if we consider MSE to be the sqaure of RMSE. For instance, you have a series of sampled data on predictions and observations, now you try to do a linear regresion: Observation (O)= a + b X Prediction (P). In this case, the MSE is the sum of squared difference between O and P and divided by sample size N. 
But if you want to measure how linear regression performs, you need to calculate Mean Squared Residue (MSR). In the same case, it would be firstly calculating Residual Sum of Squares (RSS) that corresponds to sum of squared differences between actual observation values and predicted observations derived from the linear regression.Then, it is followed for RSS divided by N-2 to get MSR. 
Simply put, in the example, MSE can not be estimated using RSS/N since RSS component is no longer the same for the component used to calculate MSE.  
