Why the sum of residuals equals 0 when we do a sample regression by OLS? That's my question, I have looking round online and people post a formula by they don't explain the formula. Could anyone please give me a hand with that ? cheers
If the OLS regression contains a constant term, i.e. if in the regressor matrix there is a regressor of a series of ones, then the sum of residuals is exactly equal to zero, as a matter of algebra.

For the simple regression,
specify the regression model
$$y_i = a +bx_i + u_i\,,\; i=1,...,n$$

Then the OLS estimator $(\hat a, \hat b)$ minimizes the sum of squared residuals, i.e.

$$(\hat a, \hat b) : \sum_{i=1}^n(y_i - \hat a - \hat bx_i)^2 = \min$$

For the OLS estimator to be the argmin of the objective function, it must be the case as a necessary condition, that the first partial derivatives with respect to $a$ and $b$, evaluated at $(\hat a, \hat b)$ equal zero.  For our result, we need only consider the partial w.r.t. $a$:

$$\frac {\partial}{\partial a} \sum_{i=1}^n(y_i -  a - bx_i)^2 \Big |_{(\hat a, \hat b)} = 0 \Rightarrow -2\sum_{i=1}^n(y_i -  \hat a - \hat bx_i) = 0 $$

But $y_i -  \hat a - \hat bx_i = \hat u_i$, i.e. is equal to the residual, so we have that 

$$\sum_{i=1}^n(y_i -  \hat a - \hat bx_i) = \sum_{i=1}^n\hat u_i = 0 $$

The above also implies that if the regression specification does not include a constant term, then the sum of residuals will not, in general, be zero.

For the multiple regression,
let $\mathbf X$ be the $n \times k$ matrix containing the regressors, $\hat {\mathbf u}$ the residual vector and $\mathbf y$ the dependent variable vector. Let $\mathbf M  = I_n-\mathbf X(\mathbf X'\mathbf X)^{-1}\mathbf X'$ be the "residual-maker" matrix, called thus because we have

$$\hat {\mathbf u} = \mathbf M\mathbf y$$

It is easily verified that $\mathbf M \mathbf X = \mathbf 0$. Also $\mathbf M$ is idempotent and symmetric.

Now, let $\mathbf i$ be a column vector of ones. Then the sum of residuals is

$$\sum_{i=1}^n \hat u_i = \mathbf i'\hat {\mathbf u} =\mathbf i'\mathbf M\mathbf y = \mathbf i'\mathbf M'\mathbf y = (\mathbf M\mathbf i)'\mathbf y = \mathbf 0' \mathbf y = \mathbf 0$$

So we need the regressor matrix to contain a series of ones, so that we get $\mathbf M\mathbf i = \mathbf 0$.
take the estimated values from the line of best fit and use these y values to subtract from the original y values then add them up. if it is a good line of best fit then it should approach zero, but bad lines of best fit will be much less or more than zero