Fit regression through the origin with the residuals In a Coursera lecture on multi-variable linear regression with three variables, the professor shows that the sum of the residuals for y* residuals for x) / sum residuals x^2  equals the coefficient for y regressed on x. 
Can someone explains why this is so? The relevant code used is below:
$   > n = 100; x = rnorm(n); x2 = rnorm(n); x3 = rnorm(n)
2 ## Generate the data
3 > y = 1 + x + x2 + x3 + rnorm(n, sd = .1)
4 ## Get the residuals having removed X2 and X3 from X1 and Y
5 > ey = resid(lm(y ~ x2 + x3))
6 > ex = resid(lm(x ~ x2 + x3))
7 ## Fit regression through the origin with the residuals
8 > sum(ey * ex) / sum(ex ^ 2)
9 [1] 1.009
10 ## Double check with lm
11 > coef(lm(ey ~ ex - 1))
12 ex
13 1.009
14 ## Fit the full linear model to show that it agrees
15 coef(lm(y ~ x + x2 + x3))
16 (Intercept) x x2 x3
17 1.0202 1.0090 0.9787 1.0064 $

 A: ey = resid(lm(y ~ x2 + x3)) calculates the variance of y not explained by the regressors $x_2$ and $x_3$. On the other hand, ex = resid(lm(x ~ x2 + x3)) gives you the variance of $x$ not explained by $x_2$ and $x_3$.
Therefore, regressing ey over ex will calculate the 
$\text{variance of }y 
\text{ not explained by }x_2\text{ and} x_3$ 
explained by the 
$\text{variance of }x_1 \text{ not explained by }x_2\text{ and} x_3$
So you have eliminated the contribution of $x_2$ and $x_3$, and you are really just calculating the variance of $y$ explained by $x_1$, after having regressed both variables (dependent an independent) over $x_2$ and $x_2$ to effectively eliminate the effect of these two additional explanatory variables. 
Now you just have to keep in mind that the regression through the origin with one single regressor is $\hat \beta=\frac{\displaystyle\sum_{i=1}^{n}Y_iX_i}{\displaystyle\sum_{i=1}^{n}X_i^2}$, and that the $-1$ in coef(lm(ey ~ ex - 1)) is the call for the OLS without intercept.
The reason why the intercept is eliminated is because the intercept is a regressor in its own right. Even if we didn't specify it in the call for ey and ex the regression over $1$ was there in the model matrix.
The package {swirl} in R contains step-by-step practice in this technique of picking one regressor, replacing all other variables by the residuals of their regressions against that one.
