linear regression:
OLS estimate of the model
Y = X*betas + epsilon,
with epsilon homoskedastic Gaussian white noise
If the first column of X is composed by ones, the first beta is the intercept.
Besides the output variables, a table with a summary of the output is printed on
the console.
As an example, given the data in X and Y, the function calla and output look
like this:
-->
[betas,std_err,t_stats,p_values,sigma,resid,R2,R2_adj,F_stat,F_prob]=OLS(Y,X);
------------------------------------------------------
parameter std. err. t stat. p-value
0.5708444 0.174263 3.2757638 0.0011274
0.7953754 0.2387666 3.331184 0.0009291
-0.5209135 0.233858 -2.2274779 0.0263623
------------------------------------------------------
noise std. err. 1.492155
R squaded (adjusted) 0.030705 (0.026805)
F statistic (p-value) 7.872004 (0.000431)
