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OLS linear regression

Ordinary least square linear regression estimation
(530 downloads for this version - 530 downloads for all versions)
Mario Maggi
Mario Maggi
Supported Scilab Version
Creation Date
August 30, 2019
            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:



    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)            
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