Extend Regression module to address first point in issue #67

[raibread]

I extended olsRegress to the following settings with normally distributed errors

(1) homoskedastic errors, known variance (2) homoskedastic errors, unknown variance (3) heteroskedastic errors, known variance (4) heteroskedastic errors, unknown variance

In case (4) will still assume that we known the diagonal matrix $W$ in the error covariance $var(\epsilon) = \sigma^2W$ but not $\sigma^2$. Case (4) addresses the first point specifically. The other cases are equally (or more) important from a statisticians point of view. In each case, standard errors for regression coefficients and overall model fit test statistics are computed along with their corresponding reference distributions.

I also provide example usage and implementation of Cholesky decomposition to use when extending the Regression module to eventually deal with regression error with arbitrary linear correlation structure (known as generalized least squares). This algorithm will also be useful if, in the future, one wanted to implement a version of R's glm.

[Shimuuar]

Thank you I'' review it over the weekend

[ocramz]

@raibread @Shimuuar are you still interested in merging this?

[Shimuuar]

Sorry I forgot about this PR. I really should add to readme that if I don't reapond for a long time one should ping me