added html files

doc/src/Regression/Regression.do.txt

@@ -393,9 +393,10 @@ and
 !bblock
 
 For a linear fit we don't need to invert a matrix!!  
+Defining
 !bt
 \[
-\gamma =  \sum_{i=0}^{n-1}\frac{n-1}{\sigma_i^2},
+\gamma =  \sum_{i=0}^{n-1}\frac{1}{\sigma_i^2},
 \]
 !et
 
@@ -419,7 +420,7 @@ For a linear fit we don't need to invert a matrix!!
 \gamma_{xy} = \sum_{i=0}^{n-1}\frac{y_ix_{i}}{\sigma_i^2},
 \]
 !et
-and show that
+we obtain
 !bt
 \[
 \beta_0 = \frac{\gamma_{xx}\gamma_y-\gamma_x\gamma_y}{\gamma\gamma_{xx}-\gamma_x^2},
@@ -431,7 +432,7 @@ and show that
 \]
 !et
 
-The LSM suffers often from both being underdetermined and overdetermined in the unknown coefficients $\beta_i$.  A better approach is to use the Singular Value Decomposition (SVD) method discussed below. Or using Lasso and Ridge regression. See below.
+This approach (different linear and non-linear regression) suffers often from both being underdetermined and overdetermined in the unknown coefficients $\beta_i$.  A better approach is to use the Singular Value Decomposition (SVD) method discussed below. Or using Lasso and Ridge regression. See below.
 !eblock