added html files

doc/pub/Regression/html/Regression.html

@@ -621,8 +621,9 @@ $$
 
 <p>
 For a linear fit we don't need to invert a matrix!!  
+Defining
 $$
-\gamma =  \sum_{i=0}^{n-1}\frac{n-1}{\sigma_i^2},
+\gamma =  \sum_{i=0}^{n-1}\frac{1}{\sigma_i^2},
 $$
 
 
@@ -642,7 +643,7 @@ $$
 \gamma_{xy} = \sum_{i=0}^{n-1}\frac{y_ix_{i}}{\sigma_i^2},
 $$
 
-and show that
+we obtain
 $$
 \beta_0 = \frac{\gamma_{xx}\gamma_y-\gamma_x\gamma_y}{\gamma\gamma_{xx}-\gamma_x^2},
 $$
@@ -652,7 +653,7 @@ $$
 $$
 
 <p>
-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.
 </div>