updating the regression analysis

doc/pub/Regression/html/Regression.html

@@ -260,10 +260,12 @@ $$
 \hat{y} = [y_0,y_1, y_2,\dots, y_{n-1}]^T,
 $$
 
+and
 $$
 \hat{\beta} = [\beta_0,\beta_1, \beta_2,\dots, \beta_{n-1}]^T,
 $$
 
+and
 $$
 \hat{\epsilon} = [\epsilon_0,\epsilon_1, \epsilon_2,\dots, \epsilon_{n-1}]^T,
 $$
@@ -618,9 +620,9 @@ $$
 <p>
 
 <p>
-We define then
+For a linear fit we don't need to invert a matrix!!  
 $$
-\gamma =  \sum_{i=0}^{1}\frac{n-1}{\sigma_i^2},
+\gamma =  \sum_{i=0}^{n-1}\frac{n-1}{\sigma_i^2},
 $$
 
 
@@ -650,7 +652,7 @@ $$
 $$
 
 <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.
+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.
 </div>