updating the regression analysis

doc/src/Regression/Regression.do.txt

@@ -59,11 +59,13 @@ Defining the vectors
 \hat{y} = [y_0,y_1, y_2,\dots, y_{n-1}]^T,
 \]
 !et
+and
 !bt
 \[
 \hat{\beta} = [\beta_0,\beta_1, \beta_2,\dots, \beta_{n-1}]^T,
 \]
 !et
+and
 !bt
 \[
 \hat{\epsilon} = [\epsilon_0,\epsilon_1, \epsilon_2,\dots, \epsilon_{n-1}]^T,
@@ -390,7 +392,7 @@ and
 ===== The $\chi^2$ function  =====
 !bblock
 
-We define then
+For a linear fit we don't need to invert a matrix!!  
 !bt
 \[
 \gamma =  \sum_{i=0}^{n-1}\frac{n-1}{\sigma_i^2},
@@ -429,7 +431,7 @@ and show that
 \]
 !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. 
+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.
 !eblock
 
 

doc/src/Regression/make.sh

@@ -47,7 +47,7 @@ system doconce format html $name --html_style=bootstrap --pygments_html_style=de
 system doconce split_html $html.html --method=split --pagination --nav_button=bottom
 
 # IPython notebook
-#system doconce format ipynb $name $opt
+system doconce format ipynb $name $opt
 
 # LaTeX Beamer slides
 beamertheme=red_plain