typo

doc/src/week36/week36.do.txt

@@ -419,7 +419,7 @@ which leads to
 \[
 \hat{\bm{\beta}}_i^{\mathrm{Lasso}} = \left\{\begin{array}{ccc}y_i-\frac{\lambda}{2} &\mathrm{if} & y_i> \frac{\lambda}{2}\\
                                                           y_i+\frac{\lambda}{2} &\mathrm{if} & y_i< -\frac{\lambda}{2}\\
-							  0 &\mathrm{if} & \verty_i\vert\le  \frac{\lambda}{2}\end{array}\right.\\.
+							  0 &\mathrm{if} & \vert y_i\vert\le  \frac{\lambda}{2}\end{array}\right.\\.
 \]
 !et
 
@@ -429,6 +429,7 @@ We will now couple the discussions of ordinary least squares, Ridge and Lasso re
 We will amongst other things show that the regularization parameter can reduce considerably the variance of the parameters $\beta$.
 
 
+
 !split 
 ===== Linking the regression analysis with a statistical interpretation =====