deleted a line

doc/pub/week35/html/week35-reveal.html

@@ -2990,9 +2990,7 @@ eigenvalues ordered in a descending way, that is \( \sigma_i \geq
 \sigma_{i+1} \).
 
 <p>
-For small eigenvalues \( \sigma_i \) it means that their contributions become less important, a fact which can be used to reduce the number of degrees of freedom.
-Actually, calculating the variance of \( \boldsymbol{X}\boldsymbol{v}_j \) shows that this quantity is equal to \( \sigma_j^2/n \).
-With a parameter \( \lambda \) we can thus shrink the role of specific parameters.
+For small eigenvalues \( \sigma_i \) it means that their contributions become less important, a fact which can be used to reduce the number of degrees of freedom. More about this when we have covered the material on a statistical interpretation of various linear regression methods.
 </section>
 
 

doc/pub/week35/html/week35-solarized.html

@@ -2944,9 +2944,7 @@ eigenvalues ordered in a descending way, that is \( \sigma_i \geq
 \sigma_{i+1} \).
 
 <p>
-For small eigenvalues \( \sigma_i \) it means that their contributions become less important, a fact which can be used to reduce the number of degrees of freedom.
-Actually, calculating the variance of \( \boldsymbol{X}\boldsymbol{v}_j \) shows that this quantity is equal to \( \sigma_j^2/n \).
-With a parameter \( \lambda \) we can thus shrink the role of specific parameters.
+For small eigenvalues \( \sigma_i \) it means that their contributions become less important, a fact which can be used to reduce the number of degrees of freedom. More about this when we have covered the material on a statistical interpretation of various linear regression methods.
 
 <p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>

doc/pub/week35/html/week35.html

@@ -2949,9 +2949,7 @@ eigenvalues ordered in a descending way, that is \( \sigma_i \geq
 \sigma_{i+1} \).
 
 <p>
-For small eigenvalues \( \sigma_i \) it means that their contributions become less important, a fact which can be used to reduce the number of degrees of freedom.
-Actually, calculating the variance of \( \boldsymbol{X}\boldsymbol{v}_j \) shows that this quantity is equal to \( \sigma_j^2/n \).
-With a parameter \( \lambda \) we can thus shrink the role of specific parameters.
+For small eigenvalues \( \sigma_i \) it means that their contributions become less important, a fact which can be used to reduce the number of degrees of freedom. More about this when we have covered the material on a statistical interpretation of various linear regression methods.
 
 <p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>

doc/pub/week35/ipynb/week35.ipynb

@@ -3928,9 +3928,7 @@
     "eigenvalues ordered in a descending way, that is $\\sigma_i \\geq\n",
     "\\sigma_{i+1}$.\n",
     "\n",
-    "For small eigenvalues $\\sigma_i$ it means that their contributions become less important, a fact which can be used to reduce the number of degrees of freedom.\n",
-    "Actually, calculating the variance of $\\boldsymbol{X}\\boldsymbol{v}_j$ shows that this quantity is equal to $\\sigma_j^2/n$.\n",
-    "With a parameter $\\lambda$ we can thus shrink the role of specific parameters.  \n",
+    "For small eigenvalues $\\sigma_i$ it means that their contributions become less important, a fact which can be used to reduce the number of degrees of freedom. More about this when we have covered the material on a statistical interpretation of various linear regression methods.\n",
     "\n",
     "\n",
     "## More interpretations\n",

doc/src/week35/week35.do.txt

@@ -2436,9 +2436,7 @@ $\frac{\sigma_j^2}{\sigma_j^2+\lambda}$. Recall that the SVD has
 eigenvalues ordered in a descending way, that is $\sigma_i \geq
 \sigma_{i+1}$.
 
-For small eigenvalues $\sigma_i$ it means that their contributions become less important, a fact which can be used to reduce the number of degrees of freedom.
-Actually, calculating the variance of $\bm{X}\bm{v}_j$ shows that this quantity is equal to $\sigma_j^2/n$.
-With a parameter $\lambda$ we can thus shrink the role of specific parameters.  
+For small eigenvalues $\sigma_i$ it means that their contributions become less important, a fact which can be used to reduce the number of degrees of freedom. More about this when we have covered the material on a statistical interpretation of various linear regression methods.
 
 
 !split