corrected some small errors

doc/src/week35/week35.do.txt

@@ -2050,7 +2050,7 @@ and using our SVD decomposition of $\bm{X}$ we have
 \tilde{y}_{\mathrm{OLS}}=\bm{U}\bm{\Sigma}\bm{V}^T\left(\bm{V}\tilde{\bm{\Sigma}}^{2}(\bm{V}^T\right)^{-1}\bm{V}\bm{\Sigma}^T\bm{U}^T\bm{y},
 \]
 !et
-which gives us, using the orthogonality of the matrices $\bm{U}$ and $\bm{V}$,
+which gives us, using the orthogonality of the matrix $\bm{V}$,
 
 !bt
 \[
@@ -2061,8 +2061,10 @@ which gives us, using the orthogonality of the matrices $\bm{U}$ and $\bm{V}$,
 It means that the ordinary least square model (with the optimal
 parameters) $\bm{\tilde{y}}$, corresponds to an orthogonal
 transformation of the output (or target) vector $\bm{y}$ by the
-vectors of the matrix $\bm{U}$. Note that the summation ends at $p-1$,
-that is $\bm{\tilde{y}}\ne \bm{y}$.
+vectors of the matrix $\bm{U}$. _Note that the summation ends at_
+$p-1$, that is $\bm{\tilde{y}}\ne \bm{y}$. We can thus not use the
+orthogonality relation for the matrix $\bm{U}$. This can already be
+when we multiply the matrices $\bm{\Sigma}^T\bm{U}^T$.
 
 !split
 ===== Further properties (important for our analyses later) =====