minor typo

doc/src/week35/week35.do.txt

@@ -1721,15 +1721,19 @@ 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 matrices $\bm{U}$ and $\bm{V}$,
 
 !bt
 \[
-\tilde{y}_{\mathrm{OLS}}=\bm{U}\bm{U}^T\bm{y}=\sum_{i=0}^{n-1}\bm{u}_i\bm{u}^T_i\bm{y},
+\tilde{y}_{\mathrm{OLS}}=\bm{U}\bm{U}^T\bm{y}=\sum_{i=0}^{p-1}\bm{u}_i\bm{u}^T_i\bm{y},
 \]
 !et
 
-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 $\bm{U}$.
+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 $\bm{\tilde{y}}\ne \bm{y}$.
 
 !split
 ===== Further properties (important for our analyses later) =====