typos in reg slides

doc/src/Regression/Regression.do.txt

@@ -1465,25 +1465,9 @@ As an example, the above defective matrix can be decomposed as
 with eigenvalues $\sigma_1=2$ and $\sigma_2=0$. 
 The SVD exits always! 
 
-
-!split
-===== Another Example =====
-
-Consider the following matrix which can be SVD decomposed as
-
-!bt
-\[
-\bm{X} = \frac{1}{15}\begin{bmatrix} 14 & 2\\ 4 & 22\\ 16 & 13\end{bmatrix}=\frac{1}{3}\begin{bmatrix}  1&  2 & 2 \\ 2& -1 & 1\\ 2 & 1& -2\end{bmatrix} \begin{bmatrix}  2&  0 \\ 0& 1\\ 0 & 0\end{bmatrix}\frac{1}{5}\begin{bmatrix}  3&  4 \\ 4& -3\end{bmatrix}=\bm{U}\bm{\Sigma}\bm{V}^T. 
-\]
-!et
-
-This is a $3\times 2$ matrix which is decomposed in terms of a
-$3\times 3$ matrix $\bm{U}$, and a $2\times 2$ matrix $\bm{V}$. It is easy to see
-that $\bm{U}$ and $\bm{V}$ are orthogonal (how?). 
-
-And the SVD
+The SVD
 decomposition (singular values) gives eigenvalues 
-$\sigma_i\geq\sigma_{i+1}$ for all $i$ and for dimensions larger than $i=2$, the
+$\sigma_i\geq\sigma_{i+1}$ for all $i$ and for dimensions larger than $i=p$, the
 eigenvalues (singular values) are zero.
 
 In the general case, where our design matrix $\bm{X}$ has dimension