adding update to regression slides

doc/pub/Regression/html/._Regression-bs000.html

@@ -405,7 +405,7 @@ MathJax.Hub.Config({
 <center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
 <br>
 <p>
-<center><h4>Aug 21, 2019</h4></center> <!-- date -->
+<center><h4>Aug 27, 2019</h4></center> <!-- date -->
 <br>
 <p>
 

doc/pub/Regression/html/._Regression-bs028.html

@@ -398,10 +398,15 @@ The examples we have looked at so far are cases where we normally can
 invert the matrix \( \boldsymbol{X}^T\boldsymbol{X} \). Using a polynomial expansion as we
 did both for the masses and the fitting of the equation of state,
 leads to row vectors of the design matrix which are essentially
-orthogonal due to the polynomial character of our model. This may
+orthogonal due to the polynomial character of our model. Obtaining the inverse of the design matrix is then often done via a so-called LU, QR or Cholesky decomposition. 
+More material to come here.
+
+<p>
+This may
 however not the be case in general and a standard matrix inversion
-algorithm based on say LU decomposition may lead to singularities. We will see an example of this below when we try to fit
-the coupling constant of the widely used Ising model. 
+algorithm based on say LU, QR or Cholesky decomposition may lead to singularities. We will see examples of this below.
+
+<p>
 There is however a way to partially circumvent this problem and also gain some insight about the ordinary least squares approach.
 
 <p>
@@ -415,6 +420,9 @@ inversion algorithm. Thereafter we dive into the math of the SVD.
 </div>
 
 
+<p>
+<!-- todo: change model here. -->
+
 <p>
 <p>
 <!-- navigation buttons at the bottom of the page -->

doc/pub/Regression/html/Regression-bs.html

@@ -405,7 +405,7 @@ MathJax.Hub.Config({
 <center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
 <br>
 <p>
-<center><h4>Aug 21, 2019</h4></center> <!-- date -->
+<center><h4>Aug 27, 2019</h4></center> <!-- date -->
 <br>
 <p>
 

doc/pub/Regression/html/Regression-reveal.html

@@ -148,7 +148,7 @@ MathJax.Hub.Config({
 <center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
 <br>
 <p>&nbsp;<br>
-<center><h4>Aug 21, 2019</h4></center> <!-- date -->
+<center><h4>Aug 27, 2019</h4></center> <!-- date -->
 <br>
 <p>
 
@@ -1300,10 +1300,15 @@ The examples we have looked at so far are cases where we normally can
 invert the matrix \( \boldsymbol{X}^T\boldsymbol{X} \). Using a polynomial expansion as we
 did both for the masses and the fitting of the equation of state,
 leads to row vectors of the design matrix which are essentially
-orthogonal due to the polynomial character of our model. This may
+orthogonal due to the polynomial character of our model. Obtaining the inverse of the design matrix is then often done via a so-called LU, QR or Cholesky decomposition. 
+More material to come here.
+
+<p>
+This may
 however not the be case in general and a standard matrix inversion
-algorithm based on say LU decomposition may lead to singularities. We will see an example of this below when we try to fit
-the coupling constant of the widely used Ising model. 
+algorithm based on say LU, QR or Cholesky decomposition may lead to singularities. We will see examples of this below.
+
+<p>
 There is however a way to partially circumvent this problem and also gain some insight about the ordinary least squares approach.
 
 <p>
@@ -1314,6 +1319,9 @@ inversion algorithm. Thereafter we dive into the math of the SVD.
 
 
 </div>
+
+<p>
+<!-- todo: change model here. -->
 </section>
 
 

doc/pub/Regression/html/Regression-solarized.html

@@ -291,7 +291,7 @@ MathJax.Hub.Config({
 <center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
 <br>
 <p>
-<center><h4>Aug 21, 2019</h4></center> <!-- date -->
+<center><h4>Aug 27, 2019</h4></center> <!-- date -->
 <br>
 <p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
@@ -1368,10 +1368,15 @@ The examples we have looked at so far are cases where we normally can
 invert the matrix \( \boldsymbol{X}^T\boldsymbol{X} \). Using a polynomial expansion as we
 did both for the masses and the fitting of the equation of state,
 leads to row vectors of the design matrix which are essentially
-orthogonal due to the polynomial character of our model. This may
+orthogonal due to the polynomial character of our model. Obtaining the inverse of the design matrix is then often done via a so-called LU, QR or Cholesky decomposition. 
+More material to come here.
+
+<p>
+This may
 however not the be case in general and a standard matrix inversion
-algorithm based on say LU decomposition may lead to singularities. We will see an example of this below when we try to fit
-the coupling constant of the widely used Ising model. 
+algorithm based on say LU, QR or Cholesky decomposition may lead to singularities. We will see examples of this below.
+
+<p>
 There is however a way to partially circumvent this problem and also gain some insight about the ordinary least squares approach.
 
 <p>
@@ -1384,6 +1389,9 @@ inversion algorithm. Thereafter we dive into the math of the SVD.
 </div>
 
 
+<p>
+<!-- todo: change model here. -->
+
 <p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 

doc/pub/Regression/html/Regression.html

@@ -296,7 +296,7 @@ MathJax.Hub.Config({
 <center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
 <br>
 <p>
-<center><h4>Aug 21, 2019</h4></center> <!-- date -->
+<center><h4>Aug 27, 2019</h4></center> <!-- date -->
 <br>
 <p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
@@ -1373,10 +1373,15 @@ The examples we have looked at so far are cases where we normally can
 invert the matrix \( \boldsymbol{X}^T\boldsymbol{X} \). Using a polynomial expansion as we
 did both for the masses and the fitting of the equation of state,
 leads to row vectors of the design matrix which are essentially
-orthogonal due to the polynomial character of our model. This may
+orthogonal due to the polynomial character of our model. Obtaining the inverse of the design matrix is then often done via a so-called LU, QR or Cholesky decomposition. 
+More material to come here.
+
+<p>
+This may
 however not the be case in general and a standard matrix inversion
-algorithm based on say LU decomposition may lead to singularities. We will see an example of this below when we try to fit
-the coupling constant of the widely used Ising model. 
+algorithm based on say LU, QR or Cholesky decomposition may lead to singularities. We will see examples of this below.
+
+<p>
 There is however a way to partially circumvent this problem and also gain some insight about the ordinary least squares approach.
 
 <p>
@@ -1389,6 +1394,9 @@ inversion algorithm. Thereafter we dive into the math of the SVD.
 </div>
 
 
+<p>
+<!-- todo: change model here. -->
+
 <p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 

doc/src/Regression/Regression.do.txt

@@ -974,10 +974,14 @@ The examples we have looked at so far are cases where we normally can
 invert the matrix $\bm{X}^T\bm{X}$. Using a polynomial expansion as we
 did both for the masses and the fitting of the equation of state,
 leads to row vectors of the design matrix which are essentially
-orthogonal due to the polynomial character of our model. This may
+orthogonal due to the polynomial character of our model. Obtaining the inverse of the design matrix is then often done via a so-called LU, QR or Cholesky decomposition. 
+More material to come here. 
+
+
+This may
 however not the be case in general and a standard matrix inversion
-algorithm based on say LU decomposition may lead to singularities. We will see an example of this below when we try to fit
-the coupling constant of the widely used Ising model. 
+algorithm based on say LU, QR or Cholesky decomposition may lead to singularities. We will see examples of this below.
+
 There is however a way to partially circumvent this problem and also gain some insight about the ordinary least squares approach. 
 
 This is given by the _Singular Value Decomposition_ algorithm, perhaps
@@ -987,6 +991,9 @@ inversion algorithm. Thereafter we dive into the math of the SVD.
 
 !eblock
 
+
+# todo: change model here.
+
 !split
 =====  The Ising model =====