diff --git a/doc/Projects/2018/hw1/html/hw1-bs.html b/doc/Projects/2018/hw1/html/hw1-bs.html
index 829932ef6..2b32af321 100644
--- a/doc/Projects/2018/hw1/html/hw1-bs.html
+++ b/doc/Projects/2018/hw1/html/hw1-bs.html
@@ -120,7 +120,7 @@ MathJax.Hub.Config({
 <center><b>Department of Physics, University of Oslo, Norway</b></center>
 <br>
 <p>
-<center><h4>Aug 27, 2018</h4></center> <!-- date -->
+<center><h4>Aug 30, 2018</h4></center> <!-- date -->
 <br>
 <p>
 </div> <!-- end jumbotron -->
@@ -245,12 +245,12 @@ Discuss the meaning of these results. Try also to vary the coefficient in front
 Show that the variance of the parameters \( \beta \) in the linear regression method (chapter 3, equation (3.8) of <a href="https://www.springer.com/gp/book/9780387848570" target="_self">Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer</a>) is given as 
 
 $$
-mathrm{Var}(\hat{\beta}) = \left(\hat{X}^T\hat{X}\right)^{-1}\sigma^2,
+\mathrm{Var}(\hat{\beta}) = \left(\hat{X}^T\hat{X}\right)^{-1}\sigma^2,
 $$
 
 with 
 $$
-\sigma^2 = \frac{1}{N-p-1}\sum_{i=1}{N} (y_i-\tilde{y}_i)^2,
+\sigma^2 = \frac{1}{N-p-1}\sum_{i=1}^{N} (y_i-\tilde{y}_i)^2,
 $$
 
 where we have assumed that we fit a function of degree \( p-1 \) (for example a polynomial in \( x \)). 
diff --git a/doc/Projects/2018/hw1/html/hw1.html b/doc/Projects/2018/hw1/html/hw1.html
index adb5658c6..64a4c3ea1 100644
--- a/doc/Projects/2018/hw1/html/hw1.html
+++ b/doc/Projects/2018/hw1/html/hw1.html
@@ -86,7 +86,7 @@ MathJax.Hub.Config({
 <center><b>Department of Physics, University of Oslo, Norway</b></center>
 <br>
 <p>
-<center><h4>Aug 27, 2018</h4></center> <!-- date -->
+<center><h4>Aug 30, 2018</h4></center> <!-- date -->
 <br>
 
 <h2 id="___sec0">Exercise 1  </h2>
@@ -209,12 +209,12 @@ Discuss the meaning of these results. Try also to vary the coefficient in front
 Show that the variance of the parameters \( \beta \) in the linear regression method (chapter 3, equation (3.8) of <a href="https://www.springer.com/gp/book/9780387848570" target="_blank">Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer</a>) is given as 
 
 $$
-mathrm{Var}(\hat{\beta}) = \left(\hat{X}^T\hat{X}\right)^{-1}\sigma^2,
+\mathrm{Var}(\hat{\beta}) = \left(\hat{X}^T\hat{X}\right)^{-1}\sigma^2,
 $$
 
 with 
 $$
-\sigma^2 = \frac{1}{N-p-1}\sum_{i=1}{N} (y_i-\tilde{y}_i)^2,
+\sigma^2 = \frac{1}{N-p-1}\sum_{i=1}^{N} (y_i-\tilde{y}_i)^2,
 $$
 
 where we have assumed that we fit a function of degree \( p-1 \) (for example a polynomial in \( x \)). 
diff --git a/doc/Projects/2018/hw1/ipynb/ipynb-hw1-src.tar.gz b/doc/Projects/2018/hw1/ipynb/ipynb-hw1-src.tar.gz
index 875d6394b..c1f90dfe8 100644
Binary files a/doc/Projects/2018/hw1/ipynb/ipynb-hw1-src.tar.gz and b/doc/Projects/2018/hw1/ipynb/ipynb-hw1-src.tar.gz differ
diff --git a/doc/Projects/2018/hw1/pdf/hw1.p.tex b/doc/Projects/2018/hw1/pdf/hw1.p.tex
index 57890b7ac..e5bdf40f9 100644
--- a/doc/Projects/2018/hw1/pdf/hw1.p.tex
+++ b/doc/Projects/2018/hw1/pdf/hw1.p.tex
@@ -155,7 +155,7 @@ Homework 1
 
 % --- begin date ---
 \begin{center}
-Aug 27, 2018
+Aug 30, 2018
 \end{center}
 % --- end date ---
 
@@ -282,11 +282,11 @@ Discuss the meaning of these results. Try also to vary the coefficient in front
 Show that the variance of the parameters $\beta$ in the linear regression method (chapter 3, equation (3.8) of \href{{https://www.springer.com/gp/book/9780387848570}}{Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer}) is given as 
 
 \[
-mathrm{Var}(\hat{\beta}) = \left(\hat{X}^T\hat{X}\right)^{-1}\sigma^2,
+\mathrm{Var}(\hat{\beta}) = \left(\hat{X}^T\hat{X}\right)^{-1}\sigma^2,
 \]
 with 
 \[
-\sigma^2 = \frac{1}{N-p-1}\sum_{i=1}{N} (y_i-\tilde{y}_i)^2,
+\sigma^2 = \frac{1}{N-p-1}\sum_{i=1}^{N} (y_i-\tilde{y}_i)^2,
 \]
 where we have assumed that we fit a function of degree $p-1$ (for example a polynomial in $x$). 
 
diff --git a/doc/Projects/2018/hw1/pdf/hw1.pdf b/doc/Projects/2018/hw1/pdf/hw1.pdf
index 832626724..8cc421700 100644
Binary files a/doc/Projects/2018/hw1/pdf/hw1.pdf and b/doc/Projects/2018/hw1/pdf/hw1.pdf differ
diff --git a/doc/Projects/2018/hw1/pdf/hw1.tex b/doc/Projects/2018/hw1/pdf/hw1.tex
index 9b7b511d6..c722cb8e3 100644
--- a/doc/Projects/2018/hw1/pdf/hw1.tex
+++ b/doc/Projects/2018/hw1/pdf/hw1.tex
@@ -125,7 +125,7 @@ Homework 1
 
 % --- begin date ---
 \begin{center}
-Aug 27, 2018
+Aug 30, 2018
 \end{center}
 % --- end date ---
 
@@ -252,11 +252,11 @@ Discuss the meaning of these results. Try also to vary the coefficient in front
 Show that the variance of the parameters $\beta$ in the linear regression method (chapter 3, equation (3.8) of \href{{https://www.springer.com/gp/book/9780387848570}}{Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer}) is given as 
 
 \[
-mathrm{Var}(\hat{\beta}) = \left(\hat{X}^T\hat{X}\right)^{-1}\sigma^2,
+\mathrm{Var}(\hat{\beta}) = \left(\hat{X}^T\hat{X}\right)^{-1}\sigma^2,
 \]
 with 
 \[
-\sigma^2 = \frac{1}{N-p-1}\sum_{i=1}{N} (y_i-\tilde{y}_i)^2,
+\sigma^2 = \frac{1}{N-p-1}\sum_{i=1}^{N} (y_i-\tilde{y}_i)^2,
 \]
 where we have assumed that we fit a function of degree $p-1$ (for example a polynomial in $x$). 
 
diff --git a/doc/src/Projects/2018/Exercises/hw1.do.txt b/doc/src/Projects/2018/Exercises/hw1.do.txt
index 7e9b014c6..08c3d6adf 100644
--- a/doc/src/Projects/2018/Exercises/hw1.do.txt
+++ b/doc/src/Projects/2018/Exercises/hw1.do.txt
@@ -105,13 +105,13 @@ Show that the variance of the parameters $\beta$ in the linear regression method
 
 !bt
 \[
-mathrm{Var}(\hat{\beta}) = \left(\hat{X}^T\hat{X}\right)^{-1}\sigma^2,
+\mathrm{Var}(\hat{\beta}) = \left(\hat{X}^T\hat{X}\right)^{-1}\sigma^2,
 \]
 !et
 with 
 !bt
 \[
-\sigma^2 = \frac{1}{N-p-1}\sum_{i=1}{N} (y_i-\tilde{y}_i)^2,
+\sigma^2 = \frac{1}{N-p-1}\sum_{i=1}^{N} (y_i-\tilde{y}_i)^2,
 \]
 !et
 where we have assumed that we fit a function of degree $p-1$ (for example a polynomial in $x$).