diff --git a/README.md b/README.md
index 2cc25b9511d9e138fd4b640eb76a230f4557e209..a7e5b7bbced30559f9a07d6743f6bb5b3574a56a 100644
--- a/README.md
+++ b/README.md
@@ -12,17 +12,14 @@ On the fourth page of this article, they indicate that the maximum
 likelihood estimates of the logistic regression using only temperature
 are: $`\hat{\alpha}=5.085`$ and $`\hat{\beta}=-0.1156`$ and their
 asymptotic standard errors are $`s_{\hat{\alpha}}=3.052`$ and
-$s_{\hat{\beta}}=0.047`$. The Goodness of fit indicated for this model
-was $`G^{2}=18.086`$ with 21 degrees of freedom. Our goal is to
+$`s_{\hat{\beta}}=0.047`$. The Goodness of fit indicated for this model
+was $`G^{2} = 18.086`$ with 21 degrees of freedom. Our goal is to
 reproduce the computation behind these values and the Figure 4 of this
 article, possibly in a nicer looking way.
 
-<div class="CENTER">
 
-**[Here is our successful replication of Dalal et al. results using
-R](challenger.pdf)**.
-
-</div>
+[*Here is our successful replication of Dalal et al. results using
+R*](challenger.pdf).
 
 1.  Try to **replicate the computation** from Dalal et al. In case it
     helps, we provide you with two implementations of this case study