update

doc/src/week35/week35.do.txt

@@ -573,8 +573,8 @@ We assume our data can represented by a fourth-order polynomial. For the $i$th c
 \tilde{y}_i = \beta_0+\beta_1x_i+\beta_2x_i^2+\beta_3x_i^3+\beta_4x_i^4.
 \]
 !et
-we have five predictors/features. The first is the intercept $\beta_0$. The other terms are $\beta_i$ with $i=1,2,3,4$. Furthermore we have $n$ entries for each predictor. It means that our design matrix is a 
-$p\times n$ matrix $\bm{X}$.
+we have five predictors/features. The first is the intercept $\beta_0$. The other terms are $\beta_i$ with $i=1,2,3,4$. Furthermore we have $n$ entries for each predictor. It means that our design matrix is an 
+$n\times p$ matrix $\bm{X}$.
 
 
 !split