update

doc/pub/week35/html/._week35-bs017.html

@@ -377,8 +377,8 @@ $$
 \tilde{y}_i = \beta_0+\beta_1x_i+\beta_2x_i^2+\beta_3x_i^3+\beta_4x_i^4.
 $$
 
-<p>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 \( \boldsymbol{X} \).
+<p>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 \( \boldsymbol{X} \).
 </p>
 
 <p>

doc/pub/week35/html/week35-reveal.html

@@ -811,8 +811,8 @@ $$
 $$
 <p>&nbsp;<br>
 
-<p>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 \( \boldsymbol{X} \).
+<p>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 \( \boldsymbol{X} \).
 </p>
 </section>
 

doc/pub/week35/html/week35-solarized.html

@@ -822,8 +822,8 @@ $$
 \tilde{y}_i = \beta_0+\beta_1x_i+\beta_2x_i^2+\beta_3x_i^3+\beta_4x_i^4.
 $$
 
-<p>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 \( \boldsymbol{X} \).
+<p>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 \( \boldsymbol{X} \).
 </p>
 
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>

doc/pub/week35/html/week35.html

@@ -899,8 +899,8 @@ $$
 \tilde{y}_i = \beta_0+\beta_1x_i+\beta_2x_i^2+\beta_3x_i^3+\beta_4x_i^4.
 $$
 
-<p>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 \( \boldsymbol{X} \).
+<p>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 \( \boldsymbol{X} \).
 </p>
 
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>

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