small update

doc/src/Projects/2021/Project1/Project1.do.txt

@@ -106,9 +106,10 @@ distribution $N(0,1)$.
 *Write your own code* (using either a matrix inversion or a singular
 value decomposition from e.g., _numpy_ ) or use your code from
 homeworks 1 and 2 and perform a standard least square regression
-analysis using polynomials in $x$ and $y$ up to fifth order. Find the
-"confidence intervals":"https://en.wikipedia.org/wiki/Confidence_interval" of the parameters (estimators) $\beta$ by computing their
-variances, evaluate the Mean Squared error (MSE)
+analysis using polynomials in $x$ and $y$ up to fifth order.
+
+Evaluate the Mean Squared error (MSE)
+
 
 !bt 
 \[ MSE(\bm{y},\tilde{\bm{y}}) = \frac{1}{n}
@@ -134,7 +135,10 @@ where we have defined the mean value  of $\bm{y}$ as
 \]
 !et
 
-Your code has to include a scaling of the data (for example by
+Plot the resulting scores (MSE and R$^2$) as functions of the polynomial degree (here up to polymial degree five).
+Plot also the parameters $\beta$ as you increase the order of the polynomial. Comment your results.
+
+Your code has to include a scaling/centering of the data (for example by
 subtracting the mean value), and
 a split of the data in training and test data. For this exercise you can
 either write your own code or use for example the function for