correcting typos

doc/src/week36/week36.do.txt

@@ -1,4 +1,4 @@
-TITLE: Week 36: Linear Rgeression and Statistical interpretations
+TITLE: Week 36: Linear Regression and Statistical interpretations
 AUTHOR: Morten Hjorth-Jensen {copyright, 1999-present|CC BY-NC} at Department of Physics, University of Oslo & Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University
 DATE: September 2-6, 2024
 
@@ -61,6 +61,8 @@ If you need to scale the data, not doing so will give an *unfair*
 penalization of the parameters since their magnitude depends on the
 scale of their corresponding predictor.
 
+The _Scikit-Learn_ site URL:"https://scikit-learn.org/stable/auto_examples/preprocessing/plot_all_scaling.html#plot-all-scaling-standard-scaler-section" has a good discussion of different ways of preprocessing data.
+
 Suppose as an example that you 
 you have an input variable given by the heights of different persons.
 Human height might be measured in inches or meters or
@@ -293,7 +295,7 @@ beta = fit_beta(X - X_offset, y - y_offset)
 intercept = np.mean(y_offset - X_offset @ beta)
 
 print(f"Manual intercept: {intercept}")
-print(f"Fitted beta (wiothout intercept): {beta}")
+print(f"Fitted beta (without intercept): {beta}")
 print(f"Sklearn intercept: {skl.intercept_}")
 print(f"Sklearn fitted beta (without intercept): {skl.coef_}")
 ypredictOwn = X @ beta