diff --git a/index.qmd b/index.qmd
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--- a/index.qmd
+++ b/index.qmd
@@ -54,14 +54,29 @@ Welcome to our course Data Analytics with Statistics! 👋
 |	40	|	**Linear Regression models**	|		|				|		|				|		|
 |	41	|	Correlation	|	[📚](https://openintro-ims.netlify.app/model-slr.html#describing-linear-relationships-with-correlation)	|		[📑](https://docs.google.com/presentation/d/1JyvtQBRiUhjncLki3ozy4SvvgwRqS7Gmj_vUpFokzzo/export/pdf)		|	[☑️](https://forms.gle/5ntV6z8yHk8g4qgZ8)	|	<!--	[💻](https://colab.research.google.com/github/kirenz/lab-ims/blob/main/ims/07-2-correlation.ipynb)	-->	|		|
 |	42	|	Sales and ads	|		|		[📑](https://docs.google.com/presentation/d/1q9o_PycjwItr-5PxotZFFzkwdqvo8fVskv_Y0H1CbxE/export/pdf)		|		|	<!--	[💻](https://colab.research.google.com/github/kirenz/lab-models/blob/main/ae/1_intro_sales.ipynb)	-->	|		|
-|	43	|	Mean squared error	|		|		[📑](https://docs.google.com/presentation/d/1DnX0RnGCguOE2elQJFrcu2efZp6hjre7fsTrAfBJQTA/export/pdf)		|		|	<!--		-->	|		|
-|	44	|	Fitting a line and residuals	|	[📚](https://openintro-ims.netlify.app/model-slr.html#fit-line-res-cor)	|		[📑](https://docs.google.com/presentation/d/18xWbx5rkptYfOaqzpfIc7RD-LC9Y-Wgs_7tz9Z2a2hg/export/pdf)		|	[☑️](https://forms.gle/JFMXzjByDRGZtbDx8)	|	<!--		-->	|		|
-|	45	|	Least squares regression	|	[📚](https://openintro-ims.netlify.app/model-slr.html#least-squares-regression)	|		[📑](https://docs.google.com/presentation/d/1jreIuC0fOiVPHt6CUNYb-Kb4uKogCeftBZP5Zum6jSQ/export/pdf)		|		|	<!--		-->	|		|
-|	46	|	R squared	|	[📚](https://openintro-ims.netlify.app/model-slr.html#r-squared)	|	<!--	[📑](https://docs.google.com/presentation/d/1ZIijR7F877M9peDH2rhb3IO7dP6JGDd1ypz_C2pAyGI/export/pdf)	-->	|		|	<!--	[💻](https://colab.research.google.com/github/kirenz/lab-ims/blob/main/ims/07-1-fitting.ipynb)	-->	|		|
-|	47	|	Categorical predictors with two levels	|	[📚](https://openintro-ims.netlify.app/model-slr.html#categorical-predictor-two-levels)	|	<!--	[📑](https://docs.google.com/presentation/d/1Ca1pslYtl_bsPgY00JMhseV4klnU81AWw6s5Y8a15PA/export/pdf)	-->	|		|	<!--	[💻](https://colab.research.google.com/github/kirenz/lab-ims/blob/main/ims/07-3-least-squares.ipynb)	-->	|		|
-|	48	|	Outliers	|	[📚](https://openintro-ims.netlify.app/model-slr.html#outliers-in-regression)	|	<!--	[📑](https://docs.google.com/presentation/d/1UxbXpM0_BtZu9nAUczUopOOGT39hzn9Ucz0jOTYmM_Y/export/pdf)	-->	|		|	<!--	[💻](https://colab.research.google.com/github/kirenz/lab-ims/blob/main/ims/07-4-strength-fit.ipynb)	-->	|		|
-|	49	|	Multiple predictors regression 1	|	[📚](https://openintro-ims.netlify.app/model-mlr.html#model-mlr)	|	<!--	[📑](https://docs.google.com/presentation/d/1ijhtWW58Kx1-ltulPSc1dKotJrXS7ZTGi6RFCKOGStA/export/pdf)	-->	|	[☑️](https://forms.gle/wHPHMvbTDczNaQD97)	|	<!--	[💻](https://colab.research.google.com/github/kirenz/lab-ims/blob/main/ims/07-5-categorical.ipynb)	-->	|		|
-|	50	|	Multiple predictors regression 2	|		|	<!--	NA	-->	|		|	<!--		-->	|		|
-|	51	|	Multiple predictors regression 3	|		|	<!--	NA	-->	|		|	<!--	[💻](https://colab.research.google.com/github/kirenz/lab-ims/blob/main/ims/08a-1-multiple.ipynb)	-->	|		|
-|	52	|	**Linear Regression with Data Splitting**	|		|	<!--		-->	|		|	<!--	[💻](https://colab.research.google.com/github/kirenz/lab-ims/blob/main/ims/08a-2-multiple.ipynb)	-->	|		|
-|	53	|	Regression example happier	|		|	<!--	[📑](https://docs.google.com/presentation/d/1ZrzKUPZqp7GlCAh4uFpXnvUW6kQTvUgD2G-YZ7B24aM/export/pdf)	-->	|		|	<!--	[💻](https://colab.research.google.com/github/kirenz/lab-ims/blob/main/ims/08a-3-multiple.ipynb)	-->	|		|
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+|	43	|	Mean squared error	|		|		[📑](https://docs.google.com/presentation/d/1DnX0RnGCguOE2elQJFrcu2efZp6hjre7fsTrAfBJQTA/export/pdf)		|		|	<!--	[💻](https://colab.research.google.com/github/kirenz/lab-models/blob/main/ae/2_mse.ipynb)	-->	|		|
+|	44	|	Fitting a line and residuals	|	[📚](https://openintro-ims.netlify.app/model-slr.html#fit-line-res-cor)	|		[📑](https://docs.google.com/presentation/d/18xWbx5rkptYfOaqzpfIc7RD-LC9Y-Wgs_7tz9Z2a2hg/export/pdf)		|	[☑️](https://forms.gle/JFMXzjByDRGZtbDx8)	|	<!--	[💻](https://colab.research.google.com/github/kirenz/lab-ims/blob/main/ims/07-1-fitting.ipynb)	-->	|		|
+|	45	|	Least squares regression	|	[📚](https://openintro-ims.netlify.app/model-slr.html#least-squares-regression)	|		[📑](https://docs.google.com/presentation/d/1jreIuC0fOiVPHt6CUNYb-Kb4uKogCeftBZP5Zum6jSQ/export/pdf)		|		|	<!--	[💻](https://colab.research.google.com/github/kirenz/lab-ims/blob/main/ims/07-3-least-squares.ipynb)	-->	|		|
+|	46	|	R squared	|	[📚](https://openintro-ims.netlify.app/model-slr.html#r-squared)	|	<!--	[📑](https://docs.google.com/presentation/d/1ZIijR7F877M9peDH2rhb3IO7dP6JGDd1ypz_C2pAyGI/export/pdf)	-->	|		|	<!--	[💻](https://colab.research.google.com/github/kirenz/lab-ims/blob/main/ims/07-4-strength-fit.ipynb)	-->	|		|
+|	47	|	Categorical predictors with two levels	|	[📚](https://openintro-ims.netlify.app/model-slr.html#categorical-predictor-two-levels)	|	<!--	[📑](https://docs.google.com/presentation/d/1Ca1pslYtl_bsPgY00JMhseV4klnU81AWw6s5Y8a15PA/export/pdf)	-->	|		|	<!--	[💻](https://colab.research.google.com/github/kirenz/lab-ims/blob/main/ims/07-5-categorical.ipynb)	-->	|		|
+|	48	|	Outliers	|	[📚](https://openintro-ims.netlify.app/model-slr.html#outliers-in-regression)	|	<!--	[📑](https://docs.google.com/presentation/d/1UxbXpM0_BtZu9nAUczUopOOGT39hzn9Ucz0jOTYmM_Y/export/pdf)	-->	|		|	<!--		-->	|		|
+|	49	|	Multiple predictors regression 1	|	[📚](https://openintro-ims.netlify.app/model-mlr.html#model-mlr)	|	<!--	[📑](https://docs.google.com/presentation/d/1ijhtWW58Kx1-ltulPSc1dKotJrXS7ZTGi6RFCKOGStA/export/pdf)	-->	|	[☑️](https://forms.gle/wHPHMvbTDczNaQD97)	|		[💻](https://colab.research.google.com/github/kirenz/lab-ims/blob/main/ims/08a-1-multiple.ipynb)		|		|
+|	50	|	Multiple predictors regression 2	|		|	<!--	NA	-->	|		|		[💻](https://colab.research.google.com/github/kirenz/lab-ims/blob/main/ims/08a-2-multiple.ipynb)		|		|
+|	51	|	Multiple predictors regression 3	|		|	<!--	NA	-->	|		|		[💻](https://colab.research.google.com/github/kirenz/lab-ims/blob/main/ims/08a-3-multiple.ipynb)		|		|
+|	52	|	**Linear Regression with Data Splitting**	|		|	<!--		-->	|		|				|		|
+|	53	|	Regression example happier	|		|	<!--	[📑](https://docs.google.com/presentation/d/1ZrzKUPZqp7GlCAh4uFpXnvUW6kQTvUgD2G-YZ7B24aM/export/pdf)	-->	|		|		[💻](https://colab.research.google.com/github/kirenz/lab-models/blob/main/mr/happier-c.ipynb)		|		|
+|	54	|	Main model challenges	|	[📚](https://e-learning.hdm-stuttgart.de/moodle/pluginfile.php/430097/mod_resource/content/0/Hands%20on%20Machine%20Learning%2C%20Model%20Challenges.pdf)	|	<!--		-->	|		|	<!--		-->	|		|
+|	55	|	Data splitting	|		|	<!--	[📑](https://docs.google.com/presentation/d/16DAGzUTUzI_prncqDeZx2HhpNnM_xTOL_Na0xTaDeVk/export/pdf)	-->	|		|		[💻](https://colab.research.google.com/github/kirenz/lab-models/blob/main/mr/happier-splitting-c.ipynb)		|		|
+|	56	|	Sales prediction	|		|	<!--	[📑](https://docs.google.com/presentation/d/1CQUpb2fwezmni-GUeTsbxkE9ZekKK69eAzqKEdUBAm8/export/pdf)	-->	|		|		[💻](https://colab.research.google.com/github/kirenz/lab-altair/blob/main/ae/04-2-stacked-bar-chart-altair.ipynb)		|		|
+|	57	|	Sales prediction with data splitting	|		|	<!--	NA	-->	|		|		[💻](https://colab.research.google.com/github/kirenz/lab-altair/blob/main/ae/04-2-standardized-bar-chart-altair.ipynb)		|		|
+|	58	|	**Advanced Linear Regression models**	|		|	<!--		-->	|		|	<!--		-->	|		|
+|	59	|	Regression splines	|	[📚](https://www.statlearning.com/)	|	<!--	NA	-->	|		|	<!--	[💻](https://colab.research.google.com/github/kirenz/notebooks/blob/main/ims/copy/99-1-splines.ipynb)	-->	|		|
+|	60	|	Generalized additive models	|	[📚](https://www.statlearning.com/)	|	<!--	NA	-->	|		|	<!--	[💻](https://colab.research.google.com/github/kirenz/notebooks/blob/main/ims/copy/99-1-gam.ipynb)	-->	|		|
+|	61	|	Adjusted R squared	|	[📚](https://openintro-ims.netlify.app/model-mlr.html#adjusted-r-squared)	|	<!--	[📑](https://docs.google.com/presentation/d/17l_8tVrVxyZRBZylkHa4J4ACCgp43gIqVgBQ5tuB9KM/export/pdf)	-->	|		|	<!--		-->	|		|
+|	62	|	Regression diagnostics	|		|	<!--	NA	-->	|		|	<!--	[💻](https://colab.research.google.com/github/kirenz/notebooks/blob/main/ims/copy/99-1-diagnostics-c.ipynb)	-->	|		|
+|	63	|	**Model Selection Methods**	|		|	<!--		-->	|		|	<!--		-->	|		|
+|	64	|	Model selection methods	|	[📚](http://www.feat.engineering/selection.html)	|	<!--	[📑](https://docs.google.com/presentation/d/1nosMLlMYxQ6gMw9betZ9AL6z8sHGqM7s9jL-Mf0VnXo/export/pdf)	-->	|		|	<!--		-->	|		|
+|	65	|	Implicit model selection	|	[📚](https://www.statlearning.com/)	|	<!--	NA	-->	|		|		[💻](https://colab.research.google.com/github/kirenz/lab-ims/blob/main/ims/08d-1-implicit.ipynb)		|		|
+|	66	|	Lasso regression	|	[📚](https://www.statlearning.com/)	|	<!--	NA	-->	|		|		[💻](https://colab.research.google.com/github/kirenz/notebooks/blob/main/ims/copy/99-1-lasso-c.ipynb)		|		|
+|	67	|	Filter model selection	|	[📚](http://www.feat.engineering/selection.html)	|	<!--	NA	-->	|		|		[💻](https://colab.research.google.com/github/kirenz/lab-ims/blob/main/ims/08d-2-filter.ipynb)		|		|
+|	68	|	Wrapper model selection	|	[📚](http://www.feat.engineering/selection.html)	|	<!--	NA	-->	|		|		[💻](https://colab.research.google.com/github/kirenz/lab-ims/blob/main/ims/08d-3-wrapper.ipynb)		|		|
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