# Week 9 โ Models & Prediction (10/17โ21)#

This week talks more about regression, simulation, and introduces the idea of minimizing a loss function.

## ๐ฅ Introduction#

This video introduces the week.

## ๐ฅ Simulation#

This video talks more about simulation as a method for studying statistical techniques, which you are doing in the assignment. I also describe more of NumPyโs random number generation facilities.

Tip

You should set random seeds for all work that will need randomness, including train/test splits for evaluating predictors.

## ๐ฅ Variance, Rยฒ, and the Sum of Squares#

This video provides more detail on explained variance and what the $$R^2$$ means.

## ๐ฅ Overfitting#

This video introduces the idea of overfitting: learning too much from the training data so we canโt predict the testing data.

## ๐ฉ Week 9 Quiz#

Take the Week 9 quiz in Canvas (will be up by end of Saturday).

Since this is the second of two very closely intertwined weeks, there are questions about ๐ย Week 8 โ Regression (10/10โ14) in the quiz ads well.

## โ Practice#

There are several ways you can practice the material so far:

• Practice more regressions with World Bank data

• Measure World Bank data predictive accuracy with train-test evaluation and mean squared error

## ๐ฉ Assignment 4#

Assignment 4 is due October 24, 2021.