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This week we will discuss models with multiple predictors:

• why estimates change when you add or remove predictors
• what interactions are
• how to predict multiple regressions
• how to use multiple regression to infer causality and what are the limits of this approach

People who haven't attended the previous meeting about linear models are very welcome, the material will be self-contained. 

Nevertheless, don't hesitate to have a look at the material (folders 0 to 5) from previous workshops for a refresher on linear models and/or R.