To complete this unit you need to
- Watch the video lectures
- Read the materials for the written assignment 1 (mandatory)
- Complete the unit 2 discussion forum task
- Return written assignment 1 (mandatory)
- View the model answer for written assignment 1 and instructor's comments to written assignment 1 (mandatory)
- Start working on data analysis assignment 1 (mandatory)
- Participate in the seminar (mandatory)
- Participate in the computer class (optional)
- Submit reflection and feedback for unit 2
The unit introduces the principles of causal inference and basics of linear regression models. After this unit, you should
- Understand the three conditions for causality and why you cannot Interpret regression results as evidence of causality unless the regression is used as a part of a research design that takes the temporal order of cause and effect into account and more importantly eliminates rival explanations.
- Understand that regression (and other analysis results) need to be interpreted and explained to a reader in a way that makes then understandable for people that do not know statistical analysis very well. You should also know that p-value is not the main result but simply indicates which regression coefficients you should probably pay more attention to when interpreting the results.
- Understand that statistical analysis is not rocket science, but a collection of fairly intuitive ideas and simple mathematics. (If you go beyond regression, then the math can get a bit more complicated.)