Topic outline

  • Unit 2: Causal inference and basics of linear regression model

    To complete this unit you need to

    1. Watch the video lectures
    2. Read the materials for the written assignment 1 (mandatory)
    3. Complete the unit 2 discussion forum task
    4. Return written assignment 1 (mandatory)
    5. View the model answer for written assignment 1 and instructor's comments to written assignment 1 (mandatory)
    6. Start working on data analysis assignment 1 (mandatory)
    7. Participate in the seminar (mandatory)
    8. Participate in the computer class (optional)
    9. Complete your caption assignments for unit 2 and all earlier units
    10. 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

    1. 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.
    2. 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.
    3. 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.)

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Unit 1: Introduction
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Unit 3: Assumptions and diagnostics in linear regression models