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Dr. Alice Lépissier
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    Lecture 1
    • Causality
      • Introduction to Causality
      • Experiments and Causality
      • Experiments vs. Observational Studies
      • Limitations of Experiments
      • Formalizing our Model
    • Probability
      • Introduction to Probability
      • Distribution of the Mean
      • Sample Mean and Law of Large Numbers
      • Central Limit Theorem
    • Bivariate OLS
      • Introduction to Bivariate Regression
      • Measures of Association
      • Regression in Practice
      • Properties of OLS
      • Problems with OLS
    • Hypothesis Testing
      • Intuition behind Hypothesis Testing
      • How to conduct Hypothesis Testing
      • Hypothesis Testing with Regression Analysis
      • Type I and Type II Errors
    • Multivariate OLS
      • Fighting Endogeneity with Multivariate Regression
      • Example of Multivariate Regression
      • Visualization with Multivariate Regression
      • Prediction with Multivariate Regression
      • Using Multivariate Regression in Practice
    • Multivariate OLS in Research
      • Research Design
      • Results
    • Types of Variables and Datasets
      • Types of Variables
      • Dummy and Log Variables in Regression
      • Types of Datasets
    • Experiments
      • Benefits and Pitfalls of Experiments
      • Experiments in Practice
      • Generalizability and Natural Experiments
    • Data Science
      • What is Data Science
      • The Data Science Workflow
      • Machine Learning

    Lecture 1 handout

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