Covers the concept of property testing using statistical methods.
Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.
Covers the basics of experimental design and analysis, focusing on statistical techniques like ANOVA, regression, mediation, and moderation.
Covers the basics of linear regression, OLS method, predicted values, residuals, matrix notation, goodness-of-fit, hypothesis testing, and confidence intervals.
Covers the binary choice model, error term assumptions, specific constants, invariances, and distribution properties.
