Class 2: Data Visualization
Data Analysis with R
Introduction
Grading
Syllabus
Class 1: Summary Statistics
HW0: Installing R
Class 2: Data Visualization
HW1: Vectors, Tibbles, and Pipes
Class 3: Group Means
Class 4: Randomness
HW2: Dplyr
Class 5: Covariance and Correlation
Class 6: Murder Mystery
HW3: Ggplot2
Class 7: Intro to Linear Regression
Class 8: Deriving OLS Estimators
HW4: lm
Class 9: More on deriving OLS estimators
Class 10: Exogeneity
HW5: Custom Functions
Class 11: Distribution of Regression coefficients
Class 12: Standard Errors
HW6: map()
Class 13: Confidence Intervals
HW7: Simulations I
Class 14: Hypothesis Testing
Class 15: Multiple Regression and Multicollinearity
HW8: Simulations II
Class 16: Interactions and Squared Terms
Class 17: Causal Inference
HW9: Data Project
Class 18: Estimating Causal Effects with IV
Class 19: Estimating Supply and Demand with IV
Class 2: Data Visualization
📚 These class notes will be published here after class on 9/30.
Note: graphing paper will be useful today in class.
HW0: Installing R
HW1: Vectors, Tibbles, and Pipes