Data Analytics III and R

Linear Regression - Simple Linear Regression, Multiple Regression, Log Transformations, and Dummy Variables

Causal Inference with Linear Regression - RCT's, Difference-in-Differences, Instrumental Variables, and Regression Discontinuity

Propensity Score Matching - Matching Models and Inverse Propensity Score Weighting

Limited Dependent Variables (Discrete Choice Models) - Linear Probability Model, Probit/Logit, Multinomial Logit, Conditional Logit, Mixed Logit, Demand Estimation with Aggregate Data (Berry 1994) 

Count Models - Ordered Probit, Poisson Model,  and Negative Binomial Models

Censored and Truncated Data - Tobit, Truncated Regression, and Sample Selection (Heckman) Models

Introduction to Survival Analysis - Kaplan Meier Estimator, Cox Regression, Exponential and Weibull Accelerated Failure Time Models 

Older Items

Examples of Descriptive Graphs and Charts in R using data from the Louisville Metro Police Department Citation Records

Merging Data and Webscrapping in R

Fixed Effect Poisson Model in STATA and R

Regression Discontinuity Basics in R (adapted from Mastering Metrics)