These note discuss how to perform Linear Regression analysis in R including log transformations, dummy variables, and fixed effects.

These notes describe how to perform - Difference in Differences - Instrumental Variables - Regression Discontinuity In R

In these notes, we discuss how to estimate the following models - Probit/Logit - Multinomial Logit - Conditional Logit - Mixed Logit - Berry 1994

These notes illustrate how to perform propensity score matching in R

In these notes, we will discuss several types of count models - Ordered Probit/Logit - Poisson Model - Negative Binomial - Hurdle Model - Zero Inflated Poisson Model

These notes described how to handle censored and truncated data in linear regression. We describe how to run a Tobit Model, a truncated regression, and a Heckman Sample Selection model.

These notes provide an introduction to survival analysis models including Kaplan Meier model, Cox regression, Weibull, and exponential models.

This document shows you how to calculate cluster robust standard errors in R for the the Fixed Effect Poisson Model. but this method will work with any maximum likelihood based estimation procedure. This particular presentation is useful for those individuals transitioning from STATA to R.

This webpage contains summary results from the public released Louisville Police Department Citation records

These notes provide an example on using Dummy variables, interactions, and log transformations to run a regression on earnings.

These slides provide a simple example of how to estimate simple linear regression and multiple regression in R.

A short description of the Chi Square and F Tests.

These are the introductory slides for the first day of class