This document contains hands-on examples on how to explore binary dependent variables. Starting with the linear probability model, I show the logistic regression model as well as probit estimation. I am also discussing marginal effects.
This document contains hands-on examples about statistical inference. Starting from statistical hypothesis testing I present the (un)paired t-test, one and two-way ANOVA as well as MANOVA. The document concludes with some examples on bootstrap estimation.
This document contains hands-on examples of how to approach a (unknown) dataset. It is split into three sections covering frequency and contingency tables as well as their convenient visualization techniques. Furthermore, summary statistics and their corresponding visualizations are covered as well.
This document contains hands-on examples for the estimation of panel data models. I present common visualization techniques, the individual and time dimension in panel data methods, pooled OLS estimation as well as Fixed Effects and Random Effects models.
Some exploratory data analysis with data from the Kaggle competition "NFL Big Data Bowl 2021".
This document contains hands-on examples to get familiar with the linear regression model. The first section deals with some theory underlying OLS and the second section applies simple and multiple linear regression models to the mtcars dataset.
This document contains a tutorial to understand the regression discontinuity design. The RDD approach is illustrated by replicating a results from a study by Carpenter and Dobkin (2009) with RStudio.
This document contains a tutorial to understand the difference-in-differences approach. The DiD approach is illustrated by replicating a famous study by Card and Krueger (1994) with RStudio.
Using the Mid-Atlantic Wage Dataset I present basic modifications of the functional form in the linear regression model. The main topics are the manipulation of the regressand and the regressors, the inclusion of variables of nominal and ordinal scale as well as interactions. Futhermore, I discuss polynomials, step functions, regression splines and inverse functions.