**Table of contents**

## Introduction

## Prerequistes

### Topics

To understand the content of the course you should be familiar with these topics:

- Random variables, including expectation and variance
- Dependence/independence of random variables
- Conditional distributions
- The Bernoulli distribution and normal distribution
- Estimators and estimates

Topics that we will cover in the course, but which you should ideally have previously studied:

- Confidence intervals and standard errors
- Hypothesis tests, Errors (“Type I” and “Type II”), p-values, statistical significance
- The linear model, OLS

### Skills

You will need to use the following computing techniques:

- Basic numerical computing using Python
- Data manipulation using Pandas and Numpy
`numpy.random`

for simulating random samples- Statsmodels for regressions. (scikit-learn does not have good support for inference.)
- Jupyter notebooks

### Review materials

## Nonconcerns

The following topics will not be covered in the course:

- Instrumental variables
- Difference-in-differences (diff-in-diffs)
- Regression discontinuity designs (RDD)
- Synthetic controls
- Analysis of variance (ANOVA)