## 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

## 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)