After the foundational material is presented (roughly the first third of the class), I will introduce a large variety of statistical models and methods. I will choose these based on what makes sense from a pedagogical perspective at first, but as the semester goes on I will choose more and more material based on students interest and class projects.

For more information on the content of the class, see the detailed lecture notes online, which gives a general outline. Here's another version of some of the material:

### Foundations

- What is statistics?
- What is political methodology?
- Models and a language of inference
- The role of simulation
- To solve probability problems
- to evaluate estimators
- to compute features of probability distributions
- to transform statistical results into quantities of interest
- Stochastic components (normal, log-normal, Bernoulli, Poisson, etc)
- The relationship between stochastic and systematic components and data generation processes
- Systematic components (linear, logit, etc.)
- Uncertainty and Inference
- Probability as a model of uncertainty
- Probability distributions, theory, discrete, continuous, examples
- Inference
- Inverse probability problems
- The likelihood theory of inference
- The Bayesian theory of inference
- Detailed example: Forecasting presidential elections
- Properties of maximum likelihood estimation (finite sample, asymptotic, etc.)
- Precision of likelihood estimates

### Specific Topics

We will not get to all these topics, and the list of topics we do cover will likely include others than those listed here, depending on student interest.

- Discrete regression models
- Binary variables
- Interpreting functional forms
- Ordinal variables
- Grouped uncorrelated binary variables
- Event count models --- Correlated and uncorrelated events; over and under dispersion.
- Basic time series models
- Basic multiple equation models, including identification
- Multinomial choice models
- Models for selection bias, censoring, and truncation
- Models for duration
- Hurdle models
- Case-control designs
- Model dependence
- Matching as nonparametric preprocessing
- Rare events
- Neural network models
- An overview of MCMC methods
- Compositional data
- Missing data (item and unit nonresponse) problems
- Ecological inference (avoiding aggregation bias)
- Models for reciprocal causation and endogenity
- Empirical and hierarchical Bayesian analysis
- Time series cross-sectional data
- Models for interpersonal incomparability in surveys
- Text Analysis