# 9 Alternative Algorithms (TODO)

As the consummate showman, P.T. Barnum is often quoted as saying “Leave them wanting more”. Unfortunately, statistics professors have less of a flare for drama. Introductory statistics courses will typically introduce a few types of models (for example, linear and *perhaps* logistic regression), and that’s a wrap. It’s often until students start *taking* the subsequent courses that they are exposed to the true limitations of previous techniques and taught to demand more.

This chapter attempts to flip that paradigm by briefly surveying a broad number of modeling techniques. The goal is not to go into all of the rigorous deals that one should understand to use these models. Instead, we hope to build a “mental toolbox” of techniques so that you know where to focus your study when you encounter a problem in the real world.

## 9.1 Not Modeling

### 9.1.1 First Principles

### 9.1.2 Simple Analyses

## 9.2 Extending Linear Regression

### 9.2.1 Modeling Binary Outcomes

### 9.2.2 Modeling Counts

### 9.2.3 Modeling Time Until an Event

### 9.2.4 Modeling Repeated Measures on a Population

### 9.2.5 Modeling Observations in a Nested Hierarchy

## 9.3 Causal Analysis Patterns

Similar to https://emilyriederer.netlify.app/post/causal-design-patterns/