# Midterm Exam

## Exam Information

**You may not have anything with you** for the exam (no notes, etc) **except a calculator.** I write questions such that you can get a perfect score *without* a calculator, and answers should typically be simple whole numbers. However, I understand that if nothing else, calculators are moral support and you can use them. I will provide simple calculators if you need to borrow one, as well as extra paper.

I write the exam so that most students can complete it in less than the required time, but you will have the whole class period, plus a few minutes between classes. Questions draw from concepts in the slides and whatever we discuss in class, **no other outside knowledge is needed.**

**You must show your work for all problems.** On all exam questions, **I give points for partial credit.** The more of your thought process you show (if you are unsure), the more points I am able to give. **Both correct answers with no work shown, or blank answers will not receive full points.**

If you have any approved testing accommodations, or know in advance you must be absent, please confirm with me ASAP and we will make arrangements

### Study Tools

## My Advice

Make sure you do all of the homework problems and learn from the answer keys to the homeworks, as well as the in-class practice problems. While some of the questions should be novel applications, conceptual questions on homeworks will get you in the right headspace to think about answering a question on an exam.

### Things Worth Knowing/Memorizing

- The difference between exogenous and endogenous variables/models
- How OLS estimators are chosen (minimize SSR)
- The four assumptions made about the error term, and which one is most important, and why
- What \(R^2\) means, in English, and the methods of calculating it
- What \(SER\) means, in English
- What homoskedasticity and heteroskedasticity mean, in English
- How to read a regression table and various forms of regression outputs from
`R`

- Interpreting what \(\hat{\beta}_0\) and \(\hat{\beta}_1\) are in terms of a graph and in terms of a question