# Course Homepage

## Contact

**Discussion Board:**Please direct all written communication for Econ 103 to Piazza rather than the instructors’ personal email accounts. For personal issues, use Piazza’s private messaging feature to communicate directly with the course instructor and your RI.**Canvas:**We will only use canvas to access Piazza and display course grades.

## Semester Calendar

For a semester calendar, including the dates of all quizzes and exams, please see the last page of the course syllabus.

## Course Documents

Please consult these documents before posting a question about course logistics to Piazza: the syllabus in particular is positively brimming with answers to FAQs.

- Syllabus: This document is the final authority on all course policies.
- Lecture Slides: All lecture slides for the semester have now been posted.
- Lecture #23 Transcript: There are no slides for our final lecture since we’ll be carrying out an extended exercise in R. Instead I’ve a detailed pdf transcript. A file containing only the associated R code is available here.
- Review Exercises: Contains basic review questions that you should complete after each lecture. All questions that appear on in-class quizzes will be drawn from this document.
- Extensions, [solutions]: Contains more advanced questions that build on the lecture material. Each exam will contain at least one question taken verbatim from this document.
- Random Variables Handout: handout summarizing the key properties of random variables that we will learn in the course.

## R Tutorials

As part of this course you will learn the basics of statistical programming in R. Every student in this course will receive a free premium subscription to DataCamp, courtesy of DataCamp for the Classroom. I will assign interactive online tutorials from Datacamp as part of your homework, which will be complemented by in-person R instruction in recitations. The three DataCamp assignments for the semester are as follows:

### DataCamp Assignment #1

- First three chapters of “Introduction to R for Finance”

### DataCamp Assignment #2

- Final two chapters of “Introduction to R for Finance”

### DataCamp Assignment #3

- Foundations of Probability in R, chapter 1: The Binomial Distribution
- Intermediate R, chapter 1: Conditionals and Control Flow
- Intermediate R, chapter 2: Loops
- Intermediate R, chapter 3: Functions

## Reading Assignments

The abbreviation “WW” refers to Wonnacott and Wonnacott, the course textbook. All other readings will be posted on Piazza. The abbreviation “Vickers” refers to *What is a p-value Anyway*?

- Lecture 1: WW chapter 1
- Lectures 2 and 3: WW chapter 2
- Lecture 4: WW chapter 11, Kahneman chapter 17 “Regression to the Mean”
- Lectures 5, 6, and 7: WW chapter 3
- Lectures 8 and 9: WW chapter 4: everything except 4.5, Appendix to Section 4-3 (pages 739-741)
- Lecture 10: WW chapter 5 and Appendix to Sections 5-3 and 5-4 (pages 742-743)
- Lecture 11: WW Appendix to Section 4-4 (pages 741-742)
- Lecture 12: WW Section 4-5
- Lecture 13: WW 6-1 and 6-2, Vickers chapter 10.
- Lecture 14: WW chapter 7
- Lecture 15: WW 8-1, Vickers chapters 11 and 12
- Lecture 16: WW 8-2 and 8-5 part A, Vickers chapter 7
- Lecture 17: WW 8-3, 8-4, 8-5 C
- Lectures 18, 19, 20, and 21: WW 9-1, 9-2, 9-3, “Trouble in the Lab” from the Economist, Vickers chapters 13-15 and 28.
- Lectures 22 and 23: WW chapter 12, chapter 13 Sections 1-4

## Apps

- Histogram Bins, code
- Regression, code
- Binomial CDF, code
- Standard Normal CDF, code
- Power: One-sided Test for a Normal Mean, code
- Power: Two-sided Test for a Normal Mean, code
- Power: Two-sided Test for a Sample Proportion, code

## Practice Exams

The following is a complete collection of the exams I have given in Econ 103 along with full solutions. All of the questions provide useful practice, but more recent exams give a better indication of what to expect on future exams. I will assign questions from some of the older exams as homework, but we will always leave those from the two most recent semesters untouched so you can practice taking them under realistic exam conditions if desired. Note that some of the material on past exams, in particular some of the R code, may differ slightly from what we cover this semester.

### Midterm I

- Spring 2019: exam, solutions
- Spring 2018: exam, solutions
- Spring 2017: exam, solutions
- Spring 2016: exam, solutions
- Fall 2015: exam, solutions
- Spring 2015: exam, solutions
- Fall 2014: exam, solutions
- Spring 2014: exam, solutions
- Fall 2013: exam, solutions
- Spring 2013: exam, solutions
- Fall 2012: exam, solutions

### Midterm II

- Spring 2019: exam, solutions
- Spring 2018: exam, solutions
- Spring 2017: exam, solutions
- Spring 2016: exam, solutions
- Fall 2015: exam, solutions
- Spring 2015: exam, solutions
- Fall 2014: exam, solutions
- Spring 2014: exam, solutions
- Fall 2013: exam, solutions
- Spring 2013: exam, solutions
- Fall 2012: exam, solutions

### Final

- Spring 2019: exam, solutions
- Spring 2018: exam, solutions
- Spring 2017: exam, solutions
- Spring 2016: exam, solutions
- Fall 2015: exam, solutions
- Spring 2015: exam, solutions
- Fall 2014: exam, solutions
- Spring 2014: exam, solutions
- Fall 2013: exam, solutions
- Spring 2013: exam, solutions
- Fall 2012: exam, solutions

## Extra Resources / Archived Material

### Additional Handouts

- Optional Proofs for Discrete RVs: additional proofs of some results from lecture. These are optional, but use only basic techniques that we cover in Econ 103. They’re well worth a look if you want a deeper understanding of the course material.
- Optional Handout on Conditional Expectation: extra information on conditional expectation that goes beyond what you’re responsible for in Econ 103.
- How to do well in Econ 103: Advice on the best way to study for this course. Slightly out-of-date given that some aspects of the course have changed, but most of the key points still apply.
- Regression to the Mean
- Combinatorics & Classical Probability

### R Tutorials from past semesters

These are the R tutorials that I used in past semesters. They do not correspond exactly the R material that we will cover this semester, but there is a large amount of overlap. For details of the R material we will cover this semester see Piazza and “R Tutorials” above.

- R Tutorial #1
- R Tutorial #2
- R Tutorial #3
- R Tutorial #4
- R Tutorial #5
- Getting the Binomal RV from the Bernoulli
- Friends of the Normal Distribution

### Lecture Videos

I have a small number of video recordings of lectures from past semesters, all of which are posted on Vimeo. These do not correspond perfectly to the most recent lecture slides, but there is a large amount of overlap so you may find them helpful.