Introduction to Statistics (PhD): School of Criminal Justice - RU-N
Intermediate Statistics | 27:202:542 |
Wednesday, 10:00-12:40 | Room: CLJ 571 |
Lab: Wed, 1:30-3:00 | Room: CLJ 571 |
frank.edwards@rutgers.edu | Office hours: Friday, 10-2PM |
TA: christiane.schwarz@rutgers.edu | Office hours: Tuesday 10-2 |
This is the course syllabus for Introduction to Statistics, Fall 2019. It is a Ph.D. level introduction to conducting quantitative social science research, and is the first part of a two-semester sequence. By the end of this course, you will be familiar with how to manipulate, visualize, and model quantitative data.
We will work primarily from two books.
And one companion website:
Note that the Wickham and Grolemund text is available for free as an e-book.
I’ve set up a Slack page for us to communicate about the course. This can be a resource for you to collaborate and ask me questions about homework, and will also be a spot where course announcements are posted. Invites will be circulated before the course begins.
Come prepared (do the readings).
Complete homeworks on time. Homeworks should take between 4-12 hours to complete. Start them early.
Be respectful and professional. Be mindful of the space you take up in the classroom. Food and drink are allowed, but please keep the cell phone use and non-course related computer use to a minimum.
Bring your computer. Most of the work we’ll be doing involves writing code, so bring a computer with you to class. Let me know if access to a laptop is an issue.
Collaborate with your colleagues. I encourage you all to work together to complete assignments. However, I do expect you each to submit your own homework writeups.
No prior statistics or programming experience is assumed. I assume that you are comfortable with algebra, geometry, and basic calculus.
All instruction will be conducted in the R statistical programming language. R is free and open-source, and can be downloaded here.
We will be using the RStudio integrated development environment. RStudio provides a powerful text editor and a range of very useful utilities.
In addition to writing code, it is a great tool for writing reports, papers, and slides using RMarkdown. This syllabus, most of my course materials, and most of my academic papers are based on Markdown. You are required to submit assignments using RMarkdown.
Lastly, I recommend learning some form of version control to ensure your work is a) backed up, b) easily accessible to collaborators and c) reproducible. Git and GitHub are great and flexible tools for software development that have powerful applications for researchers. Here’s a useful intro to GitHub for R users.
Course grading is based on course participation (20 percent) and homework assignments (80 percent). There is no final project.
Problem sets provide you an opportunity to directly apply what we’ve learned to real-world data analysis and statistical problems.
I will assign homework each week. Assignments are due on the following Tuesday at 12pm. So for the first week of class, the homework assigned on 9/4 will be due on 9/10.
Homework should be submitted to me via email (frank.edwards@rutgers.edu). Each student is allowed two 3-day extensions without penalty for a homework due-date over the semester.
I expect to see your code, code output, and your interpretations of the results for each question. You will submit your homeworks as compiled RMarkdown documents. I will instruct you on how to use this software during the first week of class. You can submit your compiled homeworks in either .html or .pdf format.
Late homeworks without a requested extension are penalized at 5 points per day late. Make every effort to submit homework on time, as we will be moving quickly.
9/4 | Introduction | Imai 1 (all); Wickham 2, 5, 6, 8, 9-11; Arnold 1 |
9/11 | Causality (1) | Imai 2.1-2.5; Arnold 2 |
9/18 | Causality (2) | Imai 2.5 -2.7; Wickham 12 |
9/25 | Measurement (1) | Imai 3.1-3.4; Wickham 3, 7; Arnold 3.1-3.4 |
10/2 | Measurement (2) | Imai 3.5-3.8; Arnold 3.5-3.6 |
10/9 | Prediction (1) | Imai 4.1-4.2; Arnold 4.1-4.2 |
10/16 | Prediction (2) | Imai 4.3-4.4; Arnold 4.3 |
10/23 | Discovery (1) | Imai 5.1-5.2; Arnold 5.1-5.2 |
10/30 | Discovery (2) | Imai 5.3-5.4; Arnold 5.3 |
11/6 | Probability (1) | Imai 6.1-6.2; Arnold 6.1-6.2 |
11/13 | No class, ASC | |
11/20 | Probability (2) | Imai 6.3-6.5; Arnold 6.3-6.4 |
11/27 | No class, University break | |
12/4 | Uncertainty (1) | Imai 7.1-7.2; Arnold 7.1-7.2 |
12/11 | Uncertainty (2) | Imai 7.3-7.4; Arnold 7.3 |