How to succeed
... in a mathematical statistics course!
"How do I study for this course? How do I follow?"
This course is a blend of traditional and interactive lectures, hands-on homework assignments, and some practical and some theoretical exercises. These components are the core of the course and will help you learn.
Lectures in this course are mostly hand-written notes, though sometimes we will see slides. Be prepared to:
read the textbook and all provided reading resources,
ask questions. Question everything!,
work in teams AND work alone,
participate in interactive quiz questions throughout the lecture,
read the textbook and all provided reading resources (no, this is not a typo! I wrote it again),
spend some serious time brainstorming about homework problems.
As they use copyrighted material, students are not allowed to post course materials anywhere (on the internet, on their sites, in other courses, etc.). Once updated, lecture slides will be released with a link here under a Creative Commons 3.0 License; stay tuned for more info.
Many problems you will be working on are traditional, so to speak. This is for several reasons:
You need to be exposed to fundamentals of statistics;
You need to spend time thinking about the problems;
You need to develop or fine-tune your writing skills. Explain steps. Justify reasoning. Draw conclusions. Communicate clearly.
However, some problems will be applied or non-traditional. Consulting other sources is not recommended; particularly for the students planning to take the PhD qualifying exam, as you need to practice writing the solutions completely and clearly, on your own. If you are not sure how to work in teams and what types of collaborations are encouraged/allowed/forbidden, please consult the course policies page.
Help! Do read the help pages provided on this site.
Campuswire (course code provided 1st lecture)
Software & Statistics help
New to R? No idea what Markdown is? Can't remember something "basic" in stats? Just need a refresher?
Not to worry!
Writing scripts: One of our reference texts has a lot of background and examples - it builds scripts bottom-up! You can always start there. If you want to browse a few other resources, these seem excellent choices:
A site with various tutorials on R and statistics
Learn R in 15 minutes from Prof. Yang at UIC (visit this page with so many more links, too!)
A lovely (and pretty complete?) list of resources for learning R: