How to succeed

... in a mathematical statistics course! 


"How do I study for this course? How do I follow?"

This course is lecture-based, but that does not mean you just sit there and tune out. You will get 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: 

As they use copyrighted material, students are not allowed to post course materials anywhere (on the internet, on their sites, in other courses, etc.). If I make slides for any lecture, 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:

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. 

Stay connected - post on Slack

Help! Do read the help pages provided on this site

General 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: 

Stats refreshers:

A lovely (and pretty complete?) list of resources for learning R: