Course Overview
Schedule
Meetups
Required Diez, D.M., Barr, C.D., & Çetinkaya-Rundel, M. (2019). OpenIntro Statistics (4th Ed). This is an open source textbook and can be downloaded in PDF format here, from the OpenIntro website, or a printed copy can be ordered from Amazon. Navarro, D. (2018, version 0.6). Learning Statistics with R This is free textbook that supplements a lot of the material covered in Diez and Barr. We will use the chapter on Bayesian analysis.
Computer Hardware This course will make extensive use of the R statistical language. You are expected to have a computer sufficient to run this the software listed below. The software will run on most platforms so a Mac or PC (running Windows or Linux) will work fine. I recommend having at least 16GBs of RAM. R and RStudio We will make use of R, an open source statistics program and language.
These are some useful resources on the web for learning R. Feel free to suggest other resources by clicking the “Improve this page” button in the top right. Learning R R for Data Science. Book by Garrett Grolemund and Hadley Wickham Quick-R. Kabakoff’s website. Great reference along with his book, R in Action. O’Reilly Try R. Great tutorial on R where you can try R commands directly from the web browser.
Occasionally you will need to type equations in homework and labs. R Markdown supports LaTeX style equations using the MathJax javascript library. I do not expect you to learn LaTeX for this course. Instead, I recommend using the free application Daum Equation Editor. It availabe online, as a Google Chrome Extension, or as a standalone Mac Application. Creating Equations with Daum Equation Editor Occasionally you will need to type equations in homework and labs.
Materials