# Course Overview

## Schedule

Schedule

## Meetups

Meetups

## Textbooks

Required Diez, D.M., Barr, C.D., & Ã‡etinkaya-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.

## Software

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.

## Links

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.

## Math Equations

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

Materials