Google Calendar Course Schedule Start Due Date Chapter Topic Jan-29 Feb-11 1 Intro to Data Feb-12 Feb-18 2 Probability Feb-19 Mar-4 3 Distributions Mar-5 Mar-18 4 Foundation for Inference Mar-19 Mar-25 5 Inference for Numerical Data Mar-26 Apr-8 6 Inference for Categorical Data Apr-9 Apr-22 7 Linear Regression Apr-23 May-6 8 Multiple & Logistic Regression May-7 May-16 Navarro Introduction to Bayesian Analysis May-17 May-21 Final Exam »
There will be weekly meetups. You are encouraged to attend as many as you can but recordings will generally be availabe the day after the meetup. You can join the meetup from your computer, tablet or smartphone at: https://global.gotomeeting.com/join/890853557. You can also dial in using your phone: (646) 749-3122 Access Code: 890-853-557 Presentation Signup Sheet Date Topic Resources Wed, Jan 31, 8:00 pm Intro to Course Slides, Video Wed, Feb 7, 8:00 pm Intro to Data Slides, Video Wed, Feb 14, 8:00 pm Probability Slides, Video Wed, Feb 21, 8:00 pm Distributions Part I Slides, Video, R Wed, Feb 28, 8:00 pm Distributions Part II Slides, Video, R Wed, Mar 7, 8:00 pm Foundation for Inference Slides, Video Wed, Mar 14, 8:00 pm Foundation for Inference cont. »
Required Diez, D.M., Barr, C.D., & Çetinkaya-Rundel, M. (2015). OpenIntro Statistics (3rd 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. (2015, version 0.5). 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. »
We will make use of R, an open source statistics program and language. Be sure to install R and RStudio on your own computers within the first few days of the class. R - Windows or Mac RStudio - Download Windows or Mac version from here LaTeX - Windows use MiKTeX or Mac use BasicTeX (it is best to use Safari to download this file as Chrome or Firefox will often fail) If using Windows, you also need to download and install these: »
These are some useful resources on the web for learning R. Feel free to suggest other resources by clicking the “Edit 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. »