Editing User:Aaronshaw/Stats course (section)

== Books and resources ==

This class will use a freely-licensed textbook:

* Diez, David M., Christopher D. Barr, and Mine Çetinkaya-Rundel. 2015. ''OpenIntro Statistics''. 3rd edition. OpenIntro, Inc. ([https://www.openintro.org/download.php?file=os3&referrer=/stat/textbook.php PDF]; [https://www.openintro.org/download.php?file=os3_tablet&referrer=/stat/textbook.php Table-friendly PDF]; [https://www.openintro.org/stat/textbook.php Other])

The texbook (in any format) is required material for the course. You can download it at no cost and/or buy (affordable!) hard copy versions in either [https://www.openintro.org/redirect.php?go=amazon_os3_hardcover&referrer=/stat/textbook.php full color hardcover] or in [https://www.openintro.org/redirect.php?go=createspace_os3&referrer=/stat/textbook.php black and white paperback]. The book is excellent and has been adopted widely. It has also developed a large online community of students and teachers who have shared other resources. Lecture slides, videos, notes, and more are all freely licensed (many through the website and others elsewhere).

I will also assigning several chapters from the following:

* Reinhart, Alex. 2015. ''Statistics Done Wrong: The Woefully Complete Guide''. SF, CA: No Starch Press. ([https://www.safaribooksonline.com/library/view/statistics-done-wrong/9781457189845/ Safari online via NU libraries])

This book provides a conceptual introduction to some common failures in statistical analysis that you should learn to recognize and avoid. It was also written by a Ph.D. student. You have access to an electronic copy via the NU library, but you may find it helpful to purchase.

A few other books may be useful resources while you're learning to analyze, visualize, and interpret statistical data with R. I will share some advice about these during the first class meeting:

* Healy, Kieran. 2019. ''Data Visualization: A Practical Introduction''. Princeton, NJ: Princeton UP. ([https://kieranhealy.org/publications/dataviz/ via Healy's website])
* Teetor, Paul. 2011. ''R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics''. 1 edition. Sebastopol, CA: O’Reilly Media. ([http://proquest.safaribooksonline.com/9780596809287 Safari Proquest/NU Libraries]; [https://en.wikipedia.org/wiki/Special:BookSources/978-0-596-80915-7 Various Sources]; [https://www.amazon.com/Cookbook-Analysis-Statistics-Graphics-Cookbooks/dp/0596809158/ref=sr_1_1?ie=UTF8&qid=1482802812&sr=8-1&keywords=r+cookbook Amazon])
* Verzani, John. 2014. ''Using R for Introductory Statistics, Second Edition''. 2 edition. Boca Raton: Chapman and Hall/CRC. ([https://en.wikipedia.org/wiki/Special:BookSources/978-1-4665-9073-1 Various Sources]; [https://www.amazon.com/Using-Introductory-Statistics-Second-Chapman/dp/1466590734/ref=mt_hardcover?_encoding=UTF8&me= Amazon])
* Wickham, Hadley. 2010. ''ggplot2: Elegant Graphics for Data Analysis''. 1st ed. 2009. Corr. 3rd printing 2010 edition. New York: Springer. ([https://link.springer.com/book/10.1007%2F978-3-319-24277-4 Springer/NU Libraries]; [https://en.wikipedia.org/wiki/Special:BookSources/978-0-596-80915-7 Various Sources])

There are also some invaluable non-textbook resources:

* [ftp://cran.r-project.org/pub/R/doc/contrib/Baggott-refcard-v2.pdf Baggott's R Reference Card v2] — Print this out. Take it with you everywhere and look at it dozens of times a day. You will learn the language faster!
* [https://stackoverflow.com/questions/tagged/r StackOverflow R Tag] — Somebody already had your question about how to do ''X'' in R. They asked it, and several people have answered it, on StackOverflow. Learning to read this effectively will take time but as build up some basic familiarity with R and with StackOverflow, it will get easier. I promise.
* [http://rseek.org/ Rseek] — Rseek is a modified version of Google that just search R websites online. Sometimes, R is hard to search before because R is a common letter. This has become much easier over time as R has become more popular but it might still be the case sometimes and Rseek is a good solution.
* [https://ggplot2.tidyverse.org/ ggplot2 documentation] — Ggplot is a powerful data visualization package for R that I recommend highly. The documentation is indispensable for learning how to use it.