resources
This page lists books, software, articles, and other external sources of
information for the statistics class.
textbook
The course text is
OpenIntro Statistics 3rd edition. We'll be using the 3rd edition, downloadable from that link. You can get a physical copy from the bookstore or Amazon. I've put links to their website and a local pdf in the left menu.
R
I suggest the RStudio version of the R software system that we'll be using throughout the course. See the left menu for links to the website and a page where you can run it in your web browser, once you get a username and password from Jim.
- The R Project Homepage from which you can download R and access the Introduction to R (pdf) in an online format.
- The in-built help system is good. To access the page about using "pie" for example, simply type "?pie" and hit return.
- Try R Code School seems to be a nice general introduction to using R. If you're a programmer already, you might prefer the youtube videos from Roger Peng for the Computing for Data Analysis Coursera course (if you're not already a programmer, these videos are not the place to start learning R).
- I've heard good things (but haven't looked at them myself) about R Twotorials, two minute videos that cover some specific thing you might want to do in R. Looking at the titles I think they're mostly more advanced than we'll need but you might find something useful.
- A sequence of online R tutorials by K. Black.
- The R Cookbook, a nice guide that takes an approach that I think is very much in keeping with the class, is (or will be soon) on the reserve shelf in the library.
- Googling specific topics can be very successful. Sites I've never gone to as a first step but are regularly very helpful and among my first choices to check out, other things being equal, when they show up in response to a query:
- I haven't played with it much yet, but RSeek is a search engine designed to look for R-related material.
- R-bloggers is a blog aggregator. Lots of interesting posts come through; worth checking regularly if you get enthusiastic about R. Also a good place to search for what people have done with a particular topic/issue.
ggplot2
ggplot2 is the graphics plot system that we'll be using, one of several that run within R.
spreadsheets
R cannot easily edit tables of data. The simplest approach is to use any spreadsheet program for editing, and then transfer the data between applications as a .csv (comma separated values) text file.
These text files can be somewhere online (i.e. have a URL here on our class website) or on your computer.
Spreadsheets can also be used for making plots and running statistical tests,
though they are not designed to be statistics engines per se.
Popular spreadsheets include
- Microsoft Office - the original. Costs money unless it came with your Windows computer.
- Libre Office - free and open source application
- Google Docs spreadsheet - free browser-based (requires a google account)
General Good Stuff regarding Stats
- In the past Matt used Collaborative Statistics by Illowsky and Dean as a secondary text. There might be occasional readings from this, or you might look here for an alternatve presentation of a topic you find tricky.
- Introduction to Probabability and Statistics Using R is another good resource. It's pitched at a higher level than Collaborative Stats and OpenIntro Stats and it's not complete, but we'll be using some sections and if it suits your style you can probably replace most of readings with material from this book. Another similar one is Verzani's Simple R.
- Pretty much any Big Book of Intro Stats will cover much the same material as we are covering. In the past at Marlboro we've successfully used Moore's Basic Practice of Statistics and Bluman's Elementary Statistics. Matt thinks that the former was a little better for learning from as an introductory text and the latter would do a better job as a reference book on a shelf if you suspect you'll be using statistical methods here and there over the years and will need your memory jogging.
- Some other online stats tools besides R.
- ACCENT principles for evaluating a graph.
descriptive stats
data visualization
various stats related articles struck Jim's fancy
other academic information
