Editing User:Aaronshaw/Stats course (section)

== Overview and learning objectives ==

This course provides a get-your-hands-dirty introduction to statistics and statistical programming mostly for applications in the social sciences and social computing. My main objectives are for all participants to acquire the conceptual, technical, and practical skills to conduct your own statistical analyses and become more sophisticated consumers of quantitative research in communication, HCI, and adjacent disciplines.

I will consider the course a complete success if every student is able to do all of the following things at the end of the quarter:
* Design and execute a complete quantitative research project, start to finish.
* Read, modify, and create short programs in the R statistical programming language.
* Feel comfortable reading and interpreting papers that use basic statistical techniques.
* Feel comfortable and prepared to enroll in more specialized and advanced statistics courses.

The course will cover the following techniques: t-tests; chi-squared tests; ANOVA, MANOVA, and related methods; linear regression; and logistic regression. We will also consider salient issues in quantitative research such as reproducibility and "the statistical crisis in science." We may cover other topics as time and interest allow.

The course materials will consist of readings, problem sets, and recorded lectures and screencasts (some created by me, some created by other people). The course requirements will emphasize active participation, self-evaluation, and will include a final project focused on the design and execution of an original piece of quantitative research. We will use the R programming language for all examples and assignments.

You are not required to know much about statistics or statistical programming to take this class. I will assume some (very little!) knowledge of the basics of empirical research methods and design, basic algebra and arithmetic, and a willingness to work to learn the rest. In general we are not going to cover the math behind the techniques we'll be learning. Although we may do some math, this is not a math class. This course will also not require knowledge of calculus or matrix algebra. I will *not* do proofs on the board. Instead, the class is unapologetically focused on the application of statistical methods. Likewise, while some exposure to R, other programming languages, or other statistical computing resources will be helpful, it is absolutely not assumed.