Computational Tools for Statistics
Modern Applied Statistics relies substantially on computational tools. Students pick up familiarity with these tools either out of necessity or by learning bits and pieces along the way in courses or even from office mates and the web. Not infrequently an incomplete picture results of what tools are available and how they may be combined (as is often necessary) to solve a given problem.
In this non-credit series of 4-6 lectures, I aim to provide a view of computational software for statistics based on my experience with what our students/faculty have asked me over the years. While such a focus will be directly useful to our Masters/PhD program, the general principles and approaches should find wider application. Our emphasis will be on practical matters. The class is meant to be interactive and so you are encouraged to bring your questions/problems to class.
Out of practical necessity, many of the topics covered will revolve around R, the main statistical software used for research in our department and LaTeX, the main typesetting software for publishing technical documents in our department.