This course will cover two aspects of statistical computing.
The first aspect will cover the use of R, a statistical computing
software environment for performing statistical procedures and making
graphical displays of data.  Some previous exposure to R and to
statistics procedures will be assumed (e.g., regression, analysis of
variance, basic plotting); in this course we will focus instead on
some less familiar but very useful methods (e.g., random number
generation for simulations, diagnostic plots for validating model
assumptions, robust methods of regression, bootstrapping for standard
errors, generalized additive models, visualizng multivariate data).
The second aspect will focus on some of the consequences of using the
computer's finite arithmetic on statisical results (e.g., periods of
random number algorithms, matrix computations, expediting calculations
for smoothing algorithms such as loess, etc.).  The two books required
for this course address these two aspects.

Textbooks:

(1) John Maindonald and John Braun, Data Analysis and Graphics Using
R, Cambridge University Press

(2) Ronald Thisted, Elements of Statistical Computing, Chapman and Hall


Class time:

11:15-12:30 Tues, Thurs: Statistics House Conference Room Professor K.
Kafadar, StatHouse 205, 6-7825