revised 4 Sept 2016
homework handouts references Syd
Instructor:  David Pollard 
When:  Monday, Wednesday 11:35  12:50 
Where:  WTS A60 
Office hours:  Thursday 4:005:30 
TA:  Yu Lu 
Problem session:  to be arranged if needed 
Other:  courses taught by DP in previous years 
Short description:  The geometry of least squares; distribution theory for normal errors; regression, analysis of variance, and designed experiments; numerical algorithms (with particular reference to the R statistical language). Linear algebra and some acquaintance with statistics assumed. 
Intended audience: 
The course is aimed at students (both graduate and undergraduate)
who have had some introductory exposure to probability (random variables, expected values, variances and covariances, density functions), linear algebra (matrices, orthonormal bases) and possibly some inference.
Students will be expected to learn a little about the R statistical language. [These days, every serious statistician has to know something about at least one statistical package. At least in academic statistical circles, R is the de facto standard for interactive use. And it is free.] 
Text: 

Expert advice: 
Advice from an unbiased expert with many years of experience interacting with students.

Grading: 
In the past I have always
based the final grade entirely on the weekly homework.
This year I am also considering a final exam, but I am open to
creative suggestions of alternatives.
Students who wish to work in teams (no more than 2 to a team) should submit a single solution set, with both members of the team involved in the solution of each problem. 
Topics:  Tentative list. The actual material covered will depend, in part,
on the backgrounds of students in the class. From past experience I know
there is not enough time to cover every topic in the detail it deserves.
