Students who would prefer to do a project instead of the final exam may do so. Projects:

- Must include a statistical analysis of some data set (I can provide some or you can provide your own);
- Must involve regression analysis or ANOVA (both topics covered in the last
1/3 of the course and in
*M&S*chapters 11-14); - Must be between 5 and 10 pages long. Computers should be used, but the
project should be a
**paper**and not just computer output--- include only the relevant plots or tables, and describe in your own words what light they shed on the scientific problem at hand; - Are due any time before the scheduled final exam (2pm Tuesday April 29).

A typical project will begin with a description of a **scientific
question** and a description of (and full citations for) some **data
set** taken in the hope of answering that question; a description of
the **statistical methods** used to help illuminate the evidence
offered by the data; some **critical analysis** of the statistical
model used (graphical methods are especially useful here---
**scattergrams**, **residual plots**, etc. will be helpful. Did
you have to **transform** one or more of the variables? Why? Did
you have to include a **quadratic term**? How did you handle your
**variable selection** problem? Are you satisfied that the
assumptions of linearity, equality of variance, and approximate
normality are satisfied? Why?); and the **conclusions** that your
analysis helps you to draw in the context of the *original
problem*. You may find the material of *M&S* chapter 14 to
be helpful here.

One source of data sets is the book *A Handbook of Small Data Sets* by
Hand *et al.* While the book's 510 data sets are **described** only
in the book itself (you can borrow my copy in my office, and photocopy
whatever data sets you like), the data sets (just numbers, no stories) are
on-line; you can get to them by following the Data
link from the Home or Syllabus pages, then take the Hand *et al.*
link from there. I have other books with data, too, but you'd have to type
in the numbers yourself; you might also find something that interests you at
one of the on-line data archives; a good place to start looking is
here (for example, CMU's
StatLib and its
Data & Story archive are good
places to start).

Projects must demonstrate mastery of a range of statistical ideas; routine
binomial analysis of survey data would *not* be appropriate. Group
projects are welcome but would have to be substantially longer and deeper
than individual projects, and must show each participant's specific
contribution in detail.

Ask by e-mail (*wolpert@stat.duke.edu* or *feng@stat.duke.edu*) or in person
if you have additional questions. You can find us before or after
class, in our Office Hours, or at other times we're not teaching or away.
We're also happy to look over outlines or drafts and give you some
feedback and suggestions UNTIL THE LAST WEEK OF CLASS. Sorry, but we
will have little if any time during reading and exam weeks--- please
start your projects early if you would like some feedback or help on
them.