**Linear Discriminant Analysis in Matlab**- Write a matlab m file with functions that enable a full linear discriminant analysis as we saw lda in Splus.
- A predict function that says which class a test observation would be assigned to using a chosen number of discriminant variables.
- A cross validation facility enabling the computation of the estimated cross validated percentage of well-classified observations.

**Bootstrap of a Principal Component Analysis**- Choice of either Splus or matlab for this project.
- Write a pca program.
- Write a bootstrap program that generates new resamples.
- Execute the pca program many times, each time, lining up the components so they are comparable.
- Project all the reconstructions on a same plane.
- Construct convex hulls around these points.

**Interface Xgobi with matlab**- Write a matlab front end for calling xgobi on a matlab matrix. This requires using !cmex the compiler for matlab and also getting matrices from matlab into a compatible structure for xgobi to deal with
**Projection Pursuit algorithm**- With either Splus or matlab,
write a projectiuon pursuit program
that finds the best
two dimensional plane to project to optimize
several possible indices:

-Friedman and Tukey's

-Entropy

-optionally-a user defined one

By using the resident optimization, or by programming your own.

**Program a Multidimensional Method of your choice**- Up to you, preferably with quality graphics etc....