Statistics 215a - Some Details - Fall 2004



Please submit report as hard copy, i.e. not electronically. Thank you.

Hi everyone. The 215a in-class final will shift to the 12:30-3:30 slot on December 16, and take place in Evans 334.

The take-home part will be handed out in class and put on the web Dec. 2 . It will be due in class Dec. 9 . Here is some advice re the report: The Report

Good luck with both of these.


2003 final in class exam


Lecture notes

Classes: Tu-Th 11-12:30 in 106 Moffitt

Lab Section: Fr 12-2 in 344 Evans


Instructor: David R. Brillinger

Office Hours: 417 Evans, Tues 3:00 - 6:00

GSI: Ingileif Hallgrimsdottir

Office Hours: 432 Evans, Wed 4-5 and Thu 12:30-1:30

Course homepage

Lab homepage

Course structure: Lectures, labs, homeworks/lab assignments, midterm, final exam

Applied Statistics at an Advanced Level: The Fall Semester will concentrate on Exploratory Data Analysis (EDA), with some data mining, and the Spring on Confirmatory Data Analysis (CDA). There will be presentation, and comparative discussion of the basic tools of EDA. In the Spring there will be substantial emphasis on the assumptions on which methods are based and their examination.

It is to be noted that there is no text for the course and that the workload will be medium to heavy.

A course reader will be available

Some pertinent books

Text for Lab: W.N. Venables and B.D. Ripley. Modern Applied Statistics with S-Plus, Most recent Edition

Prerequisites: Linear algebra, advanced calculus, fourth year statistics, computing experience.

The students will be expected to analyze data sets using Splus, R or equivalents and to be able to prepare reports in Latex or Word or somesuch. The details of the R package may be found at the CRAN site ( It is suggested that students download it to their personal computers.

Syllabus: Success stories, some classic papers/history, graphing/visualisation, batch comparison techniques, fitting, model evaluation/diagnostics, difficulties, smoothing, prediction, uncertainty methods, computing, data types, multivariate methods, data mining, data warehousing