Statistics 255

Statistical Modeling


Course Description

Statistics 255 is a calculus-based exploration of modern probability and statistics, including simulation, the Poisson process, law of large numbers, central limit theorem, exploratory data analysis, statistical models, unbiased estimators, maximum likelihood, confidence intervals, and hypothesis testing. Modern techniques such as non-parametric smoothing and bootstrapping, as well as more traditional normal approximations and large sample methods, all contribute to the construction and analysis of statistical models.

Objectives of the course

Textbook


A Modern Introduction to Probability and Statistics. Understanding Why and How,
by Dekking, Kraaikamp, Lopuhaä, and Meester,
Springer Texts in Statistics, Springer-Verlag, 2005, ISBN 1-852-33896-2

R Programming for MIPS

These notes

R Programming for MIPS

illustrate the use of the R programming language and programming environment for constructing short demos in support of a class on statistical modeling. The examples track discussions in the text A Modern Introduction to Probability and Statistics. Understanding Why and How, by Dekking, Kraaikamp, Lopuhaä, and Meester, and make use of the data sets which accompany that text.

Exercises

An important component of our course will center on working through a substantial set of exercises in statistics and probability.

Schedule for Fall 2006

The link (pdf) is to a file which was generated from an Excel spreadsheet. It can be displayed by your web browser or by an application such as Adobe Acrobat.

Professor

Chris Parrish
Office: Woods Laboratories 120
email: cparrish@sewanee.edu



cparrish@sewanee.edu