Statistics 230A Linear Models

## Course Description

We will cover the theory and applications of linear statistical models. We will try to gain understanding and insight through algebraic and geometric approaches to the theory and by using the computer to analyze real and simulated data. Applications and computational aspects will treated using the statistical language R. I plan to cover the following topics:

- Formulation of linear statistical models
- Ordinary and generalized least squares
- Statistical properties of least squares estimates. The Gauss-Markov theorem; second order properties; inference based on the multivariate normal distribution.
- Graphical displays and diagnostic procedures
- Multiple regression and analysis of variance
- Model selection
- Ridge regression and the lasso
- Nonparametric regression
- Random effects and mixed effects models
- Robust procedures
- Nonlinear least squares
- Generalized linear models

Grades will be based on a midterm, a final exam, regular homework, and computer labs.

Pre-requisites: Statistics 135, 200B or an equivalent course in statistics at a post-calculus level. Linear algebra.

Texts: Several books will be on course reserve in the Mathematics and Statistics Library. I recommend two in particular:

*Introduction to Linear Regression Analysis, 4th Edition. * D. Montgomery et al. Wiley
*Linear Models with R*. Julian Faraway. Chapman and Hall. A free early version of this can be found in the Contributed Documentation at http://cran.us.r-project.org/

## Instructor

John Rice

Office: 425 Evans Hall

Phone: 642-6930

Email: rice "at" stat.berkeley.edu

Office hours: Wed 2-4

## GSI

Greg Hather

Office: 437 Evans

Email: ghather "at" berkeley.edu

Office hours: Th 2-4

Lab homepage

## Lectures

Tu-Thu 12:30-2:00. 332 Evans

## Lab Section

Mon 10-12. 332 Evans

## Additional Material

The R Project for Statistical Computing You can download the software we will use for this class from this site.

Oleg Mayba's concise notes on linear algebra

Charlotte Wickham's concise notes on linear models.

Phil Spector's Introduction to R and R Tutorial

## Homework

Homework 1 due September 7 solutions

Homework 2 due September 14 solutions

Homework 3 due September 21 solutions

Homework 4 due October 3 solutions

Homework 5 due October 12 solutions bodytemp.csv

Homework 6 due October 19 solutions oldfaithful.csv

Homework 7 due November 28 solutions