Statistics 203:  Introduction to Regression Models and ANOVA

Winter 2009    ●   Tu Th      9:30-10:45 PM   ●   Sequoia Hall 200

Nancy R. Zhang   ●  nzhang atstanford ●   Office Hours:  Th  2:15-4:15 PM Sequoia Hall 141   

A N N O U N C E M E N T S

The final exam can now be downloaded here.   For data sets, see here.

Here are the solutions to the practice midterm.

Practice Midterm:  This is a modification of a midterm from previous years.  Your midterm will be similar in format, but with less emphasis on random effects and variable transformations, more emphasis on nested model testing.  The midterm will be open book, open notes.

The R introductory session will be Friday, 1/16 4:00-5:15 PM in the Sequoia Hall Computer Lab (Room 211).

        R introductory session slides by Yueh Wen Liao.

C O U R S E    D E S C R I P T I O N

This course introduces statistical regression models and ANOVA.  The content is very similar to STAT 191, but with more matrix theory.

• Linear regression models, ANOVA,
• Hypothesis testing and parameter estimation in linear models
• Model building, variable selection
• Nonlinear models: Poisson and Logistic regression
• Robust regression

We will cover the basic concepts behind these models, and apply them to the analysis of data sets.    Please see the syllabus for more information.

P R E R E Q U I S I T E S

Basic probability and statistics at the level of Stat 200 and Stat 116.   Basic linear algebra.

T A

T E X T B O O K S

Neter et al., Applied Linear Statistical Models, 5th Edition (Required)

Weisberg, Applied Linear Regression, 2nd Edition (Reference)

D A T A

Data sets used in this class are here.

L E C T U R E S (Materials will be posted here after every lecture.)

A S S I G N M E N T S

Assignments need to be handed in at the beginning of lecture on the due date.  Solutions are posted the following day.  You can be late by at most one day on at most 1 problem set, with deduction of 10% on grade.

R

We will be using R for most of the data analysis in this class.  R can be freely downloaded here.  If you are new to R, here is a brief introduction to the language.  The Stats 141 page also has more extensive tutorials.  Elizabeth Purdom's website also has some good resources.

Here is the R tutorial given by Jun Li from last year.

G R A D I N G