Statistics 203: Introduction to Regression Models and ANOVA
Winter 2010 ● Tu Th
9:30-10:45 PM ● Cummings Art Building Room 2
Nancy R. Zhang ●
nzhang atstanford ● Office Hours: Tu, Thur
2:15-3:15Sequoia Hall 141
A N N O U N C E M E N T S
3/11: Here is the final
exam. The data
sets are here.
3/4:
To convert binomial table to a 0-1 bernoulli
table, try this script
(it uses the NFL data).
2/26: Note the change in due date for HW
4.
2/19: Clarifications for HW3: Problem 5 (RABE 11.7): (a) Start with
any model with 19 predictors. (d)
Use data up to 1992 to fit your model, and predict the year 1996.
2/18: Problem set 2 has been graded and
placed in the 203 box in second floor Sequoia Hall.
1/26: The midterm is next Thursday, Feb
4. It will be in-class, open book
and open note. During office hours
next Tuesday 2:15-3:15 I will hold a review session, at the Girshick
Library in downstairs Sequoia Hall.
1/19: Lecture was cancelled due to power
outages on campus. The lecture
slides are posted. We will pick up
on Thursday. Due date for HW1 is
extended to next Tuesday, 1/26.
This year’s R
introductory session slides by Pei He.
The syllabus has been updated with
textbook references.
Note the change in classroom to Cummings
Art Building Room 2.
Note the change in Nancy’s office
hours to Tu Thur 2:15-3:15,
and the updating of the TA’s office hours.
An R introductory session will be Friday, 1/15 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.
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
Yunting Sun (yunting.sun at gmail) Office
hours: 12:30-1:30 PM Friday, Sequoia Hall 244
Pei
He (hepei at stanford) Office hours:
11 am-12 pm Thursday, Sequoia Hall 244
T E X T B O O K S
RABE: Chatterjee and Hadi, Regression
Analysis by Example, 4th Edition (Required)
KNNL: Kutner et al., Applied Linear Statistical Models, 5th Edition (Reference)
Weisberg, Applied Linear Regression, 2nd Edition (Reference)
D A T A
Data sets used in this class are here.
T E N T A T I V E
S Y L L A B U S (Materials will be posted here
after every lecture.)
I follow the book very
loosely. The course slides will be
your best reference. Some lectures
expose material not in RABE. For
example, the lectures on fixed and random effects come mostly from KNNL, and
those on model selection incorporate recent developments not in either
textbook. The sections numbers from
the books are listed for reference.
This schedule is tentative and may be adjusted to students’ needs
during the quarter.
|
Date |
Materials |
|
Tu
1/5 |
Review.
Slides, R examples. |
|
Th
1/7 |
Simple
linear regression (RABE 2). Slides, R examples. |
|
Tu
1/12 |
Inference,
diagnostics for linear regression (RABE 4.1-4.11). |
|
Th
1/14 |
Inference,
diagnostics for linear regression (RABE 4.1-4.11). Slides, R examples |
|
Tu
1/19 |
Multiple
regression, constraints, predictions. (RABE 3) Slides,
R examples |
|
Th
1/21 |
(Slides, R examples are from
Tuesday’s lecture) |
|
Tu
1/26 |
Multiple
diagnostics, ANOVA, (RABE 4.11-4.13, 5) |
|
Th
1/28 |
Fixed
and Random effects (KNNL 25) Slides, R examples |
|
Tu
2/2 |
Random
and mixed effects (KNNL 25) Slides, R examples |
|
Th
2/4 |
Midterm
|
|
Tu
2/9 |
Weighted
least squares, Variable transformations, PCA. (RABE 7, 9.4-9.5) Slides, R examples |
|
Th
2/11 |
PCA. Slides,
R examples |
|
Tu
2/16 |
Model
selection: step-wise procedures. (RABE 11) |
|
Th
2/18 |
Model
selection: ridge, LASSO, and LARS (RABE 11) Slides,
R examples, Hesterberg et al. review |
|
Tu
2/23 |
Logistic
regression. (RABE 12) Slides are continuing on last lecture, R examples |
|
Th
2/25 |
Logistic
regression. (RABE 12) Slides, R examples |
|
Tu
3/2 |
Contingency
tables. (KNNL 14.13) Slides, R examples |
|
Th
3/4 |
Contingency
tables. (KNNL 14.13) Slides, R examples |
|
Tu
3/9 |
Regression
with correlated errors (time series) (RABE 8,9). Slides, R examples |
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.
|
Due
date |
File |
Solutions |
|
Thurs, 1/26 |
||
|
Tues, 2/4 |
||
|
Tues, 2/23 (Tentative) |
||
|
Tues, 3/9 (Tentative) |
R
We will be using R for most of the data analysis in this class. R can be freely downloaded here.
G R A D I N G
|
Homeworks |
40% |
|
Midterm
(in class) |
20% |
|
Final
problem set (take home) |
40% |