Lecture Schedule Spring 2011
Lecture Date Topic(s) Data/Assignment Reading Computer Program
1 1/12 Introduction;
Review of simple and multiple regression
Short answers due lecture 2 Chapter 5 Davis.txt
Davis SAS program
Davis SAS output
R Davis code
Prestige SAS program
Prestige SAS output
R Prestige code
2a,b 1/14 Matrix and SAS/IML review;
Review of statistical inference for regression
Hw1, due lecture 5 Chapter 6 IML intro
IML SAS output
R Matrix code
  1/17 MLK/Human Rights Day -
UI closed, no lecture
3 1/19 Review of statistical inference for regression;
ANCOVA, the principle of marginality
Some Hw1 hints Chapters 6-7 Ancova SAS program
Ancova SAS output
SAS-text comparison
R Ancova code
4 1/21 ANCOVA, the principle of marginality;
Analysis of variance
  Chapter 7-8  
5 1/24 Analysis of variance Hw1 due Chapters 8 Moore.txt
Anova SAS program
Anova SAS output
R Anova code
6,a,b 1/26 Statistical theory for linear models Hw2, due lecture 11 Chap. 9.1-9.2  
7 1/28 Homework 1 review;
Statistical theory for linear models
  Chap. 9.1-9.2  
8a,b 1/31 Statistical theory for linear models Some Hw2 hints Chap. 9.1-9.2  
9a,b 2/2 More statistical theory for linear models;
Properties of the least-squares estimator
  Chap. 9.3-9.4  
10 2/4 Properties of the least-squares estimator;
Inference for several coefficients
  Chap. 9.3-9.4 Gen Lin Hyp SAS code;
GLH SAS output
R GLH code
11 2/7 Inference for coefficients;
Joint Confidence Regions
Hw2 due Chap. 9.4 -9.5 Joint Conf. Ellipse 1;
Joint Conf. Ellipse 2;
Crime data info;
Crime data;
R Conf. Ellipse code
12 2/9 Random regressors;
Specification error;
Confidence and prediction intervals
Exam 1 take home problems;
Data set
Chap. 9.5-9.7  
13 2/11 Homework 2 review      
14 2/14 Vector Geometry of Linear Models   Chap. 10.1 Geometry SAS code;
LS Geometry figure
15 2/16 More Vector Geometry of Linear Models   Chap. 10.2-10.4 Anova Geometry SAS code
  2/18 Exam 1   (in JEB 005)      
  2/21 President's Day -
UI closed, no lecture
2/23 Exam 1 Review; 
Statistical theory for linear models
  Chapter 10
17 2/25 Statistical theory for linear models   Chapter 10
18 2/28 Theory about TSS decomposition Hw3, due lecture 22 Chapter 10
19 3/2 Hat values and residuals   Chap. 11-1-11.3 Duncan.txt;
Hat, resid SAS program;
Hat, resid SAS output;
R Hat, resid code
20 3/4 Measuring Influence   Chap. 11.4-11.5  
21 3/7 Joint Influence   Chap. 11.6-11.8 Partial Reg. SAS code;
Partial Reg. SAS output;
R Partial Reg. code;
R Forward Search code;
R Forward Search Figure
22a, b 3/9 The normality assumption;
The constant variance assumption
Hw3 due Chap. 12.1-12.2 SLID1.txt;
Quantile/KDE SAS program;
Quantile/KDE SAS output;
R quantile/KDE code;
WLS SAS code;
WLS SAS output;
R WLS code;
23 3/11 The constant variance assumption;
The linearity assumption
  Chap. 12.2-12.3 Sandwich SAS code;
Sandwich SAS output;
R Sandwich code
Part. resid. example SAS;
Part. resid SAS output;
SLID part.res. SAS code;
SLID part. res. output;
R part. res. code
  3/14-3/18 Spring break - no classes      
24 3/21 Homework 3 review;
Discrete data
Hw4, due lecture 29 Chap. 12.4 Vocabulary.txt;
discrete data SAS code;
discrete data SAS output;
R discrete data code
25 3/23 Box-Cox transformations   Chap. 12.5 Box-Cox SAS code;
Box-Cox output;
R Box-Cox code
26 3/25 Box-Tidwell transformations;
More tests for constant variance;
Structural Dimension
  Chap. 12.5-12.6 Box-Tidwell SAS code;
Box-Tidwell output;
R Box-Tidwell code
27 3/28 Detecting Collinearity   Chap. 13.1 BFox.txt;
Collinearity SAS code;
Collinearity output;
R Collinearity code
28 a,b 3/30 Model Respecification;
Variable Selection
Exam 2 take-home problems;
Housing data
Chap. 13.2 Model selection SAS code
Model selection output;
R Model selection code
29 4/1 Variable Selection;
Biased Estimation
Hw4 due Chap. 13.2 Ridge regr. SAS code;
Ridge regression output;
R Ridge reg. code
30 4/4 Homework 4 review;
Nonlinear Regression
  Chap. 17.4 Nonlinear regr. SAS code;
Nonlinear regr. output;
R Nonlinear code
31 4/6 Nonlinear Regression   Chap. 17.4  
  4/8 Exam 2  (in JEB 005)      
EA2, 32 4/11 Exam 2 Review;
Models for Dichotomous Data
  Chap. 14.1 Cereals.txt;
Simple Logistic Reg.code;
SAS Logistic Reg output;
R Logistic Reg. code
33 4/13 Models for Dichotomous Data   Chap. 14.1 SLID-women.txt;
Logistic Reg. SAS code;
Logistic Reg. SAS output;
R Logistic Reg. code
34 4/15 The Structure of Generalized Linear Models Hw5, for review Chap. 15.1  
35 4/18 The Structure of Generalized Linear Models;
GLMs for Count Data
  Chap. 15.1-2 Ornstein.txt;
Poisson Reg. SAS code;
Poisson Reg. output ;
R Poisson Reg. code
36 4/20 GLMs for Count Data;
Statistical Theory for Generalized Linear Models
  Chap. 15.3  
37 4/22 Statistical Theory for Generalized Linear Models   Chap. 15.3 IWLS SAS code;
IWLS SAS output
38 4/25 Diagnostics for Generalized Linear Models   Chap. 15.4 Deviance calculation SAS code;
Deviance output;
GLM Diag. SAS code;
GLM Diag. output;
R GLM Diag. code
39 4/27 Introduction to Mixed Models      
40 4/29 Introduction to Mixed Models;
Homework 5 review
Exam 3 take-home problems;
Fast Food data;
Hot Dog data
  Mixed SAS code;
Mixed SAS output;
R Mixed code
41 5/2 Homework 5 review;
Student talks 1, 2
42 5/4 Student talks 3, 4, 5, 6      
43 5/6 Student talks 7, 8, 9, 10      
Office hours: M 12-2; T 12-2;
W 9:30-11:30, Th 9:30-10:30
  . .
Exam on  Thursday, May 12 at 12:30 pm
(in JEB 25)
  . .