Lecture | Date | Topic(s) | Data/Assignment | Reading | Computer Program |
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1 | 1/12 | Introduction; Review of simple and multiple regression |
Short answers due lecture 2 | Chapter 5 | Davis.txt prestige.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 |
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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 |
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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 |
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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 |
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EA1, 16 |
2/23 | Exam 1 Review; Statistical theory for linear models |
Chapter 10 extra |
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17 | 2/25 | Statistical theory for linear models | Chapter 10 extra |
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18 | 2/28 | Theory about TSS decomposition | Hw3, due lecture 22 | Chapter 10 extra |
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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 |
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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 |
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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 |
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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 |
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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 |
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27 | 3/28 | Detecting Collinearity | Chap. 13.1 | BFox.txt; Collinearity SAS code; Collinearity output; R Collinearity code |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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41 | 5/2 | Homework 5 review; Student talks 1, 2 |
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42 | 5/4 | Student talks 3, 4, 5, 6 | |||
43 | 5/6 | Student talks 7, 8, 9, 10 | |||
Finals week |
Office hours: M 12-2; T 12-2; W 9:30-11:30, Th 9:30-10:30 |
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Finals week |
Exam on Thursday, May 12 at 12:30 pm (in JEB 25) |
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