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| * [http://www.amazon.com/Categorical-Analysis-Wiley-Probability-Statistics/dp/0471360937/ref=pd_bbs_1?ie=UTF8&s=books&qid=1211231537&sr=8-1 Categorical Data Analysis] by Alan Agresti. [http://www.stat.ufl.edu/~aa/cda/cda.html Online resources]. Agr02. | |
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| || Baa08 || Chapter 4.3.2 (pp. 91 - 105) || Functional relations: linear regression || || || Chapter 6 - 6.2.1 (pp. 181-198) || Regression Modeling (Introduction and Ordinary Least Squares Regression) || || || Chapter 6.6 (pp. 258-259) || General considerations || |
|| Baa08 || Section 4.3.2 (pp. 91 - 105) || Functional relations: linear regression || || || Sections 6 - 6.2.1 (pp. 181-198) || Regression Modeling (Introduction and Ordinary Least Squares Regression) || || || Section 6.6 (pp. 258-259) || General considerations || |
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| || G&H07 || Section 3.8 || Exercises 3 and 5 || || Baa08 || Section 4.7 || Exercises 3 and 7 (for Exercise 7, perform linear regression with {{{lm}}} or {{{ols}}} instead of doing ANCOVA || |
|| G&H07 || Section 3.9 (pp. 50-51) || Exercises 3 and 5 || || Baa08 || Section 4.7 (p. 126) || Exercises 3 and 7* || * (for Exercise 7, Baayen treats linear regression using {{{lm}}} or {{{ols}}} as the same as analysis of covariance (see section 4.4.1 (pp. 117-119))). |
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| === Session 2: Issues in linear regression === || Reading || || || Assignments || || |
== Session 2: Issues in linear regression == === Reading === || G&H07 || Chapter 4 (pp. 53-74) || Linear regression: before and after fitting the model || || Baa08 || Sections 6.2.2-6.2.4 (pp. 198-212) || Collinearity, Model criticism, and Validation || || || Section 6.4 (pp. 234-239) || Regression with breakpoints || |
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| == Week 2: Logistic regression == === Session 3: === || Reading || || || Assignments || || === Session 4: === || Reading || || || Assignments || || |
=== Assignments === || G&H07 || Section 4.9 (p.76) || Exercise 4 || || Baa08 || Section 6.7 (p. 260) || Exercise 1 || |
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| == Week 3: Multilevel regression == === Session 5: === || Reading || || || Assignments || || === Session 6: === || Reading || || || Assignments || || |
In addition to the book problems, we will distribute a data set from the ongoing ngrams project. |
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| == Week 4: Implementations (optional) == === Session 7: lme4 implementation details === || Reading || attachment:Implementation.pdf attachment:Theory.pdf attachment:Notes.pdf|| || Assignments || || |
== Session 3: Multilevel (a.k.a. Hierarchical, a.k.a. Mixed ) Linear Models == === Reading === || G&H07 || Sections 1.1-1.3 (pp. 1-3) || Intro, examples, motivation || || || Chapter 11 (pp. 237-248) || Multilevel structures || || || Chapter 12 (pp. 251-277) || Multilevel linear models: the basics || === Assignments === == Session 4: Logistic regression, Generalized Linear Multilevel Models == === Reading === || G&H07 || Chapter 5 (pp. 79-105) || Logistic regression || || Baa08 || Section 6.3 (pp. 214-234) || Generalized Linear Models || || || Section 6.4 (pp. 239.243) || end of Regression with breakpoints || || Agr02 || Section 16.3 (624-625) || ??? || === Assignments === == Session 5: Mixed logit models == === Reading === || G&H07 || Chapter 14 (pp. 301-321) || Multilevel logistic regression* || * In this chapter, Gelman & Hill define some multilevel models in BUGS rather than in R. We will either provide translations for you, will do the translations together in class, or will assign the translations as an assignment. === Assignments === == Session 6: Computational methods for model fitting == === Reading === || G&H07 || Chapter 18 (pp. 387-413) || Likelihood and Bayesian inference and computation || || Agr02 || Section 15.2 (pp. 604-611) || ??? || || lme4 || implementation vignettes || attachment:Implementation.pdf attachment:Theory.pdf attachment:Notes.pdf || |
HLP Lab Mini Course on Regression Methods
May 27 2008 - June 9 2008
Texts
[http://www.amazon.com/Analysis-Regression-Multilevel-Hierarchical-Models/dp/0521867061/ref=sr_1_1?ie=UTF8&s=books&qid=1211219851&sr=8-1 Data Analysis Using Regression and Multilevel/Hierarchical Models] by Gelman & Hill (2007). [http://www.stat.columbia.edu/~gelman/arm/ Online resources]. G&H07.
[http://www.amazon.com/Analyzing-Linguistic-Data-Introduction-Statistics/dp/0521882591/ref=sr_1_1?ie=UTF8&s=books&qid=1211219948&sr=8-1 Analyzing Linguistic Data: A Practical Introduction to Statistics using R] by Harald Baayen (2008). [attachment:baayen_analyzing_08.pdf Complete electronic draft]. Baa08.
[http://www.amazon.com/Introductory-Statistics-R-Peter-Dalgaard/dp/0387954759/ref=sr_1_1?ie=UTF8&s=books&qid=1211228905&sr=8-1 Introductory Statistics with R]. [http://staff.pubhealth.ku.dk/~pd/ISwR.html Online resources]. Dal04.
[http://www.amazon.com/Categorical-Analysis-Wiley-Probability-Statistics/dp/0471360937/ref=pd_bbs_1?ie=UTF8&s=books&qid=1211231537&sr=8-1 Categorical Data Analysis] by Alan Agresti. [http://www.stat.ufl.edu/~aa/cda/cda.html Online resources]. Agr02.
R packages
[http://cran.r-project.org/web/packages/Design/index.html Design]. Linear and generalized linear regression.
[http://cran.r-project.org/web/packages/lme4/index.html lme4]. Multilevel modeling.
[http://cran.r-project.org/web/packages/arm/index.html ARM]. Companion package for Gelman & Hill (2007).
[http://cran.r-project.org/web/packages/languageR/index.html languageR]. Companion package for Baayen (2008).
Session 0: Basics
Understanding of this material will be assumed throughout the course. Please read these introductory materials and make sure you understand them before beginning the readings for the first session.
Reading
Baa08 |
Chapter 1 (pp. 1-20) |
Intro to R. |
G&H07 |
Chapter 2 (pp. 13-26) |
Intro to probability theory. |
Dal04 |
??? |
??? |
Session 1: Linear regression
Reading
G&H07 |
Chapter 3 (pp. 29-49) |
Linear regression: the basics |
Baa08 |
Section 4.3.2 (pp. 91 - 105) |
Functional relations: linear regression |
|
Sections 6 - 6.2.1 (pp. 181-198) |
Regression Modeling (Introduction and Ordinary Least Squares Regression) |
|
Section 6.6 (pp. 258-259) |
General considerations |
Assignments
G&H07 |
Section 3.9 (pp. 50-51) |
Exercises 3 and 5 |
Baa08 |
Section 4.7 (p. 126) |
Exercises 3 and 7* |
* (for Exercise 7, Baayen treats linear regression using lm or ols as the same as analysis of covariance (see section 4.4.1 (pp. 117-119))).
Session 2: Issues in linear regression
Reading
G&H07 |
Chapter 4 (pp. 53-74) |
Linear regression: before and after fitting the model |
Baa08 |
Sections 6.2.2-6.2.4 (pp. 198-212) |
Collinearity, Model criticism, and Validation |
|
Section 6.4 (pp. 234-239) |
Regression with breakpoints |
Assignments
G&H07 |
Section 4.9 (p.76) |
Exercise 4 |
Baa08 |
Section 6.7 (p. 260) |
Exercise 1 |
In addition to the book problems, we will distribute a data set from the ongoing ngrams project.
Session 3: Multilevel (a.k.a. Hierarchical, a.k.a. Mixed ) Linear Models
Reading
G&H07 |
Sections 1.1-1.3 (pp. 1-3) |
Intro, examples, motivation |
|
Chapter 11 (pp. 237-248) |
Multilevel structures |
|
Chapter 12 (pp. 251-277) |
Multilevel linear models: the basics |
Assignments
Session 4: Logistic regression, Generalized Linear Multilevel Models
Reading
G&H07 |
Chapter 5 (pp. 79-105) |
Logistic regression |
Baa08 |
Section 6.3 (pp. 214-234) |
Generalized Linear Models |
|
Section 6.4 (pp. 239.243) |
end of Regression with breakpoints |
Agr02 |
Section 16.3 (624-625) |
??? |
Assignments
Session 5: Mixed logit models
Reading
G&H07 |
Chapter 14 (pp. 301-321) |
Multilevel logistic regression* |
* In this chapter, Gelman & Hill define some multilevel models in BUGS rather than in R. We will either provide translations for you, will do the translations together in class, or will assign the translations as an assignment.
Assignments
Session 6: Computational methods for model fitting
Reading
G&H07 |
Chapter 18 (pp. 387-413) |
Likelihood and Bayesian inference and computation |
Agr02 |
Section 15.2 (pp. 604-611) |
??? |
lme4 |
implementation vignettes |
attachment:Implementation.pdf attachment:Theory.pdf attachment:Notes.pdf |