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| == Week 1: Linear regression == === Session 1: === || Reading || || || Assignments || || === Session 2: === |
== 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]. == 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 || || Agg02 || === Assignments === == Session 5: Mixed logit models == |
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| == Week 2: Logistic regression == === Session 3: === || Reading || || || Assignments || || === Session 4: === || Reading || || || Assignments || || == Week 3: Multilevel regression == === Session 5: === || Reading || || || Assignments || || === Session 6: === || Reading || || || Assignments || || == Week 4: Implementations (optional) == === Session 7: lme4 implementation details === || Reading || || || Assignments || || |
== Session 6: Computational methods for model fitting == === Reading === || 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].
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 |
|| Agg02 ||
Assignments
Session 5: Mixed logit models
Reading |
|
Assignments |
|
Session 6: Computational methods for model fitting
Reading
lme4 |
implementation vignettes |
attachment:Implementation.pdf attachment:Theory.pdf attachment:Notes.pdf |