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| #* [http://www.amazon.com/Applied-Regression-Analysis-Generalized-Linear/dp/0761930426/ref=sr_1_3/002-3707949-0352833?ie=UTF8&s=books&qid=1211219661&sr=8-3 Applied Regression Analysis and Generalized Linear Models] by John Fox (2008). [http://socserv.mcmaster.ca/jfox/Books/Applied-Regression-2E/index.html Online resources]. * [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]. * [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]. |
* [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. |
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| == Week 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 || ??? || ??? || |
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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.
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).
Week 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 |
??? |
??? |
Week 1: Linear regression
Session 1:
Reading |
G&H07: |
Assignments |
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Session 2:
Reading |
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Assignments |
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Week 2: Logistic regression
Session 3:
Reading |
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Assignments |
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Session 4:
Reading |
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Assignments |
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Week 3: Multilevel regression
Session 5:
Reading |
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Assignments |
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Session 6:
Reading |
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Assignments |
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Week 4: Implementations (optional)
Session 7: lme4 implementation details
Reading |
attachment:Implementation.pdf attachment:Theory.pdf attachment:Notes.pdf |
Assignments |
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