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| #acl HlpLabGroup:read,write,delete,revert All:read | ## page was renamed from HlpLab/StatsCourses/HLPCourse ## page was renamed from HlpLab/StatsMiniCourse #acl HlpLabGroup,TanenhausLabGroup:read,write,delete,revert,admin All:read |
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| May 27 2008 - June 9 2008 | May 27 2008 - June 19 2008 || [[HLPMiniCourseSession0 |Session0]] || May 27 || Basics and R primer (attendance optional, reading required) || || [[HLPMiniCourseSession1 |Session1]] || May 29 || Linear regression || || [[HLPMiniCourseSession2 |Session2]] || June 5 || Issues in linear regression || || [[HLPMiniCourseSession3 |Session 3]] || June 10 || Multilevel linear regression || || [[HLPMiniCourseSession4 |Session 4]] || June 12 || Logistic regression, GLM || || [[HLPMiniCourseSession5 |Session 5]] || June 17 || Multilevel logistic regression, GLMM || || [[HLPMiniCourseSession6 |Session 6]] || June 19 || Computational methods for model fitting || || [[HLPMiniCourseSession7 |Session 7]] || ??? || R wrap-up || |
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| * [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. |
'''Master copies of the texts are available in the HLP lab (Meliora 123).''' * [[http://www.amazon.com/Analysis-Regression-Multilevel-Hierarchical-Models/dp/0521867061/|Data Analysis Using Regression and Multilevel/Hierarchical Models]] by Gelman & Hill (2007). [[http://www.stat.columbia.edu/~gelman/arm/|Online resources]]. G&H07. * Analyzing Linguistic Data: A Practical Introduction to Statistics using R by Harald Baayen (2008). [[http://www.amazon.com/Analyzing-Linguistic-Data-Introduction-Statistics/dp/0521882591/|hardback ($97)]] [[http://www.amazon.com/Analyzing-Linguistic-Data-Introduction-Statistics/dp/0521709180/|paperback ($35)]] [[attachment:baayen_analyzing_08.pdf|Complete electronic draft]]. Baa08. * [[http://www.amazon.com/Introductory-Statistics-R-Peter-Dalgaard/dp/0387954759/|Introductory Statistics with R]] by Peter Dalgaard (2004). [[http://staff.pubhealth.ku.dk/~pd/ISwR.html|Online resources]]. [[http://site.ebrary.com/lib/rochester/Doc?id=10047812|Electronic copy through U of R libraries]]. Dal04. * [[http://www.amazon.com/Categorical-Analysis-Wiley-Probability-Statistics/dp/0471360937/|Categorical Data Analysis]] by Alan Agresti (2002). [[http://www.stat.ufl.edu/~aa/cda/cda.html|Online resources]]. Agr02. |
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| * [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). |
* [[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). |
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| == 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. |
== Datasets == [[attachment:attention-r-data.csv]] |
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| === 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 || Chapter 4.3.2 (pp. 91 - 105) || || || || Chapter 6 - 6.2.1 (pp. 181-198) || || || || Chapter 6.6 (pp. 258-259) || || === Session 2: === || Reading || || || Assignments || || == 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 || attachment:Implementation.pdf attachment:Theory.pdf attachment:Notes.pdf|| || Assignments || || |
== How to read == One goal of this course is to make sure we're all comfortable with the same terminology and methods. Another goal is to make sure that as new people enter the community, we can bring them up to speed pretty quickly. To help with both of these goals, we're asking that you take some additional steps when you're doing the reading for this class. 1. Keep an eye out for redundancy. If multiple pieces of assigned reading cover the same topic, and you find a single one of the treatments to be superior and sufficient, please make a note describing the nature of the redundant content, which source you preferred, and why. This will help us develop a set of "canonical" readings on these topics. 2. Record and investigate unexplained or unclear terminology. Because we're cherry picking chapters from multiple sources, it's likely that at some point an author will use a term that was originally presented in some (unread by us) earlier section of the text. Alternatively, an author might just assume knowledge that we don't have. In any case, when you come across a term in the reading that you believe is not explained well enough, please make a note of the term and where you found it. Then, please go one step further. Do your best to find a simple definition of the term, and record it for others to use ([[http://en.wikipedia.org/wiki/Statistics|Wikipedia]] and [[http://mathworld.wolfram.com/topics/ProbabilityandStatistics.html|MathWorld]] are likely to be good resources for this, but also feel free to consult your favorite stats text books). |
HLP Lab Mini Course on Regression Methods
May 27 2008 - June 19 2008
May 27 |
Basics and R primer (attendance optional, reading required) |
|
May 29 |
Linear regression |
|
June 5 |
Issues in linear regression |
|
June 10 |
Multilevel linear regression |
|
June 12 |
Logistic regression, GLM |
|
June 17 |
Multilevel logistic regression, GLMM |
|
June 19 |
Computational methods for model fitting |
|
??? |
R wrap-up |
Texts
Master copies of the texts are available in the HLP lab (Meliora 123).
Data Analysis Using Regression and Multilevel/Hierarchical Models by Gelman & Hill (2007). Online resources. G&H07.
Analyzing Linguistic Data: A Practical Introduction to Statistics using R by Harald Baayen (2008). hardback ($97) paperback ($35) Complete electronic draft. Baa08.
Introductory Statistics with R by Peter Dalgaard (2004). Online resources. Electronic copy through U of R libraries. Dal04.
Categorical Data Analysis by Alan Agresti (2002). Online resources. Agr02.
R packages
Design. Linear and generalized linear regression.
lme4. Multilevel modeling.
ARM. Companion package for Gelman & Hill (2007).
languageR. Companion package for Baayen (2008).
Datasets
How to read
One goal of this course is to make sure we're all comfortable with the same terminology and methods. Another goal is to make sure that as new people enter the community, we can bring them up to speed pretty quickly. To help with both of these goals, we're asking that you take some additional steps when you're doing the reading for this class.
- Keep an eye out for redundancy. If multiple pieces of assigned reading cover the same topic, and you find a single one of the treatments to be superior and sufficient, please make a note describing the nature of the redundant content, which source you preferred, and why. This will help us develop a set of "canonical" readings on these topics.
Record and investigate unexplained or unclear terminology. Because we're cherry picking chapters from multiple sources, it's likely that at some point an author will use a term that was originally presented in some (unread by us) earlier section of the text. Alternatively, an author might just assume knowledge that we don't have. In any case, when you come across a term in the reading that you believe is not explained well enough, please make a note of the term and where you found it. Then, please go one step further. Do your best to find a simple definition of the term, and record it for others to use (Wikipedia and MathWorld are likely to be good resources for this, but also feel free to consult your favorite stats text books).