Differences between revisions 1 and 61 (spanning 60 versions)
Revision 1 as of 2008-05-19 17:45:34
Size: 797
Editor: colossus
Comment:
Revision 61 as of 2011-06-27 17:03:45
Size: 4333
Editor: 192
Comment:
Deletions are marked like this. Additions are marked like this.
Line 1: Line 1:
#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
Line 4: Line 6:
#pragma section-numbers 3 #pragma section-numbers 4
Line 7: Line 9:
May 27 2008 - June 9 2008 May 27 2008 - June 19 2008
Line 9: Line 11:
== Week 1: Linear regression ==
=== Session 1: ===
|| Reading || ||
|| Assignments || ||
=== Session 2: ===
|| Reading || ||
|| Assignments || ||
Line 17: Line 12:
== Week 2: Logistic regression ==
=== Session 3: ===
|| Reading || ||
|| Assignments || ||
=== Session 4: ===
|| Reading || ||
|| Assignments || ||
|| [[HLPMiniLabSession |Session0]] || May 27 || Basics and R primer (attendance optional, reading required) ||
|| [[HLPMiniLabSession |Session1]] || May 29 || Linear regression ||
|| [[HLPMiniLabSession |Session2]] || June 5 || Issues in linear regression ||
|| [[HLPMiniLabSession3 |Session 3]] || June 10 || Multilevel linear regression ||
|| [[HLPMiniLabSession4 |Session 4]] || June 12 || Logistic regression, GLM ||
|| [[HLPMiniLabSession5 |Session 5]] || June 17 || Multilevel logistic regression, GLMM ||
|| [[HLPMiniLabSession6 |Session 6]] || June 19 || Computational methods for model fitting ||
|| [[HLPMiniLabSession7 |Session 7]] || ??? || R wrap-up ||
Line 25: Line 21:
== Week 3: Multilevel regression ==
=== Session 5: ===
|| Reading || ||
|| Assignments || ||
=== Session 6: ===
|| Reading || ||
|| Assignments || ||
== Texts ==
'''Master copies of the texts are available in the HLP lab (Meliora 123).'''
Line 33: Line 24:
== Week 4: Implementations (optional) ==
=== Session 7: lme4 implementation details ===
|| Reading || ||
|| Assignments || ||
 * [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.

== 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).

== Datasets ==
attachment:attention-r-data.csv

== 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

Session0

May 27

Basics and R primer (attendance optional, reading required)

Session1

May 29

Linear regression

Session2

June 5

Issues in linear regression

Session 3

June 10

Multilevel linear regression

Session 4

June 12

Logistic regression, GLM

Session 5

June 17

Multilevel logistic regression, GLMM

Session 6

June 19

Computational methods for model fitting

Session 7

???

R wrap-up

Texts

Master copies of the texts are available in the HLP lab (Meliora 123).

R packages

Datasets

attachment:attention-r-data.csv

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).

HLPMiniCourse (last edited 2011-08-09 18:01:46 by echidna)

MoinMoin Appliance - Powered by TurnKey Linux