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| * [[CommunityData:StatsGaps#Week 7|8/5 -- Discuss week 7]] | | * [[CommunityData:StatsGaps#Week 7|8/5 -- Discuss week 7]] |
| * [[CommunityData:StatsGaps#Week 8|8/12 -- Discuss week 8]] | | * [[CommunityData:StatsGaps#Week 8|8/12 -- Discuss week 8]] |
| * [[CommunityData:StatsGaps#Week 9|8/19 -- Office Hours]] | | * [[CommunityData:StatsGaps#Week 9|8/19 -- Discuss weeks 9 and 10]] |
| * [[CommunityData:StatsGaps#Week 10|8/26 -- Office Hours]] | | * [[CommunityData:StatsGaps#Week 10|8/26 -- Circle back and pick up dropped threads, discuss next steps -- what's out there, what else do you need to know to meet your goals, etc. ]] |
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| === Week 1 === | | === Week 1 === |
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| * Gelman, Andrew and Eric Loken. 2014. “The Statistical Crisis in Science Data-Dependent Analysis—a ‘garden of Forking Paths’—explains Why Many Statistically Significant Comparisons Don’t Hold Up.” ''American Scientist'' 102(6):460. [[https://www.americanscientist.org/issues/pub/2014/6/the-statistical-crisis-in-science/1 Available through Library Subscription]] (This is a reworked version of [http://www.stat.columbia.edu/~gelman/research/unpublished/p_hacking.pdf this unpublished manuscript] which provides a more detailed examples.) Also note the correction here: https://statmodeling.stat.columbia.edu/2014/10/14/didnt-say-part-2/ | | * Gelman, Andrew and Eric Loken. 2014. “The Statistical Crisis in Science Data-Dependent Analysis—a ‘garden of Forking Paths’—explains Why Many Statistically Significant Comparisons Don’t Hold Up.” ''American Scientist'' 102(6):460. [[https://www.americanscientist.org/issues/pub/2014/6/the-statistical-crisis-in-science/1 Available through Library Subscription]] (This is a reworked version of [http://www.stat.columbia.edu/~gelman/research/unpublished/p_hacking.pdf this unpublished manuscript] which provides a more detailed examples.) |
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| Learn R: | | Learn R: |
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| * [https://www.openintro.org/download.php?file=os3_slides_06&referrer=/stat/slides/slides_0x.php Mine Çetinkaya-Rundel's OpenIntro §6 Lecture Notes] | | * [https://www.openintro.org/download.php?file=os3_slides_06&referrer=/stat/slides/slides_0x.php Mine Çetinkaya-Rundel's OpenIntro §6 Lecture Notes] |
| * [https://www.openintro.org/stat/videos.php OpenIntro Video Lectures] including 4 videos for §7 | | * [https://www.openintro.org/stat/videos.php OpenIntro Video Lectures] including 4 videos for §7 |
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| === Week 7: Linear Regression ===
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| * Diez, Barr, and Çetinkaya-Rundel: §7 (Introduction to linear regression)
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| * OpenIntro eschews a mathematical approach to correlation. Look over [https://en.wikipedia.org/wiki/Correlation_and_dependence the Wikipedia article on correlation and dependence] and pay attention to the formulas. It's tedious to compute, but you should be aware of what goes into it.
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| * Lampe, Cliff, and Paul Resnick. 2004. “Slash(Dot) and Burn: Distributed Moderation in a Large Online Conversation Space.” In ''Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '04)'', 543–550. New York, NY, USA: ACM. doi:10.1145/985692.985761. [[http://dx.doi.org/10.1145/985692.985761 Available via library]]
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| Learn Stats:
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| * [[Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 7]]
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| Learn R:
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| * [https://communitydata.cc/~ads/teaching/2019/stats/r_lectures/w07-R_lecture.Rmd Week 7 R lecture materials]
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| '''Resources:'''
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| * [https://seeing-theory.brown.edu/ Seeing Theory] §5 (Regression Analysis)
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| * [https://www.openintro.org/download.php?file=os3_slides_07&referrer=/stat/slides/slides_0x.php Mine Çetinkaya-Rundel's OpenIntro §7 Lecture Notes]
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| * [https://www.openintro.org/download.php?file=os3_slides_08&referrer=/stat/slides/slides_0x.php Mine Çetinkaya-Rundel's OpenIntro §8 Lecture Notes]
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| * [https://www.openintro.org/stat/videos.php OpenIntro Video Lectures] including 4 videos for §7 and 3 videos on the sections §8.1-8.3
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| === Week 8 ===
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| Polynomial Terms, Interactions, and Logistic Regression
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| ====All:====
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| * Diez, Barr, and Çetinkaya-Rundel: §8 (Multiple and logistic regression)
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| * [https://onlinecourses.science.psu.edu/stat501/node/301 Lesson 8: Categorical Predictors] and [https://onlinecourses.science.psu.edu/stat501/node/318 Lesson 9: Data Transformations] from the PennState Eberly College of Science STAT 501 Regression Methods Course. There are several subparts (many quite short), please read them all carefully.
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| * Mako Hill wrote this document which will likely be useful for many of you: [https://communitydata.cc/~mako/2017-COM521/logistic_regression_interpretation.html Interpreting Logistic Regression Coefficients with Examples in R]
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| ====Learn Stats:====
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| * [[Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 8]]
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| ====Learn R:====
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| *[https://communitydata.science/~ads/teaching/2019/stats/r_lectures/w08-R_lecture.Rmd Week 8 R lecture materials]
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| ====Resources====
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| * Verzani: §11.3 (Linear regression), §13.1 (Logistic regression)
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| * Ioannidis, John P. A. 2005. “Why Most Published Research Findings Are False.” ''PLoS Medicine'' 2(8):e124. [[http://dx.doi.org/10.1371%2Fjournal.pmed.0020124 Open Access]]
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| * Head, Megan L., Luke Holman, Rob Lanfear, Andrew T. Kahn, and Michael D. Jennions. 2015. “The Extent and Consequences of P-Hacking in Science.” ''PLOS Biology'' 13(3):e1002106. [[http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002106 Open Access]]
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| * [https://www.openintro.org/download.php?file=os3_slides_08&referrer=/stat/slides/slides_0x.php Mine Çetinkaya-Rundel's OpenIntro §8 Lecture Notes]
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| * [https://www.openintro.org/stat/videos.php OpenIntro Video Lectures] including a video on §8.4
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