Editing Statistics and Statistical Programming (Winter 2017)
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* Feel comfortable reading papers that use basic statistical techniques. | * Feel comfortable reading papers that use basic statistical techniques. | ||
* Feel comfortable and prepared enrolling in future statistics courses in CSSS. | * Feel comfortable and prepared enrolling in future statistics courses in CSSS. | ||
== Note About This Syllabus == | == Note About This Syllabus == | ||
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* An identification of the dataset you will use and a description of the columns or type of data it will include. If you do not currently have access to these data, explain when you will have access to the data. | * An identification of the dataset you will use and a description of the columns or type of data it will include. If you do not currently have access to these data, explain when you will have access to the data. | ||
==== Final Project | ==== Final Project ==== | ||
;Outline Due Date: February 21 | ;Outline Due Date: February 21 | ||
;Maximum outline length: 5 pages | ;Maximum outline length: 5 pages | ||
;Paper Due Date: March 19 | ;Paper Due Date: March 19 | ||
;Maximum length: 6000 words (~20 pages) | ;Maximum outline length: 6000 words (~20 pages) | ||
;Presentation Date: March | ;Presentation Date: March 7 | ||
;All Deliverables: Turn in in Canvas | ;All Deliverables: Turn in in Canvas | ||
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I have a strong preference for you to write this paper individually but I'm open to the idea that you may want to work with others in the class. | I have a strong preference for you to write this paper individually but I'm open to the idea that you may want to work with others in the class. | ||
'''''Details Forthcoming:''''' ''Although this material is still somewhat thin, I'll be posting many additional details about the expectations for the final paper as we move forward through the quarter.'' | |||
=== Grading === | === Grading === | ||
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'''Lectures:''' | '''Lectures:''' | ||
* [https://communitydata.cc/~mako | * [https://communitydata.cc/~mako/com521-week_01-r_programming_intro-20170103.ogv Week 1 R Lecture (Part I): Introduction to R and Univariate statistics] (~1 hour 47 minutes) | ||
* [https://communitydata.cc/~mako | * [https://communitydata.cc/~mako/com521-week_01-github_rscripts-20170104.ogv Week 1 R Lecture (Part II): Setting up Git/GitHub and saving files in RStudio] (~40 minutes) | ||
* [[Statistics and Statistical Programming (Winter 2017)/R lecture outline: Week 1]] | * [[Statistics and Statistical Programming (Winter 2017)/R lecture outline: Week 1]] | ||
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* [[Statistics and Statistical Programming (Winter 2017)/R lecture outline: Week 2]] | * [[Statistics and Statistical Programming (Winter 2017)/R lecture outline: Week 2]] | ||
* [https://communitydata.cc/~mako | * [https://communitydata.cc/~mako/com521-week_02-lists_dataframes_graphing-20170111.ogv Week 2 R Lecture: Lists, Matrixes, Data Frames, and Beginning Graphing] (~1 hour 8 minutes) | ||
'''Resources:''' | '''Resources:''' | ||
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* [[Statistics and Statistical Programming (Winter 2017)/R lecture outline: Week 3]] | * [[Statistics and Statistical Programming (Winter 2017)/R lecture outline: Week 3]] | ||
* [https://communitydata.cc/~mako | * [https://communitydata.cc/~mako/com521-week_03-loading_data_functions_apply_misc.ogv Week 3 Lecture: Loading data, functions; apply, lapply, sapply; several miscellaneous functions] (~34 minutes) — This is the same material I covered in class. If you followed it, there's no reason you need to go back to this. | ||
* [https://communitydata.cc/~mako | * [https://communitydata.cc/~mako/com521-week_03-dates_tapply_merge.ogv Week 3 Lecture: Dates; tapply(); and merge()] (~38 minutes) [The audio seems to be broken for the last 10 minutes. Sorry about that! I've rerecorded that below.] | ||
* [https://communitydata.cc/~mako | * [https://communitydata.cc/~mako/com521-week_03-merge.ogv Week 3 Lecture: merge()] (~13 minutes) [Rerecording of the last few minutes of the previous video.] | ||
'''Resources:''' | '''Resources:''' | ||
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* [[Statistics and Statistical Programming (Winter 2017)/R lecture outline: Week 4]] | * [[Statistics and Statistical Programming (Winter 2017)/R lecture outline: Week 4]] | ||
* [https://communitydata.cc/~mako | * [https://communitydata.cc/~mako/com521-week_04-misc_confint_simulation-20170125.ogv Week 4 Lecture: order(); confidence intervals; simulations drawn from repeated random samples] (~27 minutes) | ||
'''Resources:''' | '''Resources:''' | ||
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* [[Statistics and Statistical Programming (Winter 2017)/R lecture outline: Week 5]] | * [[Statistics and Statistical Programming (Winter 2017)/R lecture outline: Week 5]] | ||
* [https://communitydata.cc/~mako | * [https://communitydata.cc/~mako/com521-week_05-ttests_and_anova.ogv Week 5 Lecture: t-tests] (~22 minutes) | ||
* [https://communitydata.cc/~mako | * [https://communitydata.cc/~mako/com521-week_05-for_if.ogv Week 5 Lecture: for loops and if statements] (~12 minutes) | ||
'''Resources:''' | '''Resources:''' | ||
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* Diez, Barr, and Çetinkaya-Rundel: §6 (Inference for categorical data) | * Diez, Barr, and Çetinkaya-Rundel: §6 (Inference for categorical data) | ||
* Verzani: §3.4 (Bivariate categorical data); §10.1-10.2 (Goodness of fit) | * Verzani: §3.4 (Bivariate categorical data); §10.1-10.2 (Goodness of fit) | ||
* 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 UW Libraries]] ( | * 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 UW Libraries]] (http://www.stat.columbia.edu/~gelman/research/unpublished/p_hacking.pdf This is a reworked version of this unpublished manuscript which provides a more detailed examples.) | ||
* Buechley, Leah and Benjamin Mako Hill. 2010. “LilyPad in the Wild: How Hardware’s Long Tail Is Supporting New Engineering and Design Communities.” Pp. 199–207 in ''Proceedings of the 8th ACM Conference on Designing Interactive Systems.'' Aarhus, Denmark: ACM. [[https://mako.cc/academic/buechley_hill_DIS_10.pdf PDF available on my personal website]] | * Buechley, Leah and Benjamin Mako Hill. 2010. “LilyPad in the Wild: How Hardware’s Long Tail Is Supporting New Engineering and Design Communities.” Pp. 199–207 in ''Proceedings of the 8th ACM Conference on Designing Interactive Systems.'' Aarhus, Denmark: ACM. [[https://mako.cc/academic/buechley_hill_DIS_10.pdf PDF available on my personal website]] | ||
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* [[Statistics and Statistical Programming (Winter 2017)/R lecture outline: Week 6]] | * [[Statistics and Statistical Programming (Winter 2017)/R lecture outline: Week 6]] | ||
'''Resources:''' | '''Resources:''' | ||
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* [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 | ||
=== Week 7: Tuesday February 14: Linear Regression === | === Week 7: Tuesday February 14: Simple Linear Regression === | ||
'''Required Readings:''' | '''Required Readings:''' | ||
* Diez, Barr, and Çetinkaya-Rundel: §7 (Introduction to linear regression) | * Diez, Barr, and Çetinkaya-Rundel: §7 (Introduction to linear regression) | ||
* Verzani: §11.1-2 (Linear regression), | * Verzani: §11.1-2 (Linear regression), | ||
* | * 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]] | ||
* 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]] | |||
''' | * ''Empirical Paper TBD'' | ||
''' | |||
=== Week 8: Tuesday February 21: Multiple and Logistic Regression === | |||
=== Week 8: Tuesday February 21: | |||
'''Required Readings:''' | '''Required Readings:''' | ||
* Diez, Barr, and Çetinkaya-Rundel: §8 (Multiple and logistic regression) | |||
* Diez, Barr, and Çetinkaya-Rundel: §8 | |||
* Verzani: §11.3 (Linear regression), §13.1 (Logistic regression) | * Verzani: §11.3 (Linear regression), §13.1 (Logistic regression) | ||
* | * ''Empirical Paper TBD'' | ||
=== Week 9: Tuesday February 28: Consulting Meetings === | === Week 9: Tuesday February 28: Consulting Meetings === | ||
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We won't meet as a group. Instead, you will each meet on-on-one with me to work through challenges and issues with your analysis. | We won't meet as a group. Instead, you will each meet on-on-one with me to work through challenges and issues with your analysis. | ||
=== Week 11: March 14: Final Presentations === | === Week 11: Date/Time TBD (Tentatively March 14): Final Presentations === | ||
== Administrative Notes == | == Administrative Notes == |