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Statistics and Statistical Programming (Spring 2019)
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== Schedule == When reading the schedule below, the following key might help resolve ambiguity: §n denotes chapter n; §n.x denotes section x of chapter; §n.x-y denotes sections x through y of chapter n. === Week 1: Thursday April 4: Introduction, Setup, and Data and Variables === * [[Statistics and Statistical Programming (Spring 2019)/Session plan: Week 1]] Please complete the readings and assignment prior to class so that we can discuss them and start talking through some of the examples in R together. '''Required Readings:''' * Diez, Barr, and Çetinkaya-Rundel: §1 (Introduction to data) * Kramer, Adam D. I., Jamie E. Guillory, and Jeffrey T. Hancock. 2014. Experimental Evidence of Massive-Scale Emotional Contagion through Social Networks. ''Proceedings of the National Academy of Sciences'' 111(24):8788–90. [[http://www.pnas.org/content/111/24/8788.full Open Access]] '''Recommended Readings:''' * Verzani: §1 (Getting Started), §2 (Univariate data) [[https://canvas.northwestern.edu/verzani_ch1-ch2.pdf Available via Canvas]] * Verzani: §A (Programming) * Healy: §2 (and skim the preferatory material as well as §1) '''Assignment (Complete before class):''' * [[Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 1]] '''Lectures:''' * [https://communitydata.cc/~ads/teaching/2019/stats/r_lectures/w01-R_lecture.zip Week 1 R lecture materials] (.zip file) * [https://communitydata.cc/~ads/teaching/2019/stats/screencasts/w01-s01-intro.webm Week 1 screencast (part 1, 23 minutes)] (the video should load directly in browser window) * [https://communitydata.cc/~ads/teaching/2019/stats/screencasts/w01-s02-intro.webm Week 1 screencast (part 2, 27 minutes)] '''Resources:''' * [https://www.openintro.org/download.php?file=os3_slides_01&referrer=/stat/slides/slides_0x.php Mine Çetinkaya-Rundel's OpenIntro §1 Lecture Notes] * [https://www.openintro.org/stat/videos.php OpenIntro Video Lectures] including some for §1 === Week 2: Thursday April 11: Probability and Visualization === * [[Statistics and Statistical Programming (Spring 2019)/Session plan: Week 2]] * Questions? Topics you'd like to discuss? Add them to the [https://canvas.northwestern.edu/courses/90927/discussion_topics/601700 Canvas discussion] for this week's material. '''Required Readings:''' * Diez, Barr, and Çetinkaya-Rundel: §2 (Probability) * Shaw, Aaron and Yochai Benkler. 2012. A tale of two blogospheres: Discursive practices on the left and right. ''American Behavioral Scientist''. 56(4): 459-487. [[https://doi.org/10.1177%2F0002764211433793 available via NU libraries]] '''Recommended Readings:''' * Verzani: §3.1-2 (Bivariate data), §4 (Multivariate data), §5 (Multivariate graphics) <!---[[https://faculty.washington.edu/makohill/com521/verzani-usingr-ch3.1-2_ch4_ch5.pdf Available with UW NetID]]---> * [https://seeing-theory.brown.edu/ Seeing Theory] §1 (Basic Probability) and §2 (Compound Probability). (Note: this site provides a beautiful visual introduction to core concepts in probability and statistics). <!--- * 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]] ---> * Healy: §3. '''Assignment (Complete Before Class):''' * [[Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 2]] '''Lectures:''' * [https://communitydata.cc/~ads/teaching/2019/stats/r_lectures/w02-R_lecture.Rmd Week 2 R lecture materials] (.Rmd file) * [https://communitydata.cc/~ads/teaching/2019/stats/screencasts/w02.webm Week 2 screencast (17 minutes)] '''Resources:''' * [https://www.openintro.org/download.php?file=os3_slides_02&referrer=/stat/slides/slides_0x.php Mine Çetinkaya-Rundel's OpenIntro §2 Lecture Notes] * [https://www.openintro.org/stat/videos.phpOpenIntro Video Lectures] including 2 short videos for §2 === Week 3: Thursday April 18: Distributions === * [[Statistics and Statistical Programming (Spring 2019)/Session plan: Week 3]] '''Required Readings:''' * Diez, Barr, and Çetinkaya-Rundel: §3.1-3.2, §3.4: You should read the rest of the chapter (§3.3 and §3.5). I won't assign problem set questions about it but it's still important to be familiar with. '''Recommended Readings:''' * Verzani: §6 (Populations) * [https://seeing-theory.brown.edu/ Seeing Theory] §3 (Probability Distributions). '''Assignment (Complete Before Class):''' * [[Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 3]] '''Lectures:''' * [https://communitydata.cc/~ads/teaching/2019/stats/r_lectures/w03-R_lecture.Rmd Week 3 R lecture materials] (.Rmd file) * [https://communitydata.cc/~ads/teaching/2019/stats/screencasts/w03.webm Week 3 screencast (19 minutes)] '''Resources:''' * [https://www.openintro.org/download.php?file=os3_slides_03&referrer=/stat/slides/slides_0x.php Mine Çetinkaya-Rundel's OpenIntro §3 Lecture Notes] * [https://www.openintro.org/stat/videos.php OpenIntro Video Lectures] including 2 videos for §3.1 and §3.2 === Week 4: Thursday April 25: Statistical significance and hypothesis testing === * [[Statistics and Statistical Programming (Spring 2019)/Session plan: Week 4]] '''Required Readings:''' * Diez, Barr, and Çetinkaya-Rundel: §4 (Foundations for inference) '''Recommended Readings:''' * Verzani: §7 (Statistical inference), §8 (Confidence intervals) * [https://seeing-theory.brown.edu/ Seeing Theory] §4 (Frequentist Inference) '''Assignment (Complete Before Class):''' * [https://docs.google.com/forms/d/e/1FAIpQLScMkAPwWQUjB4C5wtbkemkNZYjNl3ipO4Dg5wsORFmdfduEtA/viewform?usp=sf_link Mid-quarter course evaluation survey] (by Monday please!) * [[Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 4]] '''Lectures:''' *[https://communitydata.cc/~ads/teaching/2019/stats/r_lectures/w04-R_lecture.Rmd Week 4 R lecture materials] (.Rmd file) *(No screencast for this week) '''Resources:''' * [https://www.openintro.org/download.php?file=os3_slides_04&referrer=/stat/slides/slides_0x.php Mine Çetinkaya-Rundel's OpenIntro §4 Lecture Notes] * [https://www.openintro.org/stat/videos.php OpenIntro Video Lectures] including 7 videos for nearly all of §4 === Week 5: Thursday May 2: Continuous Numeric Data & ANOVA === * [[Statistics and Statistical Programming (Spring 2019)/Session plan: Week 5|Session plan]] '''Required Readings:''' * Diez, Barr, and Çetinkaya-Rundel: §5 (Inference for numerical data) <!---* 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 from Hill's website]]---> * Sweetser, K. D., & Metzgar, E. (2007). Communicating during crisis: Use of blogs as a relationship management tool. ''Public Relations Review'', 33(3), 340–342. [[https://doi.org/10.1016/j.pubrev.2007.05.016 Available through NU Libraries]] * Reinhart, §1 '''Recommended Readings:''' * Verzani: §9 (significance tests), §12 (Analysis of variance) * Gelman, Andrew and Hal Stern. 2006. “The Difference Between ‘Significant’ and ‘Not Significant’ Is Not Itself Statistically Significant.” ''The American Statistician'' 60(4):328–31. [[http://dx.doi.org/10.1198/000313006X152649 Available through NU Libraries]] '''Assignment (Complete Before Class):''' * [[Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 5]] '''Lectures:''' * No new R material for this week. <!--- * [[Statistics and Statistical Programming (Spring 2019)/R lecture outline: Week 5]] * [https://communitydata.cc/~mako/2017-COM521/com521-week_05-ttests_and_anova.ogv Week 5 R lecture screencast: t-tests] (~22 minutes) * [https://communitydata.cc/~mako/2017-COM521/com521-week_05-for_if.ogv Week 5 R lecture screencast: for loops and if statements] (~12 minutes) ---> '''Resources:''' * [https://www.openintro.org/download.php?file=os3_slides_05&referrer=/stat/slides/slides_0x.php Mine Çetinkaya-Rundel's OpenIntro §5 Lecture Notes] === Week 6: Thursday May 9: Categorical data === * [[Statistics and Statistical Programming (Spring 2019)/Session plan: Week 6|Session plan]] '''Required Readings:''' * Diez, Barr, and Çetinkaya-Rundel: §6.1-6.4 (Inference for categorical data). * 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 Hill's personal website]] * Reinhart, §4 and §5. '''Recommended Readings: * Diez, Barr, and Çetinkaya-Rundel: §6.5-6.6 (Small samples and randomization inference) * 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 NU Libraries]] (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.) '''Assignment (Complete Before Class):''' * [[Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 6]] '''Lectures:''' *[https://communitydata.cc/~ads/teaching/2019/stats/r_lectures/w06-R_lecture.Rmd Week 6 R lecture materials] (.Rmd file) *(No screencast for this week) '''Resources:''' * [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 === Week 7: Thursday May 16: Linear Regression === * [[Statistics and Statistical Programming (Spring 2019)/Session plan: Week 7|Session plan]] '''Required Readings:''' * Diez, Barr, and Çetinkaya-Rundel: §7 (Introduction to linear regression) * 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. * 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 NU libraries]] '''Recommended Readings:''' * Verzani: §11.1-2 (Linear regression). * [https://seeing-theory.brown.edu/ Seeing Theory] §5 (Regression Analysis) '''Assignment (Complete Before Class):''' * [[Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 7]] * Final project planning document (see details above!) '''Lectures:''' * [https://communitydata.cc/~ads/teaching/2019/stats/r_lectures/w07-R_lecture.Rmd Week 7 R lecture materials] '''Resources:''' * [https://www.openintro.org/download.php?file=os3_slides_07&referrer=/stat/slides/slides_0x.php Mine Çetinkaya-Rundel's OpenIntro §7 Lecture Notes] * [https://www.openintro.org/download.php?file=os3_slides_08&referrer=/stat/slides/slides_0x.php Mine Çetinkaya-Rundel's OpenIntro §8 Lecture Notes] * [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 === Week 8: Thursday May 23: Polynomial Terms, Interactions, and Logistic Regression === * [[Statistics_and_Statistical_Programming_(Spring_2019)/Session plan: Week 8|Session plan]] '''Required Readings:''' * Diez, Barr, and Çetinkaya-Rundel: §8 (Multiple and logistic regression) * [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. * (Revisit) 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 NU libraries]] * Reinhart, §8 and §9. '''Recommended Readings:''' * Verzani: §11.3 (Linear regression), §13.1 (Logistic regression) * 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]] * 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]] '''Assignment (Complete Before Class):''' * [[Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 8]] '''Lectures:''' *[https://communitydata.science/~ads/teaching/2019/stats/r_lectures/w08-R_lecture.Rmd Week 8 R lecture materials] '''Resources:''' * [https://www.openintro.org/download.php?file=os3_slides_08&referrer=/stat/slides/slides_0x.php Mine Çetinkaya-Rundel's OpenIntro §8 Lecture Notes] * [https://www.openintro.org/stat/videos.php OpenIntro Video Lectures] including a video on §8.4 * 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] === Week 9: Thursday May 30: Loose ends and Final Presentations (part 1) === * [[Statistics_and_Statistical_Programming_(Spring_2019)/Session plan: Week 9|Session plan]] '''Required readings:''' * Reinhart, §10 and §11. '''[[Statistics_and_Statistical_Programming_(Spring_2019)/Final_project_presentations|Final presentations]]: (part 1)''' * First batch today. The rest next week. '''Resources:''' * [https://communitydata.cc/~ads/teaching/2019/stats/r_lectures/w09-R_lecture.html Week 9 R-lecture] (we will use this in class) === Week 10: Thursday June 6: Fully reproducible research example, Replications, Final Presentations (part 2), and wrap-up === * Fully [https://www.overleaf.com/read/tkdpdcspwtkp reproducible research example]. * [https://canvas.northwestern.edu/courses/90927/files/folder/resources/Straub-Cook%20Replication Research replication study] by Polly Straub-Cook (UW Comm. Ph.D. student) :: (n.b.: cluster & heteroscedasticity robust standard errors!) * '''[[Statistics_and_Statistical_Programming_(Spring_2019)/Final_project_presentations|Final presentations]]: (part 2)''' :: Second batch of presenters today. * Closing thoughts :: What next? Beyond your final projects... :: Class social gathering Followed by much rejoicing!
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