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Statistics and Statistical Programming (Winter 2017)
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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: Tuesday January 3: Introduction, Setup, and Data and Variables === Hopefully, the material in OpenIntro feels very familiar from COM520. The programming material will be new but I want you to read it before you come to class so we can work through the examples a group. '''Required Readings:''' * Diez, Barr, and Çetinkaya-Rundel: §1 (Introduction to data) * Verzani: §1 (Getting Started), §2 (Univariate data) [[https://faculty.washington.edu/makohill/com521/verzani-usingr-ch1_ch2.pdf Available with UWNetID]] * 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 Available through UW libraries]] '''Optional Readings:''' * Verzani: §A (Programming) '''Assignment (Complete Before Class):''' * [[Statistics and Statistical Programming (Winter 2017)/Problem Set: Week 1]] '''Lectures:''' * [https://communitydata.cc/~mako/2017-COM521/com521-week_01-r_programming_intro-20170103.ogv Week 1 R lecture screencast (Part I): Introduction to R and univariate statistics] (~1 hour 47 minutes) * [https://communitydata.cc/~mako/2017-COM521/com521-week_01-github_rscripts-20170104.ogv Week 1 R lecture screencast (Part II): Setting up git/GitHub and saving files in RStudio] (~40 minutes) * [[Statistics and Statistical Programming (Winter 2017)/R lecture outline: Week 1]] '''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 * [[Statistics and Statistical Programming (Winter 2017)/Session plan: Week 1]] === Week 2: Tuesday January 10: Probability and Visualization === '''Required Readings:''' * Diez, Barr, and Çetinkaya-Rundel: §2 (Probability) * 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]] * 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]] '''Assignment (Complete Before Class):''' * [[Statistics and Statistical Programming (Winter 2017)/Problem Set: Week 2]] '''Lectures:''' * [[Statistics and Statistical Programming (Winter 2017)/R lecture outline: Week 2]] * [https://communitydata.cc/~mako/2017-COM521/com521-week_02-lists_dataframes_graphing-20170111.ogv Week 2 R lecture screencast: lists, matrixes, data frames, and beginning graphing] (~1 hour 8 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 * [[Statistics and Statistical Programming (Winter 2017)/Session plan: Week 2]] === Week 3: Tuesday January 17: Distributions === '''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. * Verzani: §6 (Populations) '''Assignment (Complete Before Class):''' * [[Statistics and Statistical Programming (Winter 2017)/Problem Set: Week 3]] '''Lectures:''' * [[Statistics and Statistical Programming (Winter 2017)/R lecture outline: Week 3]] * [https://communitydata.cc/~mako/2017-COM521/com521-week_03-loading_data_functions_apply_misc.ogv Week 3 R lecture screencast: 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/2017-COM521/com521-week_03-dates_tapply_merge.ogv Week 3 R lecture screencast: 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/2017-COM521/com521-week_03-merge.ogv Week 3 R lecture screencast: merge()] (~13 minutes) [Rerecording of the last few minutes of the previous video.] '''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 * [[Statistics and Statistical Programming (Winter 2017)/Session plan: Week 3]] === Week 4: Tuesday January 24: Statistical significance and hypothesis testing === '''Required Readings:''' * Diez, Barr, and Çetinkaya-Rundel: §4 (Foundations for inference) * Verzani: §7 (Statistical inference), §8 (Confidence intervals) '''Assignment (Complete Before Class):''' * [[Statistics and Statistical Programming (Winter 2017)/Problem Set: Week 4]] '''Lectures:''' * [[Statistics and Statistical Programming (Winter 2017)/R lecture outline: Week 4]] * [https://communitydata.cc/~mako/2017-COM521/com521-week_04-misc_confint_simulation-20170125.ogv Week 4 R lecture screencast: order(); confidence intervals; simulations drawn from repeated random samples] (~27 minutes) '''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 * [[Statistics and Statistical Programming (Winter 2017)/Session plan: Week 4]] === Week 5: Tuesday January 31: Continuous Numeric Data & ANOVA === '''Required Readings:''' * Diez, Barr, and Çetinkaya-Rundel: §5 (Inference for numerical data) * 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 UW Libraries]] * 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 UW Libraries] * 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]] '''Assignment (Complete Before Class):''' * [[Statistics and Statistical Programming (Winter 2017)/Problem Set: Week 5]] '''Lectures:''' * [[Statistics and Statistical Programming (Winter 2017)/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: Tuesday February 7: Categorical data === '''Required Readings:''' * Diez, Barr, and Çetinkaya-Rundel: §6 (Inference for categorical data) * 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]] (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.) * 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]] '''Assignment (Complete Before Class):''' * [[Statistics and Statistical Programming (Winter 2017)/Problem Set: Week 6]] '''Lectures:''' * [[Statistics and Statistical Programming (Winter 2017)/R lecture outline: Week 6]] * [https://communitydata.cc/~mako/2017-COM521/com521-week_06-tables_chisq_debugging.ogv Week 6 R lecture screencast: Tables, <math>\chi^2</math>-tests, and debugging.] (~40 minutes) '''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: Tuesday February 14: Linear Regression === '''Required Readings:''' * Diez, Barr, and Çetinkaya-Rundel: §7 (Introduction to linear regression); §8.1-8.3 (Multiple regression) * OpenIntro eschews a mathematical instruction to correlation. Can you look over [https://en.wikipedia.org/wiki/Correlation_and_dependence the Wikipedia article on correlation and dependence] and pay attentions to the formulas. It's tedious to compute but I'd like to you to at least see what goes into it. * Verzani: §11.1-2 (Linear regression), * 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 in UW libraries]] '''Assignment (Complete Before Class):''' * [[Statistics and Statistical Programming (Winter 2017)/Problem Set: Week 7]] '''Lectures:''' * [[Statistics and Statistical Programming (Winter 2017)/R lecture outline: Week 7]] * [https://communitydata.cc/~mako/2017-COM521/com521-week_07-linear_regression.ogv Week 7 R lecture screencast: linear regression] (~42 minutes) '''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: Tuesday February 21: Polynomial Terms, Interactions, and Logistic Regression === '''Required Readings:''' * [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. * Diez, Barr, and Çetinkaya-Rundel: §8.4 (Multiple and logistic regression) * 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]] * 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 in UW libraries]] '''Optional Readings:''' * 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 (Winter 2017)/Problem Set: Week 8]] '''Lectures:''' * [[Statistics and Statistical Programming (Winter 2017)/R lecture outline: Week 8]] * [https://communitydata.cc/~mako/2017-COM521/com521-week_08-more_regression_anova_redux.ogv Week 8 R lecture screencast: more on linear regression, including interactions, polynomials, log transformations; anova] (~28 minutes) '''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 * I've written 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: Tuesday February 28: Consulting Meetings === 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 10: Tuesday March 7: Consulting Meetings === 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 ===
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