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Statistics and Statistical Programming (Winter 2021)
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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; §n.x-y denotes sections x through y (inclusive) of chapter n. The required and recommended tasks are meant to be completed '''before class''' and will typically be necessary to complete the problem sets for each day. === Day 1: Monday January 4: Intro and setup === '''Class material:''' * [[/Day 1 session plan]] '''Required tasks:''' * Read this syllabus, discuss any questions/concerns with the teaching team. * Confirm course registration and access to [https://www.openintro.org/book/os/ the textbook] (pdf download available for $0 and b&w paperbacks for $20) as well as any software and web-services you'll need for course (Discord, Canvas, this wiki, R, RStudio). Discord invites will be sent via email. === Day 2: Wednesday January 6: Data and R === '''Class material:''' * [[/Day 2 session plan]] '''Required readings and resources:''' * Read Diez, Çetinkaya-Rundel, and Barr: §1.1-1.3 (Introduction to data) '''Recommended readings and resources:''' * Watch [https://www.youtube.com/playlist?list=PLkIselvEzpM6pZ76FD3NoCvvgkj_p-dE8 lecture materials for §1.1-3 (Videos 1-4 in the playlist)]. '''Homework:''' * Complete '''Problem set 2''': exercises from OpenIntro §1: (1.6, 1.9, 1.10, 1.16, 1.21, 1.40, 1.42, 1.43). Remember that solutions to odd-numbered problems are in the book! * Problem set 2 worked solutions [[https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/worked_solutions/worked_solutions-pset_02.html HTML], [https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/worked_solutions/worked_solutions-pset_02.rmd RMarkdown], [https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/worked_solutions/worked_solutions-pset_02.pdf PDF]] === Day 3: Monday January 11: Numerical and categorical data === '''Class material:''' * [[/Day 3 session plan]] '''Required tasks:''' * Read Diez, Çetinkaya-Rundel, and Barr: §2.1-2 (Numerical and categorical data). * The R tutorial webcast and RMarkdown tutorial that I've put together including: ** COM520 R Tutorial #1: What is R, RStudio, etc? [[https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/r_tutorials/com520-r_tutorial-01.html HTML], [https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/r_tutorials/com520-r_tutorial-01.rmd RMarkdown], [https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/r_tutorials/com520-r_tutorial-01.pdf PDF]] ** COM520 R Tutorial #2: Intro to R [[https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/r_tutorials/com520-r_tutorial-02.html HTML], [https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/r_tutorials/com520-r_tutorial-02.rmd RMarkdown], [https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/r_tutorials/com520-r_tutorial-02.pdf PDF]] '''Recommended tasks:''' * Watch [https://www.youtube.com/playlist?list=PLkIselvEzpM6pZ76FD3NoCvvgkj_p-dE8 Lecture materials for §2.1 and §2.2 (Videos 6-7 in the playlist)]. * Watch COM520 [https://uw.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=62fcbfcf-0b7e-4c6c-bf1a-aca500828992 R Tutorial #1 Screencast] on Panopto * Watch COM520 [https://uw.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=4d383a06-3df5-4607-9b16-aca5008289be R Tutorial #2 Screencast] on Panopto * If you want additional material that will provide an introductions to R, these are great resources: ** Modern Dive [https://moderndive.netlify.app/index.html Statistical inference via data science] Chapter 1: [https://moderndive.netlify.app/1-getting-started.html Getting started with R]. ** [https://rladiessydney.org/courses/ryouwithme/ RYouWithMe] course [https://rladiessydney.org/courses/ryouwithme/01-basicbasics-0/ "Basic basics" 1 & 2] (and maybe 3 if you're feeling ambitious) ** Verzani §1 (Getting started) ** Healy §2 (Get started) '''Homework:''' * Complete [[/Problem set 3]] (OpenIntro questions & programming challenges) * Problem set 3 worked solutions [[https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/worked_solutions/worked_solutions-pset_03.html HTML], [https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/worked_solutions/worked_solutions-pset_03.rmd RMarkdown], [https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/worked_solutions/worked_solutions-pset_03.pdf PDF]] === Day 4: Wednesday January 13: Applied data manipulation === '''Class material:''' * [[/Day 4 session plan]] '''Required tasks:''' * COM520 R Tutorial #3 Intro to R [[https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/r_tutorials/com520-r_tutorial-03.html HTML], [https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/r_tutorials/com520-r_tutorial-03.rmd RMarkdown], [https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/r_tutorials/com520-r_tutorial-03.pdf PDF], [https://uw.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=c9ce270f-9192-4034-bcf7-acae0073b049 Screencast]] '''Recommended tasks:''' * Additional material from any of the recommended R learning resources suggested last week or elsewhere in the syllabus. In particular, you may find the ModernDive, RYouWithMe, Healy, and/or Wickham and Grolemund resources valuable. '''Homework:''' * Complete [[/Problem set 4]] (programming challenges and statistical questions) * Problem set 4 worked solutions [[https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/worked_solutions/worked_solutions-pset_04.html HTML], [https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/worked_solutions/worked_solutions-pset_04.rmd RMarkdown], [https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/worked_solutions/worked_solutions-pset_04.pdf PDF]] <!--- '''Resources''' * [https://science.sciencemag.org/content/187/4175/398 UCB admissions paper] * [https://openpolicing.stanford.edu Stanford OpenPolicing Project] ---> === NO CLASS: Monday January 18: Martin Luther King Jr Day === === Day 5: Wednesday Janaury 20: Probability and R fundamentals === '''Required tasks:''' * Read Diez, Çetinkaya-Rundel, and Barr: §3 (Probability). * COM520 R Tutorial #4: Additional R fundamentals [[https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/r_tutorials/com520-r_tutorial-04.html HTML], [https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/r_tutorials/com520-r_tutorial-04.rmd RMarkdown], [https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/r_tutorials/com520-r_tutorial-04.pdf PDF]] '''Recommended tasks:''' * Watch [https://www.youtube.com/watch?list=PLkIselvEzpM5EgoOajhw83Ax_FktnlD6n&v=rG-SLQ2uF8U Probability introduction] and [https://www.youtube.com/watch?v=HxEz4ZHUY5Y&list=PLkIselvEzpM5EgoOajhw83Ax_FktnlD6n&index=2 Probability trees] OpenIntro lectures (just videos 1 and 2 in the playlist). * Watch COM520 [https://uw.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=25a9cdb2-0dcd-493e-b031-acb3004215b5 R Tutorial #4.1 Screencast] on Panopto * Watch COM520 [https://uw.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=02bae034-911a-4ddf-85b3-acb300886150 R Tutorial #4.2 Screencast] on Panopto '''Resources''' * [https://seeing-theory.brown.edu/index.html#secondPage Seeing Theory §1-2 (Basic Probability and Compound Probability)] '''Homework:''' * Complete [[/Problem set 5]] (OpenIntro excercises & programming challenges) * Problem set 5 worked solutions [[https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/worked_solutions/worked_solutions-pset_05-pt1.html HTML], [https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/worked_solutions/worked_solutions-pset_05-pt1.rmd RMarkdown], [https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/worked_solutions/worked_solutions-pset_05-pt1.pdf PDF]] === Day 6: Monday January 25: Distributions === <!-- '''Class material:''' * [[/Day 6 session plan]] --> '''Required tasks:''' * Read Diez, Çetinkaya-Rundel, and Barr: §4.1-3 (Normal and binomial distributions). '''Recommended tasks:''' * Watch [https://www.youtube.com/watch?list=PLkIselvEzpM6V9h55s0l9Kzivih9BUWeW&v=S_p5D-YXLS4 normal and binomial distributions] OpenIntro lectures (videos 1-3 in the playlist) * [https://seeing-theory.brown.edu/index.html#secondPage/chapter3 Seeing Theory §3 (Probability distributions)] '''Homework:''' * Go back and complete any questions from [[/Problem set 5]] that you were not able to get last time. * Complete '''Problem set 6''': exercises from OpenIntro §4: 4.4, 4.6, 4.15, 4.22 === Day 7: Wednesday January 27: Descriptive analysis and visualization === <!-- '''Class material:''' * [[/Day 7 session plan]] --> '''Required tasks:''' * COM520 R Tutorial #5: Visualization using ''ggplot2'' [[https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/r_tutorials/com520-r_tutorial-05.html HTML], [https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/r_tutorials/com520-r_tutorial-05.rmd RMarkdown], [https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/r_tutorials/com520-r_tutorial-05.pdf PDF]] '''Homework:''' * Complete [[/Problem set 7]] === Day 8: Monday February 1: Foundations for inference === <!--'''Class material:''' * [[/Day 8 session plan]] --> '''Required tasks:''' * Read Diez, Çetinkaya-Rundel, and Barr: §5 (Foundations for inference). * Complete [https://www.openintro.org/book/stat/why05/ Why .05?] OpenIntro video/exercise. '''Recommended tasks:''' * Read Kelly M., [https://rss.onlinelibrary.wiley.com/doi/pdf/10.1111/j.1740-9713.2013.00693.x Emily Dickinson and monkeys on the stair Or: What is the significance of the 5% significance level?] ''Significance'' 10:5. 2013. * [https://seeing-theory.brown.edu/index.html#secondPage/chapter4 Seeing Theory §4 (Frequentist Inference)] * Watch [https://www.youtube.com/watch?v=oLW_uzkPZGA&list=PLkIselvEzpM4SHQojH116fYAQJLaN_4Xo foundations for inference] (videos 1-3 in the playlist) OpenIntro lectures. '''Homework:''' * Complete '''Problem set 8''': exercises from OpenIntro §5: 5.4, 5.8, 5.10, 5.17, 5.30, 5.35, 5.36 === Day 9: Wednesday February 3: Reinforced foundations for inference === '''Required tasks:''' * COM520 R Tutorial #6: Distributions in R and more [[https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/r_tutorials/com520-r_tutorial-06.html HTML], [https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/r_tutorials/com520-r_tutorial-06.rmd RMarkdown], [https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/r_tutorials/com520-r_tutorial-06.pdf PDF], [https://uw.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=4d5cdd94-0e2a-4c21-b912-acc301383f9b Screencast]] * Read Reinhart, §1. {{avail-uw|1=https://alliance-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=CP71226818410001451&context=L&vid=UW&lang=en_US&search_scope=all&adaptor=Local%20Search%20Engine&tab=default_tab&query=any,contains,statistics%20done%20wrong}} * Read the following paper (it will be familiar to those of you in COM501): 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. https://doi.org/10.1073/pnas.1320040111. {{avail-uw|https://doi.org/10.1073/pnas.1320040111}} '''Recommended tasks:''' * Check out [https://gallery.shinyapps.io/CLT_mean/ OpenIntro Central limit theorem for means demo]. '''Homework:''' * Complete [[/Problem set 9]] === Day 10: Monday February 8: Inference for categorical data === '''Required tasks:''' * Read Diez, Çetinkaya-Rundel, and Barr: §6 (Inference for categorical data). '''Recommended tasks:''' * Watch [https://www.youtube.com/watch?list=PLkIselvEzpM5Gn-sHTw1NF0e8IvMxwHDW&v=_iFAZgpWsx0 inference for categorical data] (videos 1-3 in the playlist) OpenIntro lectures. * [https://gallery.shinyapps.io/CLT_prop/ OpenIntro Central limit theorem for proportions demo]. '''Homework:''' * Complete '''Problem set 10''': exercises from OpenIntro §6: 6.10, 6.16, 6.22, 6.30, 6.40 (just parts a and b; part c gets tedious) === Day 11: Wednesday February 10: Applied inference for categorical data === '''Required tasks:''' * COM520 R Tutorial #7: Categorical data [[https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/r_tutorials/com520-r_tutorial-07.html HTML], [https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/r_tutorials/com520-r_tutorial-07.rmd RMarkdown], [https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/r_tutorials/com520-r_tutorial-07.pdf PDF]] * Read Reinhart, §4 and §5 (both are quite short). * Skim the following (all are referenced in the problem set) ** Aronow PM, Karlan D, Pinson LE. (2018). The effect of images of Michelle Obama’s face on trick-or-treaters’ dietary choices: A randomized control trial. ''PLoS ONE'' 13(1): e0189693. https://doi.org/10.1371/journal.pone.0189693. {{avail-free|https://doi.org/10.1371/journal.pone.0189693}} ** 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. {{avail-free|https://mako.cc/academic/buechley_hill_DIS_10.pdf}} '''Homework:''' * Complete [[/Problem set 11]] === NO CLASS: Monday February 15: Presidents' Day === === Day 12: Wednesday February 17: Inference for numerical data (t-tests and ANOVA) === <!--'''Class material:''' This is a combo of two days, basically... * [[/Day 12 session plan]] --> '''Required tasks:''' * Read Diez, Çetinkaya-Rundel, and Barr: §7.1-5 (Inference for numerical data: differences of means; power calculations, ANOVA, and multiple comparisons). '''Recommended tasks:''' * [https://www.openintro.org/go/?id=stat_better_understand_anova&referrer=/book/os/index.php OpenIntro supplement on ANOVA calculations] (particularly useful if you think you'll be doing more ANOVAs). * Watch [https://www.youtube.com/watch?list=PLkIselvEzpM5G3IO1tzQ-DUThsJKQzQCD&v=uVEj2uBJfq0 inference for numerical data] (videos 1-8 in the playlist) OpenIntro lectures (and featuring one of the textbook authors!). * COM520 R Tutorial #8: t-tests and ANOVA [[https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/r_tutorials/com520-r_tutorial-08.html HTML], [https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/r_tutorials/com520-r_tutorial-08.rmd RMarkdown], [https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/r_tutorials/com520-r_tutorial-08.pdf PDF], [https://uw.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=70b906b1-88fc-475c-9541-acd100247d13 Screencast]] '''Homework:''' * Complete [[/Problem set 12]] === Day 13: Monday February 22: Linear regression === <!-- '''Class material:''' * [[/Day 13 session plan]] --> '''Required tasks:''' * Read Diez, Çetinkaya-Rundel, and Barr: §8 (Linear regression). * Read [https://www.openintro.org/go/?id=stat_more_inference_for_linear_regression&referrer=/book/os/index.php More inference for linear regression] (OpenIntro supplement). '''Recommended tasks:''' * Watch [https://www.youtube.com/playlist?list=PLkIselvEzpM63ikRfN41DNIhSgzboELOM linear regression] (videos 1-4 in the playlist) OpenIntro lectures. * Read [https://seeing-theory.brown.edu/index.html#secondPage/chapter6 Seeing Theory §6 (Regression analysis)] '''Homework:''' * Complete [[/Problem set 13]] === Day 14: Wednesday February 24: Applied linear regression === <!-- '''Class material:''' * [[/Day 14 session plan]] --> '''Required tasks:''' * COM520 R Tutorial #9: Linear regression [[https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/r_tutorials/com520-r_tutorial-09.html HTML], [https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/r_tutorials/com520-r_tutorial-09.rmd RMarkdown], [https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/r_tutorials/com520-r_tutorial-09.pdf PDF], [https://uw.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=3fd1ff6d-ba80-46fd-b7e1-acd100247d42 Screencast]] '''Homework:''' * Complete [[/Problem set 14]] === Day 15: Monday March 1: Multiple and logistic regression === <!-- '''Class material:''' * [[/Day 15 session plan]]--> '''Required tasks:''' * Read Diez, Çetinkaya-Rundel, and Barr: §9 (Multiple and logistic regression). (Skim §9.2-9.4) ** '''Disclaimer:''' Mako doesn't like §9.2-9.3, but it should be useful to understand and discuss them, so we'll do that. * Read [https://www.openintro.org/go/?id=stat_interaction_terms&referrer=/book/os/index.php Interaction terms] (OpenIntro supplement). * Read [https://www.openintro.org/go/?id=stat_nonlinear_relationships&referrer=/book/os/index.php Fitting models for non-linear trends] (OpenIntro supplement). '''Recommended tasks:''' * Watch [https://www.youtube.com/playlist?list=PLkIselvEzpM5f1HYzIjFt52SD4izsJ2_I multiple and logistic regression] (videos 1-4 in the playlist) OpenIntro lectures. '''Homework:''' * Complete '''Problem set 16''': exercises from OpenIntro §9: 9.4, 9.13, 9.16, 9.18, '''Required tasks:''' === Day 16: Wednesday March 3: Applied multiple and logistic regression === <!-- '''Class material:''' * [[/Day 16 session plan]] --> '''Required tasks:''' * COM520 R Tutorial #10: Tutorial on interpreting logistic regression in R [[https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/r_tutorials/com520-r_tutorial-10.html HTML], [https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/r_tutorials/com520-r_tutorial-10.rmd RMarkdown], [https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/r_tutorials/com520-r_tutorial-10.pdf PDF]] '''Homework:''' * Complete [[/Problem set 16]] === Day 17: Monday March 8: Consulting Day === We'll forgo meeting as a group. Instead, I will meet one-on-one with each of you to work through challenges you're having with your own projects. * COM520 R Tutorial #11: Bonus material {{forthcoming}} <!-- logistic_regression_interpretation.html --> === Day 18: Wednesday March 10: Final Presentations === <!-- '''Class material:''' * [[/Day 18 session plan]] --> <strike>'''Post your video via this "Discussion" on Canvas]''' {{forthcoming}} — Please view and provide constructive feedback on other's videos! * '''Post videos directly to the "Discussion."''' The Canvas text editor has an option to upload/record a video. That's what you want. * '''Please remember not to over-work/think this.''' I mentioned this in class, but just to reiterate, the focus of this assignment should not be your video editing skills. Please do what you can to record and convey your ideas clearly without devoting insane hours to creating the perfect video. * '''Some resources for recording presentations:''' There are a bunch of ways you might record/share your video. Some ideas include using the embedded media recorder in Canvas (!) that can record with with your webcam (maybe attach a few visuals to accompany this?); recording a "meeting" with yourself in Zoom; and "Panopto," a piece of high-end video recording, sharing, and editing software that UW licenses for campus use. Here are some pointers: ** You should be able to use your UW zoom account to create a zoom meeting, record your meeting (in which you deliver your presentation and share your screen with any visuals), and then share a link to the recording via the "Recordings" item in the left-hand menu of your Zoom account page. ** If nothing works, please get in touch. </strike> Since the class is small, we'll meet and give presentations during class. Everyone should plan to give a ~15 minutes conference style presentation.
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