Editing Statistics and Statistical Programming (Fall 2020) (section)

== Schedule (with all the details) ==

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 (inclusive) of chapter n.

=== Week 1 (9/17) ===
==== September 17: Intro and setup ====

;[[Statistics_and_Statistical_Programming_(Fall_2020)/w01_session_plan|Session plan]]

<blockquote>''Note: Aaron doesn't actually expect you to complete these before class on September 17''</blockquote>

'''Required'''
* Read this syllabus, discuss any questions/concerns with the teaching team.
* Complete [https://apps3.cehd.umn.edu/artist/user/scale_select.html pre-course assessment of statistical concepts] (access code TBA via email). Estimated time to do this is 30-40 minutes. '''Submission deadline: September 18, 11:00pm Chicago time'''
* 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 (Zoom, Discord, Canvas, this wiki, R, RStudio). Discord invites will be sent via email.
* Complete [https://wiki.communitydata.science/Statistics_and_Statistical_Programming_(Fall_2020)/pset0 problem set #0] 

'''Recommended'''
* Work through one (or more) introduction(s) to R and Rstudio so that you can complete problem set 0. Here are several suggestions:
** '''From Aaron:''' The [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w01-R_tutorial.html Week 01 R tutorial] (you should also download the [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w01-R_tutorial.rmd .rmd version of the tutorial] that you can open and read/edit in RStudio). These are accompanied by the R and Rstudio intro screencasts ([https://communitydata.cc/~ads/teaching/2019/stats/screencasts/w01-s01-intro.webm Part 1] and [https://communitydata.cc/~ads/teaching/2019/stats/screencasts/w01-s02-intro.webm Part 2]) Aaron created for the 2019 version of the course. 
** 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).

=== Week 2 (9/22, 9/24) ===
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w02_session_plan|Session plans]]
==== September 22: Data and variables ====
'''Required'''
* Read Diez, Çetinkaya-Rundel, and Barr: §1.1-1.3 (Introduction to data). 
* Watch [https://www.youtube.com/playlist?list=PLkIselvEzpM6pZ76FD3NoCvvgkj_p-dE8 Lecture materials for §1.1-3 (Videos 1-4 in the playlist)].
* Submit, review, and respond to questions or requests for discussion via Discord or some other means.

==== September 24: Numerical and categorical data ====
'''Required'''
* Read Diez, Çetinkaya-Rundel, and Barr: §2.1-2 (Numerical and categorical data). 
* Review [https://www.youtube.com/playlist?list=PLkIselvEzpM6pZ76FD3NoCvvgkj_p-dE8 Lecture materials for §2.1 and §2.2 (Videos 6-7 in the playlist)].
* Complete '''exercises from OpenIntro §2:''' 2.12, 2.13, 2.16, 2.20, 2.23, 2.30 (and remember that solutions to odd-numbered problems are in the book!)
* Submit, review, and respond to questions or requests for discussion via Discord or some other means.

=== Week 3 (9/29, 10/1) ===

;[[Statistics_and_Statistical_Programming_(Fall_2020)/w03_session_plan|Session plans]]

==== September 29: R fundamentals: Import, transform, tidy, and describe data ====
'''Required'''
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset1|problem set #1]] (due Monday, September 28 at 1pm Central)

'''Recommended'''
* [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w03-R_tutorial.html Week 3 R tutorial] (note that you can access .rmd or .pdf versions by replacing the suffix of the URL accordingly).
* 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.
<!---
'''Resources'''
* [https://science.sciencemag.org/content/187/4175/398 UCB admissions paper]
* [https://openpolicing.stanford.edu Stanford OpenPolicing Project]
--->

==== October 1: Probability ====
'''Required'''
* Read Diez, Çetinkaya-Rundel, and Barr: §3 (Probability). 
* 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).
* Complete '''exercises from OpenIntro §3:''' 3.12, 3.15, 3.22, 3.28, 3.34, 3.38

'''Resources'''
* [https://seeing-theory.brown.edu/index.html#secondPage Seeing Theory §1-2 (Basic Probability and Compound Probability)]

=== Week 4 (10/6, 10/8) ===
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w04_session_plan|Session plans]]

==== October 6: Emotional contagion and more advanced R fundamentals: import, tidy, transform, and simulate data; write functions ====
'''Required'''
* Read the paper below as well as the attendant [https://www.pnas.org/content/111/29/10779.1 "Expression of editorial concern"] and [https://www.pnas.org/content/111/29/10779.2 "Correction"] that were subsequently appended to it.
: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]]
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset2|problem set #2]] (due Monday, October 5 at 1pm CT)

'''Recommended'''
* [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w04-R_tutorial.html Week 4 R tutorial] (as usual, also available as .rmd or .pdf)

==== October 8: Distributions ====
'''Required'''
* Read Diez, Çetinkaya-Rundel, and Barr: §4.1-3 (Normal and binomial distributions). 
* Watch [https://www.youtube.com/watch?list=PLkIselvEzpM6V9h55s0l9Kzivih9BUWeW&v=S_p5D-YXLS4 normal and binomial distributions] OpenIntro lectures (videos 1-3 in the playlist).
* Complete '''exercises from OpenIntro §4:''' 4.4, 4.6, 4.15, 4.22

'''Resources'''
* [https://seeing-theory.brown.edu/index.html#secondPage/chapter3 Seeing Theory §3 (Probability distributions)]

==== October 9: [[#Research project plan and dataset identification|Research project plan and dataset identification]] due by 5pm CT ====
*'''Submit via [https://canvas.northwestern.edu/courses/122522/assignments Canvas]''' (due by 5pm CT)

=== Week 5 (10/13, 10/15) ===
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w05_session_plan|Session plans]]
==== October 13: Descriptive analysis and visualization of data ====
'''Required'''
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset3|problem set #3]] (due Monday, October 12 at 1pm CT)

'''Recommended'''
* [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w05-R_tutorial.html Week 5 R tutorial] and [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w05a-R_tutorial.html Week 5 R tutorial supplement] (both, as usual, also available as .rmd or .pdf).

==== October 15: Foundations for (frequentist) inference ====
'''Required'''
* Read Diez, Çetinkaya-Rundel, and Barr: §5 (Foundations for inference). 
* Watch [https://www.youtube.com/watch?v=oLW_uzkPZGA&list=PLkIselvEzpM4SHQojH116fYAQJLaN_4Xo foundations for inference] (videos 1-3 in the playlist) OpenIntro lectures.
* Complete [https://www.openintro.org/book/stat/why05/ Why .05?] OpenIntro video/exercise.
* Complete '''exercises from OpenIntro §5:''' 5.4, 5.8, 5.10, 5.17, 5.30, 5.35, 5.36

'''Resources'''
* 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)]

=== Week 6 (10/20, 10/22) ===
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w06_session_plan|Session plans]]
==== October 20: Reinforced foundations for inference ====
'''Required'''
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset4|problem set #4]]  
* Read Reinhart, §1.
* Revisit the Kramer et al. (2014) paper we read a few weeks ago:
: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]]  

==== October 22: Inference for categorical data ====
'''Required'''
* Read Diez, Çetinkaya-Rundel, and Barr: §6 (Inference for categorical data). 
* Watch [https://www.youtube.com/watch?list=PLkIselvEzpM5Gn-sHTw1NF0e8IvMxwHDW&v=_iFAZgpWsx0 inference for categorical data] (videos 1-3 in the playlist) OpenIntro lectures.
* Complete '''exercises from OpenIntro §6:''' 6.10, 6.16, 6.22, 6.30, 6.40 (just parts a and b; part c gets tedious)

'''Resources'''
* [https://gallery.shinyapps.io/CLT_prop/ OpenIntro Central limit theorem for proportions demo].

=== Week 7 (10/27, 10/29) ===
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w07_session_plan|Session plans]]
==== October 27: Applied inference for categorical data ====
'''Required'''
* 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 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. [[https://mako.cc/academic/buechley_hill_DIS_10.pdf PDF available on Hill's personal website]]
** 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]]
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset5|problem set #5]]
'''Resources'''
* [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w06-R_tutorial.html Week 06 R tutorial] (it's very short!)

==== October 29: Inference for numerical data (part 1) ====
'''Required'''
* Read Diez, Çetinkaya-Rundel, and Barr: §7.1-3 (Inference for numerical data: differences of means). 
* Watch [https://www.youtube.com/watch?list=PLkIselvEzpM5G3IO1tzQ-DUThsJKQzQCD&v=uVEj2uBJfq0 inference for numerical data] (videos 1-4 in the playlist) OpenIntro lectures (and featuring one of the textbook authors!).
* Complete '''exercises from OpenIntro §7:''' 7.12, 7.24, 7.26

'''Resources'''
* [https://gallery.shinyapps.io/CLT_mean/ OpenIntro Central limit theorem for means demo].

==== October 30: [[#Research project planning document|Research project planning document]] due 5pm CT====
* Submit via [https://canvas.northwestern.edu/courses/122522/assignments/787297 Canvas] (due by 5pm CT)

=== Week 8 (11/3, 11/5) ===
==== November 3: U.S. election day (no class meeting) ====

==== November 4: Interactive self-assessment due ====
* Please submit results [https://canvas.northwestern.edu/courses/122522/assignments/799630 (via Canvas)] from the [https://communitydata.science/~ads/teaching/2020/stats/assessment/interactive_assessment.rmd interactive self-assessment] by 5pm CT.

==== November 5: Inference for numerical data (part 2) ====
'''Required'''
* Read Diez, Çetinkaya-Rundel, and Barr: §7.4-5 (Inference for numerical data: power calculations, ANOVA, and multiple comparisons). 
* Watch [https://www.youtube.com/watch?list=PLkIselvEzpM5G3IO1tzQ-DUThsJKQzQCD&v=uVEj2uBJfq0 inference for numerical data] (videos 4-8 in the playlist) OpenIntro lectures (and featuring one of the textbook authors!).
* Complete '''exercises from OpenIntro §7:''' 7.42, 7.44, 7.46

'''Resources'''
* [https://www.openintro.org/go/?id=stat_better_understand_anova&referrer=/book/os/index.php OpenIntro supplement on ANOVA calculations] (useful if you think you'll be doing more ANOVAs).

=== Week 9 (11/10, 11/12) ===
==== November 10: Applied inference for numerical data (t-tests, power analysis, ANOVA) ====
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w09_session_plan|Session plans]]

'''Required'''
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset6|problem set #6]]

'''Resources'''
* [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w09-R_tutorial.html Week 09 R tutorial]

==== November 12: Linear regression ====
'''Required'''
* Read Diez, Çetinkaya-Rundel, and Barr: §8 (Linear regression).
* Watch [https://www.youtube.com/playlist?list=PLkIselvEzpM63ikRfN41DNIhSgzboELOM linear regression] (videos 1-4 in the playlist) OpenIntro lectures.
* 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).
* Complete '''exercises from OpenIntro §8:''' 8.6, 8.36, 8.40, 8.44
* Complete '''exercises from OpenIntro supplement:''' 4 and 5 (answers provided in the supplement).
 
'''Resources'''
* [https://seeing-theory.brown.edu/index.html#secondPage/chapter6 Seeing Theory §6 (Regression analysis)]

=== Week 10 (11/17, 11/19) ===
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w10_session_plan|Session plans]]
==== November 17: Applied linear regression ====
'''Required'''
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset7|Problem set #7]]

'''Resources'''
* [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w10-R_tutorial.html Week 10 R tutorial]
==== November 19: Multiple and logistic regression ====
'''Required'''
* Read Diez, Çetinkaya-Rundel, and Barr: §9 (Multiple and logistic regression). (Skim §9.2-9.4) 
** '''Disclaimer:''' Aaron doesn't like §9.2-9.3, but it should be useful to understand and discuss them, so we'll do that. 
* Watch [https://www.youtube.com/playlist?list=PLkIselvEzpM5f1HYzIjFt52SD4izsJ2_I multiple and logistic regression] (videos 1-4 in the playlist) OpenIntro lectures.
* 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).
* Complete '''exercises from OpenIntro §9:''' 9.4, 9.13, 9.16, 9.18, 

'''Resources'''

=== Week 11 (11/24) ===
==== November 24: Applied multiple and logistic regression ====
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w11_session_plan|Session plans]]
'''Required'''
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset8|Problem set #8]]
'''Resources'''
* Mako Hill created (and Aaron updated) a brief tutorial on [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/logistic_regression_interpretation.html interpreting logistic regression coefficients with examples in R]

=== Week 12+ ===

==== December 3: [[#Research project presentation|Research project presentation]] due by 5pm CT ====
'''[https://canvas.northwestern.edu/courses/122522/discussion_topics/856868 Post your video via this "Discussion" on Canvas]'''. 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 NU licenses for campus use. Here are some pointers:
** NU has a "digital learning resource hub" which provides some [https://digitallearning.northwestern.edu/resource-hub#for-students resources for students]. The first item in that list has pointers for recording yourself and posting to Canvas and includes info about the Canvas media recorder and Panopto.
** You should be able to use your NU 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 [https://northwestern.zoom.us/ https://northwestern.zoom.us/] account page.
** If nothing works, please get in touch.

==== December 4: Post-course assessment of statistical concepts due by 11pm CT ====
Complete [https://apps3.cehd.umn.edu/artist/user/scale_select.html post-course assessment] (access code TBA VIA email). Submission deadline: December 4, 11:00pm Chicago time.

==== December 10: [[#Research project paper|Research project paper]] due by 5pm CT ====
'''[https://canvas.northwestern.edu/courses/122522/assignments/812317 Submit your paper, data, and code via Canvas].'''