Editing Statistics and Statistical Programming (Fall 2020)

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:Also usually available via chat during "business hours."
:Also usually available via chat during "business hours."


;'''Teaching Assistant:''' [http://nickmvincent.com Nick Vincent] ([mailto:nickvincent@u.northwestern.edu nickvincent@u.northwestern.edu])
:'''Teaching Assistant:''' [http://nickmvincent.com Nick Vincent] ([mailto:nickvincent@u.northwestern.edu nickvincent@u.northwestern.edu])
:Office Hours: Monday 10am-12pm and by appointment. I'll try to respond to any asynchronous questions in a timely fashion during "business hours" (9a-5p Central Time), and will also have OH by appointment. I'll respond best to email (above), but am also happy to use Discord for quicker back-and-forth.
::Office Hours: Monday 10am-12pm and by appointment. I'll try to respond to any asynchronous questions in a timely fashion during "business hours" (9a-5p Central Time), and will also have OH by appointment. I'll respond best to email (above), but am also happy to use Discord for quicker back-and-forth.
:I am happy to try out alternative communication software for OH!
::I am happy to try out alternative communication software for OH!


<br>
<br>
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===== Notes on finding a dataset =====
''' Notes on finding a dataset '''


In order to complete your final project, you will each need a dataset. If you already have a dataset for the project you plan to conduct, great! If not, fear not! There are many datasets to draw from. Some ideas are below (please suggest others, provide updated links, or report problems). The teaching team will also be available to help you brainstorm/find resources if needed:
In order to complete your final project, you will each need a dataset. If you already have a dataset for the project you plan to conduct, great! If not, fear not! There are many datasets to draw from. Some ideas are below (please suggest others, provide updated links, or report problems). The teaching team will also be available to help you brainstorm/find resources if needed:
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* Use the [http://scientificdata.isa-explorer.org/index.html ISA Explorer] to find datasets. Keep in mind the large majority of datasets it will search are drawn from the natural sciences.
* Use the [http://scientificdata.isa-explorer.org/index.html ISA Explorer] to find datasets. Keep in mind the large majority of datasets it will search are drawn from the natural sciences.
* The City of Chicago has one of the best [https://data.cityofchicago.org/ data portal sites] of any municipality in the U.S. (and better than many federal agencies). There are also numerous administrative datasets released by other public entities (try searching!) that you might find inspiring.
* The City of Chicago has one of the best [https://data.cityofchicago.org/ data portal sites] of any municipality in the U.S. (and better than many federal agencies). There are also numerous administrative datasets released by other public entities (try searching!) that you might find inspiring.
<!---
* <TODO fix/update accordingly> Set up a meeting with Jennifer Muilenburg — Data Curriculum and Communications Librarian who runs [https://www.lib.washington.edu/digitalscholarship/services/data research data services at the UW libraries]. Her email is: libdata@uw.edu I've have talked to her about this course and she is excited about meeting with you to help.
-->
* [http://fivethirtyeight.com FiveThirtyEight.com] has published a [https://cran.r-project.org/web/packages/fivethirtyeight/vignettes/fivethirtyeight.html GitHub repository and an R package] with pre-processed and cleaned versions of many of the datasets they use for articles published on their website.
* [http://fivethirtyeight.com FiveThirtyEight.com] has published a [https://cran.r-project.org/web/packages/fivethirtyeight/vignettes/fivethirtyeight.html GitHub repository and an R package] with pre-processed and cleaned versions of many of the datasets they use for articles published on their website.
* If you interested in studying online communities, there are some great resources for accessing data from Reddit, Wikipedia, and StackExchange. See [https://files.pushshift.io/reddit/ pushshift] for dumps of Reddit data, [https://meta.wikimedia.org/wiki/Research:Data here] for an overview of Wikipedia's data resources, and [https://data.stackexchange.com/ Stack Exchange's data portal].
* If you interested in studying online communities, there are some great resources for accessing data from Reddit, Wikipedia, and StackExchange. See [https://files.pushshift.io/reddit/ pushshift] for dumps of Reddit data, [https://meta.wikimedia.org/wiki/Research:Data here] for an overview of Wikipedia's data resources, and [https://data.stackexchange.com/ Stack Exchange's data portal].
* The NY Times is publishing a [https://github.com/nytimes/covid-19-data COVID-19 data repository] that includes county-level metrics for deaths, mask usage, and other pandemic-related data. The release a lot of it as frequently updated .csv files and the repository includes documentation of the measurements, data collection details, and more.
* The Community Data Science Collective and colleagues have created a [[COVID-19_Digital_Observatory| COVID-19 digital observatory]] (hosted in part right here on this wiki!) that publishes a bunch of pandemic-related data as csv and json files.
* The [https://openpolicing.stanford.edu Stanford Open Policing project] has published a huge archive of policing data related mostly to traffic stops in states and many cities of the U.S. We'll use at least one of these files for a problem set.


==== Research project planning document ====
==== Research project planning document ====
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==== Research project paper ====
==== Research project paper ====


;Paper due date: December 10, 2020, 5pm CT
;Paper due date: December 8, 2020, 5pm CT
;Maximum length: 6000 words (~20 pages)
;Maximum length: 6000 words (~20 pages)


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I do not have strong preferences about the style or formatting guidelines you follow for the paper and its bibliography. However, ''your paper must follow a standard format'' (e.g., [https://cscw.acm.org/2019/submit-papers.html ACM SIGCHI CSCW format] or [https://www.apastyle.org/index APA 6th edition] ([https://templates.office.com/en-us/APA-style-report-6th-edition-TM03982351 Word] and [https://www.overleaf.com/latex/templates/sample-apa-paper/fswjbwygndyq LaTeX] templates)) that is applicable for a peer-reviewed journal or conference proceedings in which you might aim to publish the work (they all have formatting or submission guidelines published online and you should follow them). This includes the references. I also strongly recommend that you use reference management software like Zotero to handle your bibliographic sources.
I do not have strong preferences about the style or formatting guidelines you follow for the paper and its bibliography. However, ''your paper must follow a standard format'' (e.g., [https://cscw.acm.org/2019/submit-papers.html ACM SIGCHI CSCW format] or [https://www.apastyle.org/index APA 6th edition] ([https://templates.office.com/en-us/APA-style-report-6th-edition-TM03982351 Word] and [https://www.overleaf.com/latex/templates/sample-apa-paper/fswjbwygndyq LaTeX] templates)) that is applicable for a peer-reviewed journal or conference proceedings in which you might aim to publish the work (they all have formatting or submission guidelines published online and you should follow them). This includes the references. I also strongly recommend that you use reference management software like Zotero to handle your bibliographic sources.


==== Human subjects research, IRB, and ethics ====
==== Human subjects research, IRB, and ethics ====
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'''Recommended'''
'''Recommended'''
* Work through one (or more) introduction(s) to R and Rstudio so that you can complete problem set 0. Here are several suggestions:
* 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.  
** '''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].
** 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).
** [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).
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=== Week 2 (9/22, 9/24) ===
=== Week 2 (9/22, 9/24) ===
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w02_session_plan|Session plans]]
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w02_session_plan|Session plan]]
==== September 22: Data and variables ====
==== September 22: Data and variables ====
'''Required'''
'''Required'''
* Read Diez, Çetinkaya-Rundel, and Barr: §1.1-1.3 (Introduction to data).  
* 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)].
* Watch [https://www.youtube.com/playlist?list=PLkIselvEzpM6pZ76FD3NoCvvgkj_p-dE8 Lecture materials for §1.1-3 (Videos 1-4 in the playlist)].
* Complete '''exercises from OpenIntro §1:''' 1.6, 1.9, 1.10, 1.16, 1.21, 1.40, 1.42, 1.43 (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.
* Submit, review, and respond to questions or requests for discussion via Discord or some other means.


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=== Week 3 (9/29, 10/1) ===
=== Week 3 (9/29, 10/1) ===
 
==== September 29: Import, clean, transform, and describe data ====
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w03_session_plan|Session plans]]
 
==== September 29: R fundamentals: Import, transform, tidy, and describe data ====
'''Required'''
'''Required'''
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset1|problem set #1]] (due Monday, September 28 at 1pm Central)
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset1|problem set #1]] (due Monday, September 28 at 1pm Central)
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'''Recommended'''
'''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).
* [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'''
'''Resources'''
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=== Week 4 (10/6, 10/8) ===
=== Week 4 (10/6, 10/8) ===
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w04_session_plan|Session plans]]
==== October 6: More advanced R fundamentals (import, tidy, transform, and simulate data; write functions) ====
 
==== October 6: Emotional contagion and more advanced R fundamentals: import, tidy, transform, and simulate data; write functions ====
'''Required'''
'''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.
* Complete problem set #2 (due Monday, October 5 at 1pm CT)
: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]]
<!--- add empirical paper here!!! --->
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset2|problem set #2]] (due Monday, October 5 at 1pm CT)


'''Recommended'''
'''Resources'''
* [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 ====
==== October 8: Distributions ====
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=== Week 5 (10/13, 10/15) ===
=== Week 5 (10/13, 10/15) ===
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w05_session_plan|Session plans]]
==== October 13: <Topic> ====
==== October 13: Descriptive analysis and visualization of data ====
'''Required'''
'''Required'''
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset3|problem set #3]] (due Monday, October 12 at 1pm CT)
* Complete problem set #3


'''Recommended'''
'''Resources'''
* [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 ====
==== October 15: Foundations for (frequentist) inference ====
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* Watch [https://www.youtube.com/watch?v=oLW_uzkPZGA&list=PLkIselvEzpM4SHQojH116fYAQJLaN_4Xo foundations for inference] (videos 1-3 in the playlist) OpenIntro lectures.
* 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 [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
* Complete '''exercises from OpenIntro §5:''''


'''Resources'''
'''Resources'''
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=== Week 6 (10/20, 10/22) ===
=== Week 6 (10/20, 10/22) ===
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w06_session_plan|Session plans]]
==== October 20: <Topic> ====
==== October 20: Reinforced foundations for inference ====
'''Required'''
'''Required'''
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset4|problem set #4]] 
* Complete problem set #4
* Read Reinhart, §1.
 
* Revisit the Kramer et al. (2014) paper we read a few weeks ago:
'''Resources'''
: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 ====
==== October 22: Inference for categorical data ====
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* Read Diez, Çetinkaya-Rundel, and Barr: §6 (Inference for categorical data).  
* 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.
* 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)
* Complete '''exercises from OpenIntro §6:''''


'''Resources'''
'''Resources'''
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=== Week 7 (10/27, 10/29) ===
=== Week 7 (10/27, 10/29) ===
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w07_session_plan|Session plans]]
==== October 27: <Topics> ====
==== October 27: Applied inference for categorical data ====
'''Required'''
'''Required'''
* Read Reinhart, §4 and §5 (both are quite short).
* Complete problem set #5
* 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'''
'''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) ====
==== October 29: Inference for numerical data (part 1) ====
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* Read Diez, Çetinkaya-Rundel, and Barr: §7.1-3 (Inference for numerical data: differences of means).  
* 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!).
* 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
* Complete '''exercises from OpenIntro §7:''''


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


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


=== Week 8 (11/3, 11/5) ===
=== Week 8 (11/3, 11/5) ===
==== November 3: U.S. election day (no class meeting) ====
==== November 3: Self-assessment exercise (no class meeting) ====
 
'''Election Day (U.S.): No class meeting today'''
==== 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) ====
==== November 5: Inference for numerical data (part 2) ====
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* Read Diez, Çetinkaya-Rundel, and Barr: §7.4-5 (Inference for numerical data: power calculations, ANOVA, and multiple comparisons).  
* 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!).
* 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
* Complete '''exercises from OpenIntro §7:''''


'''Resources'''
'''Resources'''
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=== Week 9 (11/10, 11/12) ===
=== Week 9 (11/10, 11/12) ===
==== November 10: Applied inference for numerical data (t-tests, power analysis, ANOVA) ====
==== November 10: <Topic> ====
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w09_session_plan|Session plans]]
 
'''Required'''
'''Required'''
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset6|problem set #6]]
* Complete problem set #6


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


==== November 12: Linear regression ====
==== November 12: Linear regression ====
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* Watch [https://www.youtube.com/playlist?list=PLkIselvEzpM63ikRfN41DNIhSgzboELOM linear regression] (videos 1-4 in the playlist) OpenIntro lectures.
* 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).
* 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 §8:''''
* Complete '''exercises from OpenIntro supplement:''' 4 and 5 (answers provided in the supplement).
* Complete '''exercises from OpenIntro supplement:''''
   
   
'''Resources'''
'''Resources'''
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=== Week 10 (11/17, 11/19) ===
=== Week 10 (11/17, 11/19) ===
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w10_session_plan|Session plans]]
==== November 17: <Topic> ====
==== November 17: Applied linear regression ====
'''Required'''
'''Required'''
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset7|Problem set #7]]
* Complete Problem set #7


'''Resources'''
'''Resources'''
* [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w10-R_tutorial.html Week 10 R tutorial]
 
==== November 19: Multiple and logistic regression ====
==== November 19: Multiple and logistic regression ====
'''Required'''
'''Required'''
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* 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_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).
* 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,
* Complete '''exercises from OpenIntro §9:''''
* Complete '''exercises from OpenIntro supplements:''''


'''Resources'''
'''Resources'''


=== Week 11 (11/24) ===
=== Week 11 (11/24) ===
==== November 24: Applied multiple and logistic regression ====
==== November 24: <Topic> and assessment ====
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w11_session_plan|Session plans]]
'''Required'''
'''Required'''
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset8|Problem set #8]]
* Complete Problem set #8
* Complete [https://apps3.cehd.umn.edu/artist/user/scale_select.html post-course assessment of statistical concepts] (access code TBA VIA email). '''Submission deadline: December 1, 11:00pm Chicago time'''
'''Resources'''
'''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]
* Mako Hill created an example of [https://communitydata.science/~mako/2017-COM521/logistic_regression_interpretation.html interpreting logistic regression coefficients with examples in R]


=== Week 12+ ===
=== Week 12+ ===
==== December 3: [[#Research project presentation|Research project presentation]] due by 5pm CT ====
==== 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 ====
==== 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].'''


== Credit and Notes ==
== Credit and Notes ==


This syllabus has, in ways that should be obvious, borrowed and built on the [https://www.openintro.org/stat/index.php OpenInto Statistics curriculum]. Most aspects of this course design extend Benjamin Mako Hill's [[Statistics_and_Statistical_Programming_(Winter_2017)|COM 521 class]] from the University of Washington as well as a [[Statistics_and_Statistical_Programming_(Spring_2019)|prior iteration of the same course]] offered at Northwestern in Spring 2019.
This syllabus has, in ways that should be obvious, borrowed and built on the [https://www.openintro.org/stat/index.php OpenInto Statistics curriculum]. Most aspects of this course design extend Benjamin Mako Hill's [[Statistics_and_Statistical_Programming_(Winter_2017)|COM 521 class]] from the University of Washington as well as a [[Statistics_and_Statistical_Programming_(Spring_2019)|prior iteration of the same course]] offered at Northwestern in Spring 2019.
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