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]) | |||
:Office Hours: | ::Office Hours: 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 also try to schedule some fixed time during which I'll hang out on a video call, hours TBA. | ||
:I am happy to try out alternative communication software for OH! | ::I'll likely use whatever conference we use for class sessions, but am happy to try out alternative communication software for OH! | ||
<br> | <br> | ||
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A couple of other notes about the synchronous course meetings: | A couple of other notes about the synchronous course meetings: | ||
* The teaching team will do our best to notice and respond to any questions or comments that come up via Discord or Zoom during the class. Please do what you can to support these efforts. | * The teaching team will do our best to notice and respond to any questions or comments that come up via Discord or Zoom during the class. Please do what you can to support these efforts. | ||
* You might want to create/acquire something like [https://www.mccormick.northwestern.edu/news/articles/2020/08/back-to-school-hack-shares-students-handwritten-work-and-teacher-response-in-real-time.html NU Mechanical Engineering Professor Michael Peshkin's homebrew document camera] to facilitate sharing hand-written notes/drawings during class. | * You might want to create/acquire something like [https://www.mccormick.northwestern.edu/news/articles/2020/08/back-to-school-hack-shares-students-handwritten-work-and-teacher-response-in-real-time.html NU Mechanical Engineering Professor Michael Peshkin's homebrew document camera] to facilitate sharing hand-written notes/drawings during class. | ||
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==== Working groups ==== | ==== Working groups ==== | ||
At the start of the course you will be assigned to a small working group. This will be a group of 2-3 students (exact numbers will depend on the final enrollment) with whom you | At the start of the course you will be assigned to a small working group. This will be a group of 2-3 students (exact numbers will depend on the final enrollment) with whom you will meet outside of class time to discuss, complete, and/or review your weekly assignments (as well as some of the research project assignments). The groups will rotate at least once during the quarter to ensure that you get to work with different members of the class. The main idea is to support collaborative learning, peer support, and accountability. While the specifics of exactly when and how you work with your working group will largely be up to you, the teaching team will provide suggestions and a template that you can use as a starting point. | ||
As a general rule, we strongly encourage you to collaborate with members of your working group on any/all weekly (minor) assignments. You may, if you choose, also collaborate with others in your group or the class on your research project (major) assignments; however, collaborative research projects should be discussed with a member of the teaching team and all research project assignment submissions should include the names of all collaborators. | As a general rule, we strongly encourage you to collaborate with members of your working group on any/all weekly (minor) assignments. You may, if you choose, also collaborate with others in your group or the class on your research project (major) assignments; however, collaborative research projects should be discussed with a member of the teaching team and all research project assignment submissions should include the names of all collaborators. | ||
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* Verzani, John. 2014. ''Using R for Introductory Statistics, Second Edition''. 2 edition. Boca Raton: Chapman and Hall/CRC. ([https://en.wikipedia.org/wiki/Special:BookSources/978-1-4665-9073-1 Various Sources]; [https://www.amazon.com/Using-Introductory-Statistics-Second-Chapman/dp/1466590734/ref=mt_hardcover?_encoding=UTF8&me= Amazon]) | * Verzani, John. 2014. ''Using R for Introductory Statistics, Second Edition''. 2 edition. Boca Raton: Chapman and Hall/CRC. ([https://en.wikipedia.org/wiki/Special:BookSources/978-1-4665-9073-1 Various Sources]; [https://www.amazon.com/Using-Introductory-Statistics-Second-Chapman/dp/1466590734/ref=mt_hardcover?_encoding=UTF8&me= Amazon]) | ||
* Wickham, Hadley. 2010. ''ggplot2: Elegant Graphics for Data Analysis''. 1st ed. 2009. Corr. 3rd printing 2010 edition. New York: Springer. ([https://link.springer.com/book/10.1007%2F978-3-319-24277-4 Springer/NU Libraries]; [https://en.wikipedia.org/wiki/Special:BookSources/978-0-596-80915-7 Various Sources]) | * Wickham, Hadley. 2010. ''ggplot2: Elegant Graphics for Data Analysis''. 1st ed. 2009. Corr. 3rd printing 2010 edition. New York: Springer. ([https://link.springer.com/book/10.1007%2F978-3-319-24277-4 Springer/NU Libraries]; [https://en.wikipedia.org/wiki/Special:BookSources/978-0-596-80915-7 Various Sources]) | ||
There are also some invaluable non-textbook resources: | There are also some invaluable non-textbook resources: | ||
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''I strongly urge you'' to produce a project that will further your academic career outside of the class. There are many ways that this can happen. Some obvious options are to prepare a project that you can submit for publication, use as pilot analysis that you can report in a grant or thesis proposal, and/or use to fulfill a degree requirement. | ''I strongly urge you'' to produce a project that will further your academic career outside of the class. There are many ways that this can happen. Some obvious options are to prepare a project that you can submit for publication, use as pilot analysis that you can report in a grant or thesis proposal, and/or use to fulfill a degree requirement. | ||
There are several intermediate milestones, deliverables, and deadlines to help you accomplish a successful research project. Unless otherwise noted, all deliverables should be submitted via Canvas | There are several intermediate milestones, deliverables, and deadlines to help you accomplish a successful research project. Unless otherwise noted, all deliverables should be submitted via Canvas. | ||
==== | ==== Project plan and dataset identification ==== | ||
;Due date: October 9, 2020 | ;Due date: October 9, 2020 | ||
;Maximum length: 500 words (~1-2 pages) | ;Maximum length: 500 words (~1-2 pages) | ||
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''' 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]. | ||
==== | ==== Project planning document ==== | ||
;Due date: October 30, 2020 | ;Due date: October 30, 2020 | ||
;Suggested length: ~5 pages | ;Suggested length: ~5 pages | ||
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* [https://canvas.northwestern.edu/files/9439379/download?download_frd=1 One provided as an appendix to Gerber and Green's excellent textbook, ''Field Experiments: Design, Analysis, and Interpretation'' (FEDAI)]. It's over-detailed and over-long for the purposes of this assignment, but nevertheless an exemplary approach to planning empirical quantitative research in a careful, intentional way that is worthy of imitation. | * [https://canvas.northwestern.edu/files/9439379/download?download_frd=1 One provided as an appendix to Gerber and Green's excellent textbook, ''Field Experiments: Design, Analysis, and Interpretation'' (FEDAI)]. It's over-detailed and over-long for the purposes of this assignment, but nevertheless an exemplary approach to planning empirical quantitative research in a careful, intentional way that is worthy of imitation. | ||
==== Research | ==== Research paper ==== | ||
;Paper due date: December 8, 2020 | |||
;Paper due date: December | |||
;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. | ||
==== Project presentation ==== | |||
;Presentation due date: December 3, 2020 | |||
;Maximum length: 10 minutes | |||
<!-- TODO revisit old presentations page to update/adapt | |||
[[Statistics_and_Statistical_Programming_(Spring_2019)/Final_project_presentations]] | |||
---> | |||
You will also create and record a short presentation of your final project. The presentation will provide an opportunity to share a brief overview of your project and findings with the other members of the class. Since you will all give other research presentations throughout your career, I strongly encourage you to take the opportunity to refine your academic presentation skills. The document [https://canvas.northwestern.edu/files/9439377/download?download_frd=1 Creating a Successful Scholarly Presentation] (file posted to Canvas) may be useful. | |||
Additional details about the presentation goals, format suggestions, resources, and more will be provided later in the quarter. | |||
==== Human subjects research, IRB, and ethics ==== | ==== Human subjects research, IRB, and ethics ==== | ||
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==== September 17: Intro and setup ==== | ==== September 17: Intro and setup ==== | ||
; | ;Note: Aaron doesn't actually expect you to complete these before class on September 17 | ||
'''Required''' | '''Required''' | ||
* Complete [https://apps3.cehd.umn.edu/artist/user/scale_select.html pre-course assessment of statistical concepts] (access code TBA via email). '''Submission deadline: September 18, 11:00pm Chicago time''' | |||
* Complete [https://apps3.cehd.umn.edu/artist/user/scale_select.html pre-course assessment of statistical concepts] (access code TBA via email) | * Confirm access to software and web-services for course (Zoom, Discord, Canvas, this wiki, R, RStudio). | ||
* Confirm | * Complete [https://wiki.communitydata.science/Statistics_and_Statistical_Programming_(Fall_2020)/pset0 problem set 0] | ||
* Complete [https://wiki.communitydata.science/Statistics_and_Statistical_Programming_(Fall_2020)/pset0 problem set | |||
'''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 . | ** '''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) === | ||
==== 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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* Read Diez, Çetinkaya-Rundel, and Barr: §2.1-2 (Numerical and categorical data). | * 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)]. | * 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:''' | * Complete '''exercises from OpenIntro §2:''' (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. | ||
=== Week 3 (9/29, 10/1) === | === Week 3 (9/29, 10/1) === | ||
==== September 29: Working with data and variables in R ==== | |||
==== September 29: R | |||
'''Required''' | '''Required''' | ||
* Complete | * R lecture materials from 2019 W02 | ||
* Complete problem set #1 | |||
** TODO Empirical paper/data (UCB admissions. Police stops in IL.) | |||
** TODO update PS2 Programming challenges from 2019 | |||
'''Resources''' | '''Resources''' | ||
* [https://science.sciencemag.org/content/187/4175/398 UCB admissions paper] | * [https://science.sciencemag.org/content/187/4175/398 UCB admissions paper] | ||
* [https://openpolicing.stanford.edu Stanford OpenPolicing Project] | * [https://openpolicing.stanford.edu Stanford OpenPolicing Project] | ||
==== October 1: Probability ==== | ==== October 1: Probability ==== | ||
'''Required''' | '''Required''' | ||
* Read Diez, Çetinkaya-Rundel, and Barr: §3 (Probability). | * Read Diez, Çetinkaya-Rundel, and Barr: §3.1-3; §3.4-5 (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). | * 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:''' | * Complete '''exercises from OpenIntro §3:'''' | ||
'''Resources''' | '''Resources''' | ||
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=== Week 4 (10/6, 10/8) === | === Week 4 (10/6, 10/8) === | ||
==== October 6: <Topic> ==== | |||
==== October 6: | |||
'''Required''' | '''Required''' | ||
* Complete problem set #2 | |||
* Complete | |||
''' | '''Resources''' | ||
==== October 8: Distributions ==== | ==== October 8: Distributions ==== | ||
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* Read Diez, Çetinkaya-Rundel, and Barr: §4.1-3 (Normal and binomial distributions). | * 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). | * 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:''' | * Complete '''exercises from OpenIntro §4:'''' | ||
'''Resources''' | '''Resources''' | ||
* [https://seeing-theory.brown.edu/index.html#secondPage/chapter3 Seeing Theory §3 (Probability distributions)] | * [https://seeing-theory.brown.edu/index.html#secondPage/chapter3 Seeing Theory §3 (Probability distributions)] | ||
==== October 9: [[# | ==== October 9: [[#Project plan and dataset identification|Project plan and dataset identification]] due ==== | ||
*'''Submit via [https://canvas.northwestern.edu/courses/122522/assignments Canvas]''' | *'''Submit via [https://canvas.northwestern.edu/courses/122522/assignments Canvas]''' | ||
=== Week 5 (10/13, 10/15) === | === Week 5 (10/13, 10/15) === | ||
==== October 13: <Topic> ==== | |||
==== October 13: | |||
'''Required''' | '''Required''' | ||
* Complete | * Complete problem set #3 | ||
''' | '''Resources''' | ||
==== 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:''' | * Complete '''exercises from OpenIntro §5:'''' | ||
'''Resources''' | '''Resources''' | ||
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=== Week 6 (10/20, 10/22) === | === Week 6 (10/20, 10/22) === | ||
==== October 20: <Topic> ==== | |||
==== October 20: | |||
'''Required''' | '''Required''' | ||
* Complete | * Complete problem set #4 | ||
'''Resources''' | |||
==== 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:''' | * Complete '''exercises from OpenIntro §6:'''' | ||
'''Resources''' | '''Resources''' | ||
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=== Week 7 (10/27, 10/29) === | === Week 7 (10/27, 10/29) === | ||
==== October 27: <Topics> ==== | |||
==== October 27: | |||
'''Required''' | '''Required''' | ||
* Complete problem set #5 | |||
* Complete | |||
'''Resources''' | '''Resources''' | ||
==== 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:''' | * Complete '''exercises from OpenIntro §7:'''' | ||
'''Resources''' | '''Resources''' | ||
* [https://gallery.shinyapps.io/CLT_mean/ OpenIntro Central | * [https://gallery.shinyapps.io/CLT_mean/ OpenIntro Central liumit theorem for means demo]. | ||
==== October 30: [[# | ==== October 30: [[#Project planning document]] due ==== | ||
* Submit via [https://canvas.northwestern.edu/courses/122522/assignments | * Submit via [https://canvas.northwestern.edu/courses/122522/assignments Canvas] | ||
=== Week 8 (11/3, 11/5) === | === Week 8 (11/3, 11/5) === | ||
==== November 3: | ==== November 3: Self-assessment exercise (no class meeting) ==== | ||
'''Election Day (U.S.): No class meeting today''' | |||
==== 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:''' | * 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: | ==== November 10: <Topic> ==== | ||
'''Required''' | '''Required''' | ||
* Complete | * Complete problem set #6 | ||
'''Resources''' | '''Resources''' | ||
==== 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:''' | * Complete '''exercises from OpenIntro §8:'''' | ||
* Complete '''exercises from OpenIntro supplement:''' | * Complete '''exercises from OpenIntro supplement:'''' | ||
'''Resources''' | '''Resources''' | ||
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=== Week 10 (11/17, 11/19) === | === Week 10 (11/17, 11/19) === | ||
==== November 17: <Topic> ==== | |||
==== November 17: | |||
'''Required''' | '''Required''' | ||
* Complete | * Complete Problem set #7 | ||
'''Resources''' | '''Resources''' | ||
==== 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:''' | * Complete '''exercises from OpenIntro §9:'''' | ||
* Complete '''exercises from OpenIntro supplements:'''' | |||
'''Resources''' | '''Resources''' | ||
=== Week 11 (11/24) === | === Week 11 (11/24) === | ||
==== November 24: | ==== November 24: <Topic> and assessment ==== | ||
'''Required''' | '''Required''' | ||
* Complete | * 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 | * Mako Hill created an example of [https://communitydata.science/~mako/2017-COM521/logistic_regression_interpretation.html interpreting logistic regression coefficients with examples in R] | ||
== 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. |