CommunityData:StatsGaps: Difference between revisions
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* [https://www.openintro.org/download.php?file=os3_slides_02&referrer=/stat/slides/slides_0x.php Mine Çetinkaya-Rundel's OpenIntro §2 Lecture Notes] | * [https://www.openintro.org/download.php?file=os3_slides_02&referrer=/stat/slides/slides_0x.php Mine Çetinkaya-Rundel's OpenIntro §2 Lecture Notes] | ||
* [https://www.openintro.org/stat/videos.phpOpenIntro Video Lectures] including 2 short videos for §2 | * [https://www.openintro.org/stat/videos.phpOpenIntro Video Lectures] including 2 short videos for §2 | ||
=== Week 3: Distributions === | |||
All: (N/A) | |||
Learn R: | |||
* [https://communitydata.cc/~ads/teaching/2019/stats/r_lectures/w03-R_lecture.Rmd Week 3 R lecture materials] (.Rmd file) | |||
* [https://communitydata.cc/~ads/teaching/2019/stats/screencasts/w03.webm Week 3 screencast (19 minutes)] | |||
Learn Stats: | |||
* Read Diez, Barr, and Çetinkaya-Rundel: §3.1-3.2, §3.4 (Aaron says: You should read the rest of the chapter (§3.3 and §3.5). I won't assign problem set questions about it but it's still important to be familiar with.) | |||
* Do Problem Set 3 [[Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 3]] | |||
Refresh: | |||
* Read Problem Set 3 [[Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 3]] | |||
Stronger: | |||
* Skim Problem Set 3 [[Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 3]] | |||
'''Extra Resources:''' | |||
* [https://seeing-theory.brown.edu/ Seeing Theory] §3 (Probability Distributions). | |||
* [https://www.openintro.org/download.php?file=os3_slides_03&referrer=/stat/slides/slides_0x.php Mine Çetinkaya-Rundel's OpenIntro §3 Lecture Notes] | |||
* [https://www.openintro.org/stat/videos.php OpenIntro Video Lectures] including 2 videos for §3.1 and §3.2 |
Revision as of 03:30, 11 July 2019
DRAFT -- IN PROGRESS -- IGNORE
Welcome to the StatsGaps StudyGroup page -- a set of suggested learning pathways making use of course resources produced by community data faculty, meant to be used by folks who have a mixture of familiarity and non-familiarity with R, statistics, and research processes. The primary text is: [Open Intro to Statistics]
We borrow heavily from the course most recently taught by Aaron: [[1]]
Follow the strand(s) that apply to you:
- Learn R -- you don't know R
- Learn Stats -- you haven't taken much if any statistics, or otherwise feel you're mostly starting from scratch
- Refresh -- overview and shore up your stats knowledge if it feels rusty
- Stronger -- your stats knowledge is strong but your class stopped before you got to good stuff you see used in lots of the papers in this group, like regression)
Week 1
All:
- 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. [Open Access] If you haven't read this, you should--also, check the 'Editorial expression of concern' at the top and toss "kramer guillory hancock 2014" if you want to see the firestorm that this article created.-khc
Learn R:
- Week 1 R lecture materials (.zip file)
- Week 1 screencast (part 1, 23 minutes) (the video should load directly in browser window)
- Week 1 screencast (part 2, 27 minutes)
Learn Stats:
- Diez, Barr, and Çetinkaya-Rundel: §1 (Introduction to data)
- [Do Problem Set 1]
Refresh:
- Diez, Barr, and Çetinkaya-Rundel: §1 (Introduction to data)
- [Read Problem Set 1]
Stronger:
- [Skim Problem Set 1] -- since we may discuss it f2f. Take a look at the text's Chapter 1 if you find any of the questions to be confusing or the answer you came up with is different than the key.
Extra Resources:
- Mine Çetinkaya-Rundel's OpenIntro §1 Lecture Notes
- OpenIntro Video Lectures including some for §1
Week 2: Probability and Visualization
All:
- 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. [[2]]
Learn R:
Learn Stats:
- Diez, Barr, and Çetinkaya-Rundel: §2 (Probability)
- Do problem set 2 -- Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 2
Refresh:
- Diez, Barr, and Çetinkaya-Rundel: §2 (Probability)
- Read problem set 2 Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 2
Stronger:
- Skim problem set 2 Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 2
Extra Resources:
- Seeing Theory §1 (Basic Probability) and §2 (Compound Probability). (Note: this site provides a beautiful visual introduction to core concepts in probability and statistics).
- 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. [PDF]
- Mine Çetinkaya-Rundel's OpenIntro §2 Lecture Notes
- Video Lectures including 2 short videos for §2
Week 3: Distributions
All: (N/A)
Learn R:
Learn Stats:
- Read Diez, Barr, and Çetinkaya-Rundel: §3.1-3.2, §3.4 (Aaron says: You should read the rest of the chapter (§3.3 and §3.5). I won't assign problem set questions about it but it's still important to be familiar with.)
- Do Problem Set 3 Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 3
Refresh:
- Read Problem Set 3 Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 3
Stronger:
- Skim Problem Set 3 Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 3
Extra Resources:
- Seeing Theory §3 (Probability Distributions).
- Mine Çetinkaya-Rundel's OpenIntro §3 Lecture Notes
- OpenIntro Video Lectures including 2 videos for §3.1 and §3.2