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interpretation of complex and real-world datasets that go beyond textbook problems. Course topics will vary from year to year depending on the instructor and selected case studies. Topics may include visualization techniques to summarize and display high dimensional data, advanced topics in design and linear regression, selected topics in data mining, nonparametric analysis, and analysis of network data. Through a series of case studies, students develop the capacity to think and compute with data, undertake and assess analyses, and effectively communicate their results using written and oral presentation.
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Our world is awash in data. To allow decisions to be made based on evidence, there is a need for statisticians to be able to make sense of the data around us and communicate their findings. In this course, students will be exposed to advanced statistical methods and will undertake the analysis and
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tool shows unreferenced statements from articles. First, evaluate whether the statement in question is true! An uncited statement could just be lacking a reference or it could be inaccurate or misleading. Reliable sources on the subject will help you choose whether to add it or correct the
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It's time to dive into
Knowledge. Below, you'll find the first set of online trainings you'll need to take. New modules will appear on this timeline as you get to new milestones. Be sure to check back and complete them! Incomplete trainings will be reflected in your
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Using your chosen statistical article. Read through it, thinking about ways to improve the language, such as fixing grammatical mistakes. Then, make the appropriate changes. You don’t need to contribute new information to the article (but you
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Welcome to your
Knowledge project's course timeline. This page will guide you through the Knowledge project for your course. Be sure to check with your instructor to see if there are other pages you should be following as
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This course page is an automatically-updated version of the main course page at dashboard.wikiedu.org. Please do not edit this page directly; any changes will be overwritten the next time the main course page gets
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Please submit a brief (1 page) summary and overview of your changes along with a reflection on the process (no more than 2 pages double spaced) by the end of the day on Monday to your private github repo as
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Choose at least 2 questions relevant to the article you're evaluating. Leave your evaluation on the article's Talk page. Be sure to sign your feedback with four tildes —
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It's time to think critically about
Knowledge articles. You'll evaluate a Knowledge article, and leave suggestions for improving it on the article's Talk page.
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Your course has also been assigned a
Knowledge Content Expert. Check your Talk page for notes from them. You can also reach them through the "
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Add 1-2 sentences to a course-related article, and cite that statement to a reliable source, as you learned in the online training.
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Familiarize yourself with editing
Knowledge by adding a citation to an article. There are two ways you can do this:
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Is the article neutral? Are there any claims, or frames, that appear heavily biased toward a particular position?
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When you finish the trainings, practice by introducing yourself to a classmate on that classmate’s Talk page.
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Check a few citations. Do the links work? Is there any close paraphrasing or plagiarism in the article?
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Is everything in the article relevant to the article topic? Is there anything that distracted you?
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Create an account and join this course page, using the enrollment link your instructor sent you.
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Where does the information come from? Are these neutral sources? If biased, is that bias noted?
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Choose an article, and consider some questions (but don't feel limited to these):
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Is any information out of date? Is anything missing that could be added?
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Complete the "Evaluating
Articles and Sources" training (linked below).
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Are there viewpoints that are overrepresented, or underrepresented?
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Is each fact referenced with an appropriate, reliable reference?
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To get started, please review the following handouts:
222:2016-09-06 00:00:00 UTC – 2016-12-22 23:59:59 UTC
527:In class - Introduction to the Knowledge project
974:Dashboard.wikiedu.org courses, Amherst College
8:
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761:Assignment - Copyedit an article (redux)
227:Approximate number of student editors
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969:Dashboard.wikiedu.org course pages
570:Assignment - Practicing the basics
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488:Predictive probability of success
483:Predictive probability of success
616:Assignment - Critique an article
449:La Canada Flintridge, California
260:Pioneer Valley Transit Authority
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693:Wednesday, 28 September 2016
515:Wednesday, 14 September 2016
947:Wednesday, 16 November 2016
705:Assignment - Add to an article
178:STAT495-Advanced-Data-Analysis
1:
916:Wednesday, 9 November 2016
885:Wednesday, 2 November 2016
854:Wednesday, 26 October 2016
823:Wednesday, 19 October 2016
749:Wednesday, 5 October 2016
689:Monday, 26 September 2016
665:12:38, 24 October 2016 (UTC)
608:Wednesday, 21 September 2016
604:Monday, 19 September 2016
269:Multiple comparisons problem
264:Multiple comparisons problem
943:Monday, 14 November 2016
366:Complex Normal Distribution
361:Complex Normal Distribution
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912:Monday, 7 November 2016
881:Monday, 31 October 2016
850:Monday, 24 October 2016
819:Monday, 17 October 2016
745:Monday, 3 October 2016
697:Friday, 30 September 2016
519:Friday, 16 September 2016
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951:Friday, 18 November 2016
920:Friday, 11 November 2016
311:Statistical significance
302:Statistical significance
91:Editing guidelines (PDF)
889:Friday, 4 November 2016
858:Friday, 28 October 2016
827:Friday, 21 October 2016
796:Friday, 14 October 2016
771:"Knowledge-summary.pdf"
541:" button on this page.
383:Imputation (statistics)
378:Imputation (statistics)
349:Conditional probability
344:Conditional probability
753:Friday, 7 October 2016
400:Look-elsewhere effect
395:Look-elsewhere effect
559:Evaluating Knowledge
440:La Canada Flintridge
81:Interactive training
286:Logistic regression
281:Logistic regression
75:Knowledge Resources
328:Stochastic process
323:Stochastic process
133:Questions? Ask us:
553:Editing Knowledge
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465:Digital marketing
453:Survival analysis
444:Survival Analysis
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18:Knowledge:Wiki Ed
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469:Machine Learning
426:Maui High School
307:Johnny Appleseed
298:Johnny Appleseed
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201:Knowledge Expert
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102:Help pages (PDF)
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66:Edit this page
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720:Citation Hunt
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126:Other courses
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55:Activity Feed
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421:Wuthering313
412:Bayes factor
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219:Course dates
135:
723:statement.
255:Jbrowning17
183:Institution
175:Course name
147:wikiedu.org
30:This Course
963:Categories
390:Oliviaxu17
249:Reviewing
214:Statistics
191:Instructor
46:Discussion
657:Bikestats
555:pages 1–5
246:Assigned
36:Dashboard
539:Get Help
496:Timeline
318:Chaley17
293:Cokusiak
276:Trant22t
243:Student
167:updated.
930:Week 10
356:Mowen17
335:MulingS
211:Subject
137:contact
120:Connect
899:Week 9
868:Week 8
837:Week 7
806:Week 6
783:Week 5
732:Week 4
676:Week 3
591:Week 2
579:grade.
532:well.
502:Week 1
460:Ajav13
435:Akim17
407:Llee17
767:can).
373:Cki17
16:<
718:The
661:talk
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