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StatKey Online Tools for Teaching a Modern Introductory Statistics Course Robin Lock Burry Professor of Statistics St. Lawrence University

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Presentation on theme: "StatKey Online Tools for Teaching a Modern Introductory Statistics Course Robin Lock Burry Professor of Statistics St. Lawrence University"— Presentation transcript:

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2 StatKey Online Tools for Teaching a Modern Introductory Statistics Course Robin Lock Burry Professor of Statistics St. Lawrence University rlock@stlawu.edu Wiley Faculty Network - March 11, 2013

3 What is it? Freely available at www.lock5stat.com/statkey Runs in (almost) any browser. Also available as a Google Chrome App. A set of web-based, interactive, dynamic statistics tools designed for teaching simulation-based methods such as bootstrap intervals and randomization tests at an introductory level. StatKey

4 Who Developed StatKey? The Lock 5 author team to support a new text: Statistics: Unlocking the Power of Data Robin & Patti St. Lawrence Dennis Iowa State Eric UNC/Duke Kari Harvard/Duke Wiley (2013)

5 WHY? Address instructor concerns about accessibility of simulation-based methods at the intro level Design an easy-to-use set of learning tools Provide a no-cost technology option for any environment OR as a supplement to existing technology Support our new textbook, while also being usable with other texts or on its own StatKey

6 Programming Team Rich Sharp Stanford Ed Harcourt St. Lawrence Kevin Angstadt St. Lawrence StatKey is programmed in JavaScript StatKey

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8 Let’s try it out! www.lock5stat.com StatKey

9 Sampling Distribution Look at many samples from the population to see how a statistic varies from sample to sample. BUT, in most real situations... WE ONLY HAVE ONE SAMPLE! BOOTSTRAP ! Key idea: To simulate a sampling distribution, we take many samples with replacement from the original sample using the same n.

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11 Why does the bootstrap work?

12 Sampling Distribution Population µ BUT, in practice we don’t see the “tree” or all of the “seeds” – we only have ONE seed

13 Bootstrap Distribution Bootstrap “Population” What can we do with just one seed? Grow a NEW tree! µ

14 Golden Rule of Bootstraps The bootstrap statistics are to the original statistic as the original statistic is to the population parameter.

15 According to a CNN poll of n=722 likely voters in Ohio: 368 choose Obama (51%) 339 choose Romney (47%) 15 choose otherwise (2%) http://www.cnn.com/POLITICS/pollingcenter/polls/3250 Find a 95% confidence interval for the proportion of Obama supporters in Ohio.

16 Example: Pulse Rate by Athlete? Find a 95% CI for the difference in mean pulse rate between students who are not athletes and those who are.

17 Beer and Mosquitoes Does consuming beer attract mosquitoes? Experiment: 25 volunteers drank a liter of beer, 18 volunteers drank a liter of water Randomly assigned! Mosquitoes were caught in traps as they approached the volunteers. 1 1 Lefvre, T., et. al., “Beer Consumption Increases Human Attractiveness to Malaria Mosquitoes, ” PLoS ONE, 2010; 5(3): e9546. Beer mean = 23.6 Water mean = 19.22 H 0 : μ B =μ W H 0 : μ B >μ W

18 Traditional Inference 1. Which formula? 2. Calculate numbers and plug into formula 3. Which theoretical distribution? 4. Find p-value 0.0005 < p-value < 0.001

19 www.lock5stat.com/statkey StatKey Give it a try! Send comments and feedback rlock@stlawu.edu

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