StatKey: Online Tools for Bootstrap Intervals and Randomization Tests Kari Lock Morgan Department of Statistical Science Duke University Joint work with.

Slides:

Advertisements

Similar presentations

Panel at 2013 Joint Mathematics Meetings

Advertisements

StatKey Online Tools for Teaching a Modern Introductory Statistics Course Robin Lock St. Lawrence University USCOTS Breakout – May 2013 Patti Frazer Lock.

Statistical Inference Using Scrambles and Bootstraps Robin Lock Burry Professor of Statistics St. Lawrence University MAA Allegheny Mountain 2014 Section.

Using Randomization Methods to Build Conceptual Understanding in Statistical Inference: Day 1 Lock, Lock, Lock Morgan, Lock, and Lock MAA Minicourse –

Using Randomization Methods to Build Conceptual Understanding in Statistical Inference: Day 1 Lock, Lock, Lock, Lock, and Lock MAA Minicourse – Joint Mathematics.

Simulating with StatKey Kari Lock Morgan Department of Statistical Science Duke University Joint Mathematical Meetings, San Diego 1/11/13.

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

Statistics: Unlocking the Power of Data Lock 5 STAT 101 Dr. Kari Lock Morgan 10/23/12 Sections , Single Proportion, p Distribution (6.1)

Hypothesis Testing: Intervals and Tests

Early Inference: Using Bootstraps to Introduce Confidence Intervals Robin H. Lock, Burry Professor of Statistics Patti Frazer Lock, Cummings Professor.

Hypothesis Testing I 2/8/12 More on bootstrapping Random chance

Intuitive Introduction to the Important Ideas of Inference Robin Lock – St. Lawrence University Patti Frazer Lock – St. Lawrence University Kari Lock Morgan.

Statistics: Unlocking the Power of Data Lock 5 Inference Using Formulas STAT 101 Dr. Kari Lock Morgan Chapter 6 t-distribution Formulas for standard errors.

Models and Modeling in Introductory Statistics Robin H. Lock Burry Professor of Statistics St. Lawrence University 2012 Joint Statistics Meetings San Diego,

Section 3.4 Bootstrap Confidence Intervals using Percentiles.

A Fiddler on the Roof: Tradition vs. Modern Methods in Teaching Inference Patti Frazer Lock Robin H. Lock St. Lawrence University Joint Mathematics Meetings.

STAT 101 Dr. Kari Lock Morgan Exam 2 Review.

Connecting Simulation- Based Inference with Traditional Methods Kari Lock Morgan, Penn State Robin Lock, St. Lawrence University Patti Frazer Lock, St.

Dr. Kari Lock Morgan Department of Statistics Penn State University Teaching the Common Core: Making Inferences and Justifying Conclusions ASA Webinar.

Starting Inference with Bootstraps and Randomizations Robin H. Lock, Burry Professor of Statistics St. Lawrence University Stat Chat Macalester College,

Using Simulation Methods to Introduce Statistical Inference Patti Frazer Lock Kari Lock Morgan Cummings Professor of Mathematics Assistant Professor of.

Building Conceptual Understanding of Statistical Inference with Lock 5 Dr. Kari Lock Morgan Department of Statistical Science Duke University Wake Forest.

Inference for Categorical Variables 2/29/12 Single Proportion, p Distribution Intervals and tests Difference in proportions, p 1 – p 2 One proportion or.

Bootstrapping: Let Your Data Be Your Guide Robin H. Lock Burry Professor of Statistics St. Lawrence University MAA Seaway Section Meeting Hamilton College,

Introducing Inference with Simulation Methods; Implementation at Duke University Kari Lock Morgan Department of Statistical Science, Duke University

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

Using Bootstrap Intervals and Randomization Tests to Enhance Conceptual Understanding in Introductory Statistics Kari Lock Morgan Department of Statistical.

Introducing Inference with Bootstrap and Randomization Procedures Dennis Lock Statistics Education Meeting October 30,

Randomization Tests Dr. Kari Lock Morgan PSU /5/14.

Building Conceptual Understanding of Statistical Inference Patti Frazer Lock Cummings Professor of Mathematics St. Lawrence University

More Randomization Distributions, Connections

Understanding the P-value… Really! Kari Lock Morgan Department of Statistical Science, Duke University with Robin Lock, Patti Frazer.

Using Simulation Methods to Introduce Inference Kari Lock Morgan Duke University In collaboration with Robin Lock, Patti Frazer Lock, Eric Lock, Dennis.

Confidence Intervals: Bootstrap Distribution

Statistics: Unlocking the Power of Data Lock 5 Normal Distribution STAT 250 Dr. Kari Lock Morgan Chapter 5 Normal distribution Central limit theorem Normal.

Normal Distribution Chapter 5 Normal distribution

Statistics: Unlocking the Power of Data Lock 5 Synthesis STAT 250 Dr. Kari Lock Morgan SECTIONS 4.4, 4.5 Connecting bootstrapping and randomization (4.4)

Using Lock5 Statistics: Unlocking the Power of Data

What Can We Do When Conditions Aren’t Met? Robin H. Lock, Burry Professor of Statistics St. Lawrence University BAPS at 2011 JSM Miami Beach, August 2011.

How to Handle Intervals in a Simulation-Based Curriculum? Robin Lock Burry Professor of Statistics St. Lawrence University 2015 Joint Statistics Meetings.

Statistics: Unlocking the Power of Data Lock 5 Afternoon Session Using Lock5 Statistics: Unlocking the Power of Data Patti Frazer Lock University of Kentucky.

Building Conceptual Understanding of Statistical Inference Patti Frazer Lock Cummings Professor of Mathematics St. Lawrence University

Statistics: Unlocking the Power of Data Patti Frazer Lock Cummings Professor of Mathematics St. Lawrence University University of Kentucky.

Statistics: Unlocking the Power of Data Lock 5 STAT 101 Dr. Kari Lock Morgan 9/18/12 Confidence Intervals: Bootstrap Distribution SECTIONS 3.3, 3.4 Bootstrap.

Introducing Inference with Simulation Methods; Implementation at Duke University Kari Lock Morgan Department of Statistical Science, Duke University

Statistics: Unlocking the Power of Data Lock 5 STAT 101 Dr. Kari Lock Morgan 11/1/12 ANOVA SECTION 8.1 Testing for a difference in means across multiple.

Statistics: Unlocking the Power of Data Lock 5 Normal Distribution STAT 101 Dr. Kari Lock Morgan 10/18/12 Chapter 5 Normal distribution Central limit theorem.

Using Randomization Methods to Build Conceptual Understanding of Statistical Inference: Day 2 Lock, Lock, Lock Morgan, Lock, and Lock MAA Minicourse- Joint.

Robin Lock St. Lawrence University USCOTS Opening Session.

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

Confidence Intervals: Bootstrap Distribution

Introducing Inference with Bootstrapping and Randomization Kari Lock Morgan Department of Statistical Science, Duke University with.

Implementing a Randomization-Based Curriculum for Introductory Statistics Robin H. Lock, Burry Professor of Statistics St. Lawrence University Breakout.

Statistics: Unlocking the Power of Data Lock 5 Bootstrap Intervals Dr. Kari Lock Morgan PSU /12/14.

Testing for an Association between two Categorical Variables

Building Conceptual Understanding of Statistical Inference Patti Frazer Lock Cummings Professor of Mathematics St. Lawrence University Canton, New York.

Statistics: Unlocking the Power of Data Lock 5 Exam 2 Review STAT 101 Dr. Kari Lock Morgan 11/13/12 Review of Chapters 5-9.

Using Bootstrapping and Randomization to Introduce Statistical Inference Robin H. Lock, Burry Professor of Statistics Patti Frazer Lock, Cummings Professor.

Statistics: Unlocking the Power of Data Lock 5 STAT 101 Dr. Kari Lock Morgan 12/6/12 Synthesis Big Picture Essential Synthesis Bayesian Inference (continued)

Statistics: Unlocking the Power of Data Lock 5 Inference for Means STAT 250 Dr. Kari Lock Morgan Sections 6.4, 6.5, 6.6, 6.10, 6.11, 6.12, 6.13 t-distribution.

Statistics: Unlocking the Power of Data Lock 5 Inference for Means STAT 250 Dr. Kari Lock Morgan Sections 6.4, 6.5, 6.6, 6.10, 6.11, 6.12, 6.13 t-distribution.

Statistics: Unlocking the Power of Data Lock 5 STAT 250 Dr. Kari Lock Morgan SECTION 7.2 χ 2 test for association (7.2) Testing for an Association between.

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

Bootstraps and Scrambles: Letting a Dataset Speak for Itself Robin H. Lock Patti Frazer Lock ‘75 Burry Professor of Statistics Cummings Professor of MathematicsSt.

Making computing skills part of learning introductory stats

Connecting Intuitive Simulation-Based Inference to Traditional Methods

Improving Conceptual Understanding in Intro Stats

Improving Conceptual Understanding in Intro Stats

Teaching with Simulation-Based Inference, for Beginners

Randomization Tests - Beyond One/Two Sample Means & Proportions

Presentation transcript:

StatKey: Online Tools for Bootstrap Intervals and Randomization Tests Kari Lock Morgan Department of Statistical Science Duke University Joint work with Robin Lock, Patti Frazer Lock, Eric Lock, Dennis Lock ICOTS 7/17/14

StatKey A set of web-based, interactive, dynamic statistics tools designed for teaching simulation-based methods at an introductory level. Freely available at No login required Runs in (almost) any browser (incl. smartphones) Google Chrome App available (no internet needed) Standalone or supplement to existing technology

StatKey Developed by the Lock 5 team Developed for our book, Statistics: Unlocking the Power of Data (although can be used with any book) Programmed by Rich Sharp (Stanford), Ed Harcourt and Kevin Angstadt (St. Lawrence) Wiley (2013) Robin & Patti St. Lawrence Dennis Miami Dolphins Kari Duke / Penn State Eric U Minnesota

StatKey Goals Free Convenient Very easy-to-use Helps promote understanding For those who want to use simulation methods, technology should not be a limiting factor!

StatKey WHY? Address instructor concerns about accessibility of simulation-based methods at the intro level Design an easy-to-use set of learning tools accessible to everyone Provide a no-cost technology option for any environment OR as a supplement to existing technology Make sure everyone

Bootstrap Confidence Interval Lange, T., Royals, H. and Connor, L. (2004). Mercury accumulation in largemouth bass (Micropterus salmoides) in a Florida Lake. Archives of Environmental Contamination and Toxicology, 27(4),

Original Sample One Simulated Sample Distribution of Simulated Statistics Bootstrap Confidence Interval

SE = Distribution of Bootstrap Statistics  2  (0.433, 0.621) Middle 95% of bootstrap statistics

CI for Proportion Have you used simulation-based methods (bootstrap confidence intervals or randomization tests) in your teaching?

Randomization Test Crum, A. and Langer, E., (2007). Mind-Set Matters: Exercise and the Placebo Effect. Psychological Science, 18,

Randomization Test p-value Proportion as extreme as observed statistic observed statistic Distribution of Statistic Assuming Null is True

r = 0.43 NFL Teams Malevolent Uniforms Do NFL teams with more malevolent uniforms get more penalty yards?

Ability to simulate one to many samples Helps students distinguish and keep straight the original data, a single simulated data set, and the distribution of simulated statistics Students have to interact with the bootstrap/randomization distribution – they have to know what to do with it Consistent interface for bootstrap intervals, randomization tests, theoretical distributions StatKey Pedagogical Features

Maid weight loss example: t-distribution df = 33 Theoretical Distributions

p-value t-statistic MUCH more intuitive and easier to use than tables!!!

Chi-square tests Goodness-of-fit or test for association Gives  2 statistic, as well as observed and expected counts for each cell Randomization test or  2 distribution ANOVA Difference in means or regression Gives entire ANOVA table Randomization test or F-distribution Chi-Square and ANOVA

Is there an association between type of painkiller taken during pregnancy and miscarriage? Chi-Square Test for Association

Chi-Square Test Randomization Distribution Chi-Square Distribution (3 df) p-value =  2 = p-value =  2 = 6.168

Does exposure to light at night make you fatter? (does it make mice fatter?) ANOVA for Difference in Means

Simulate a sampling distribution Generate confidence intervals for each simulated statistic, keep track of coverage rate Population data: Finishing times for the 2008 Men’s Olympic marathon Sampling Distributions

Descriptive Statistics

Help Help page, including instructional videos

Suggestions? Comments? Questions? You can me at or the whole Lock 5 team at