# Data Sets

The following data sets have been submitted by members of the SBI listserve.

• Hope College students (in 2003) were wondering if there are any gender differences when it comes to how long people talk on their cell phone. They asked a sample of other students and asked them their gender (0=female, 1=male) and how long their last cell phone call was as measured in seconds (they could find this data recorded on the phone). Dealing with the outlier in this data set makes it interesting.  cellphonedata

# Archived webinar/e-conference sessions

Listed below are a series of webinar/e-conference style presentations given by faculty using, or considering the use of, SBI methods

Batting for power (using a simulation-based approach)
Allan Rossman and Beth Chance (Cal Poly San Luis Obispo)
October 27, 2015

Reflections on making the switch to a simulation-based inference curriculum
Panelists: Julie Clark (Hollins), Lacey Echols (Butler), Dave Klanderman (Trinity), Laura Schultz (Rowan); Moderator: Nathan Tintle
September 8, 2015

Teaching the statistical investigation process with randomization-based inference
Nathan Tintle (Dordt College) and Beth Chance (Cal Poly San Luis Obispo)
eCOTS 2014

Teaching Randomization-based Methods in an Introductory Statistics Course: The CATALST Curriculum
Bob delMas, University of Minnesota
eCOTS 2014

StatKey – Online Tools for Teaching Bootstrap Intervals and Randomization Tests
with Robin Lock, St. Lawrence University
August 27th, 2013

Using Simulation to Introduce Inference for Regression
with Josh Tabor, Canyon del Oro High School
May 28th, 2013

Introducing inference with bootstrapping and randomization
with Kari Lock Morgan, Duke University
eCOTS 2012

Using Simulation Methods to Introduce Inference
with Kari Lock Morgan, Duke University
December 13th, 2011

Bootstrapping and randomization: Seeing all the moving parts
with Chris Wild, University of Auckland, New Zealand
November 22nd, 2011

Create an Iron Chef in statistics classes?
Rebekah Isaak, Laura Le, Laura Ziegler, and the CATALST Team
June 14th, 2011

Golfballs In The Yard – Using Simulation To Teach Hypothesis Testing
Randall Pruim, Calvin College
January 25th, 2011

“Using baboon “mothering” behavior to teach Permutation tests”
with Thomas Moore, Grinnell College
Sept 14, 2010

“Pedagogical simulations with StatCrunch”
with Webster West, Texas A&M University
July 13, 2010

Concepts of Statistical Inference: A Randomization-Based Curriculum
Allan Rossman & Beth Chance, Cal Poly – San Luis Obispo; and John Holcomb, Cleveland State University
April 14th, 2009

Teaching Statistical Inference via Simulation using R
Daniel Kaplan, Macalester College
October 14th, 2008

# New Listserv

For more immediate conversations about these issues, also consider joining the Simulation-Based Inference Listserv. The SBI mailing list  is intended for individuals interested in discussing pedagogical issues related to using simulation-based inference techniques (e.g., randomization tests) in introductory statistics courses as the primary introduction to statistical inference. For more information or to subscribe, go to https://www.causeweb.org/mailman/listinfo/sbi.

# Assessment Opportunities

We are looking for individuals/institutions willing to give common assessment items during Winter/Spring 2015.

• Pre/Post attitude surveys (SATS)
• Pre/Post concept inventory (modeled after CAOS and GOALS)
• Embedded multiple choice exam questions (Unit 1: One Proportion, Unit 2: Two Proportions and/or Two Means)

We will send you names of students who participated (if you want to give course credit) as well as a report at the end of the term with your student results and comparison results. For more information, please contact Cindy Nederhoff <Cindy.Nederhoff@dordt.edu>.

 Introduction to Statistical Investigations ISI Discussion Board Forum Statistics: Unlocking the power of data Statistical Reasoning in Sports Statistical Thinking: A simulation approach to modeling uncertainty Intro Stat with Randomization and Simulation Investigating Statistical Concepts, Applications, and Methods