Jeff Witmer (Oberlin College)
Tuesday, October 17, 2017 - 2:00pm
Regression to the mean, also known as "the regression effect," is an important but sometimes overlooked topic in introductory statistics. We will discuss the regression effect and how to teach it. We will also consider a number of examples of the "regression fallacy," in which people who are ignorant of the regression effect make up ad hoc (and sometimes very misleading) explanations for what they see in data.
Carolee Mitchell, Academic Relationships Manager, data.world
Tuesday, July 25, 2017 - 2:00pm
18M+ open datasets exist today, and growth is accelerating. But these data sets live in data portals without common taxonomies or architectures, and must first be cleaned and prepared by data users. Human and computers normalize, extract meaning, and identify correlations, but this work is siloed: used for one project, then lost forever, only to be repeated from scratch by the next person to touch the data.
Open data can help us rise to humanity’s toughest challenges, but only if we maximize its network effect. To build the web of Linked Data, we have to start by connecting the people who are working with data.
Nicholas J. Horton (Amherst College)
Tuesday, June 20, 2017 - 2:00pm
In 2014 Committee of Presidents of Statistical Societies (COPSS) published a book entitled "Past, Present, and Future of Statistical Science" that contains 52 short chapters contributed by past winners of one of the COPSS Awards. The goal of the book (which is freely downloadable from the COPSS website or http://tinyurl.com/copss-ppf) was to "showcase the breadth and vibrancy of statistics, to describe current challenges and new opportunities, to highlight the exciting future of statistical science, and to provide guidance for future generations of statisticians (page xvii)." In this webinar, I will describe how these chapters were integrated into a theoretical statistics course to help students see the big picture and potential for statistics.
Kristen Roland and Jennifer Kaplan (University of Georgia)
Tuesday, April 18, 2017 - 2:00pm
As enrollment in introductory statistics courses across the country rises, more instructors for these courses are needed. Many statistics courses are now taught by Graduate Teaching Assistants (GTAs). Little is known, however, about the training needs of GTAs to foster active learning and promote conceptual understanding, critical recommendations of the GAISE guidelines to improve undergraduate learning in statistics. This talk will discuss changes to our lab activities to incorporate GAISE recommendations of teaching for conceptual understanding, foster active learning, and integrating real data. We will also discuss initial findings concerning the struggles GTAs have with connecting their theoretical knowledge to conceptual ideas concerning confidence intervals for one population proportion. The material is based on work supported by NSF DUE 1504587.
Chester Ismay and Albert Y. Kim
Tuesday, March 14, 2017 - 2:00pm
This webinar will provide a guide to creating a user-adaptable electronic textbook incorporating data visualization, data science, and other relevant pedagogical concepts into your introductory statistics course. We present our own introductory statistics and data science textbook available at http://moderndive.com that:
Focuses on the entirety of the data/science pipeline from importing data to visualizing and summarizing data to inferential techniques and developing students as effective data storytellers
Blurs the line between lecture and lab
Uses freely available modern, rich, and complex data sources
Leverages resampling and simulation to build statistical inference concepts
Most importantly, provides complete customizability to the instructor and reproducibility to the student
We’ll discuss how collaboration and crowd-sourcing have and will play a role in our textbook going forward and other open-source materials we are creating to better support introductory statistics/data science students learning the skills and tools that statisticians/data scientists are using today.
For the complete powerpoint presentation of today's webinar: http://bit.ly/moderndive-causeweb
Concetta DePaolo, David Robinson, and Aimee Jacobs
Tuesday, February 21, 2017 - 2:30pm
We present actual data gathered from a café run by business students. We give examples of time series forecasting and data mining applications, and frame problems as managerial questions to emphasize data-driven decision making.
Monnie McGee, Lynne Stokes, and Pavel Nadolsky, Southern Methodist University
Tuesday, January 17, 2017 - 2:00pm
In this webinar we will present our experiences and provide tips on how to implement a flipped classroom approach we call "Just-in-Time Teaching." In this method in and out of classroom activities are reversed; student preparation before class includes watching a brief lecture via video and responding to web-based discussion questions designed to elicit common misunderstandings students have, and class time is reserved for guided practice to reinforce new knowledge.
Monday, December 12, 2016 - 2:00pm
Our university recently began offering a bachelor’s degree in data science. One of the required courses for this major is a course on data summary and visualization. Fall Semester 2016 was the second time this course was offered at our university. In this talk, I will describe the content, structure, and pace of this course and provide examples of student output.
Dennis Pearl, Director of CAUSE
Tuesday, November 29, 2016 - 2:00pm
This webinar will provide a tour of CAUSEweb.org and its special collections and features. The webinar will also provide ways for community involvement in building the collections and seek audience suggestions for future projects.
Jennifer J. Kaplan, University of Georgia; Neal Rogness, Grand Valley State University; Diane Fisher, University of Louisiana at Lafayette
Tuesday, September 13, 2016 - 1:00pm
Research on faculty professional development suggests that in order for faculty to change their teaching, they must perceive a problem, be presented with changes they can adapt to their own teaching style, and see evidence of change in student learning based on the changes. Many words in statistics pose a barrier for entry level students because they everyday meanings which differ from their discipline usage within statistics; this can lead to lexical ambiguity for students. The webinar will focus on two High-Impact, Little-Time (HILT) activities developed by faculty involved in a faculty learning community to help exploit lexical ambiguities associated with parameter. We will present the activities, along with the data that show the effectiveness of the activities with respect to student learning.