Presentations can be downloaded by clicking on links for the presenter(s).
Not all presenters provided electronic copies of their presentations.
What's Needed in a Second Statistics Class to Prepare Undergraduates for
a Data Analytics Environment?
8:00 AM, Saturday, Nov. 18; Session Co-Chairs: Robert Andrews (V. C. U.) & Keith Ord (Georgetown University)
Abstract: Experienced teachers present their perspectives on class content and instruction for a second statistics class and the audience will be included in discussion. Topics include obtaining real data, the most appropriate statistical analysis procedures and software for introductory analytics through effective communication of analysis results to a decision maker.
Keith Ord, Georgetown University National Academy of Sciences Committee Report on Data Science
Bill Miller, Georgia College & State University [Publicly-Available (Free) Databases ]
Linda Boardman Liu, Boston College [Communicating Analysis Results]
Barry A Wray & Stephen Hill, UNC-Wilmington [Lessons Learned at UNCW]
Weiyong Zhang, Old Dominion University [Thoughts on Second Statistics Class]
Analytics Curriculum, MOOCs, and Student Learning in Traditional vs. Online
10 AM Saturday, November 18; Session Chair: Robert Andrews, Virginia Commonwealth University
Abstract: Discussion of experiences in developing curriculum to include analytics in the undergraduate business program, a review of massive online open courses in statistics and data analytics and assessment of student learning in statistics and operations management for both traditional & online course delivery. Discussion will include audience participation.
Rickard Enstroem, MacEwan University [Developing a Minor in Business Intelligence]
Binshan Lin, Louisiana State Unive rsity in Shreveport [Teaching Online]
Burcu Adivar, Fayetteville State University [Student Learning Experience in Quantitative Courses]
Wilma Andrews, Virginia Commonwealth University [Presentations that Communicate Results ]
Multivariate Methods: Excel Regression Tips, Causal Inference, & Moving from Bivariate to Multivariate
1:00 PM Saturday, November 18; Session Chair: Robert Andrews, Virginia Commonwealth University
Abstract: Excel techniques for any/all pair-wise scatter plots and to automatically find the best possible subset of regression predictor variables. Overview of causal inference approaches and comparison of available methods including assumptions and pros & cons. Show an easy switch from popular bivariate scatter plots to a Principal Component Analysis.
Cliff Ragsdale, Virginia Tech [Excel Regression Tips]
Helmut Schneider & Xuan Wang, Louisiana State University [Causal Inference]
Jean Paul Maalouf & Efthalia Anagnostou, XLSTAT [Bivariate to Principal Components Analysis]
What Statistics Knowledge Should Students Know to Prepare for Graduate Business
2:50 PM Saturday, November 18; Session Chair: Ram B Misra, Montclair State University
Abstract: A panel of textbook authors and professors teaching graduate level analytics courses lead a discussion among those attending on the content of a prerequisite statistics class. What current topics should be retained and what new topics should be added for analytics preparation? What computational tools are the most appropriate?
Jeffrey Camm, Wake Forest University [What we want students to know]
David Levine, Baruch College [Session PowerPoint]
Ram Misra, Montclair State University [Session PowerPoint]
Helen Zhang, University of Arizona [Session PowerPoint]
Advanced Analysis Methods: Text Mining and PLS Structural Equation Modeling
4:30 PM Saturday, November 18; Session Chair: Robert Andrews, Virginia Commonwealth University
Abstract: Demonstrations using real data of the capabilities of a point-and-click dynamic interface in JMP13 to explore, prepare, and analyze text data and in XLSTAT for PLS Structural Equation Modeling along with a comparison of the capabilities of using Python vs SPSS Modeler for teaching text mining.
Mia Stephens, JMP [Text Mining with JMP Pro 13: A Case Study]
Kellie Keeling, University of Denver [Comparing SPSS Modeler and Python for Teaching Text Mining]
Jean Paul Maalouf & Efthalia Anagnostou, XLSTAT [SEM Presentation] [SEM mp4 Demo]
Analysts Need 2 Speak 4 Numbers & Data to Tell Their Story
8:00 AM Sunday, November 19; Session Chair: Robert Andrews (Virginia Commonwealth University)
Abstract: Panelists address the process of storytelling from data collection to effective visualizations and dashboard development for successfully communicating the data story. Discussion includes the capabilities and shortcomings of Tableau, Excel & PowerBI, in addition to multiple forms of data visualizations & ways to avoid creating ineffective or confusing visuals.
Cory Hutchinson, Louisiana State University [Numbers do not have 2 Speak 4 Themselves]
Mark Verret, Louisiana State University [Using PowerBI and Tableau]
Mary Dunaway, University of Virginia [Teaching with Tableau® and Best Practices]
Michelle Sisto, EDHEC, Nice, France [Be a WHIZ in BIZ and VIZ]
Programming Guidance for Using R and JMP 13 Capabilities for Statistics
1:00 PM Sunday, November 19; Session Chair: Robert Andrews, Virginia Commonwealth University
Abstract:Two presentations on R, one focuses on how instructors can use swirl for teaching R, another presents an R companion that can be used for statistical analysis problems. The third presentation demonstrates how to facilitate JMP 13 use by creating Excel-like templates and user experiences.
Bob Stine, Wharton School of The University of Pennsylvania [Software for Analytics Is it time for R?]
Kazim Topuz, Oklahoma State University [Learn R in R- Swirl]
David F. Stephan, Two Bridges Instructional Technology [Programming Guidance for Using R and JMP 13 Capabilities for Statistics Instruction]
Analytics Big Picture with Detailed Workflow Steps for Addressing Real Problems
2:50 PM Sunday, November 19; Session Chair: Robert Andrews, Virginia Commonwealth University
Abstract: Consideration of what decision makers want (ought to want) from analytics; a workflow demonstration to solve real problems from data acquisition/management > EDA > Modeling > Communication/Decision making > Deployment; teaching various data mining concepts including business problem identification, selection bias, data preparation, classification with class imbalance, and model evaluation.
Cliff Ragsdale, Virginia Tech [The Value Propositionof Decision Sciences in Analytics]
Mia Stephens, JMP [Analytics: Its More than Just Modeling]
Paul Brooks, Virginia Commonwealth University [Learning Predictive Modeling with Data from Lending Club]
Getting Students to See the Forest (and not just the Trees) in Introductory
4:30 PM Sunday, November 19; Session Chair: Robert Andrews, Virginia Commonwealth University
Abstract: Introductory business analytics requires an integrated approach to learning that links the many analytics methods (the trees) to an ongoing business activity (the forest). In this session, panelists discuss the challenges in preparing learning aids to achieve this "forest" goal as well as some of the solutions they have devised.
Presenters: David M. Levine, Baruch College, City University of New York
David F. Stephan, Two Bridges Instructional Technology
Kathryn A. Szabat, LaSalle University
Experiences with Efforts to Improve Statistics Instruction
8:00 AM Monday, November 20; Session Chair: Robert Andrews, Virginia Commonwealth University
Abstract: Reports on teaching statistics using (1) principles of Ignatian Pedagogy (IPP) that requires reflection on context; (2) philosophy that statistical inference is from data to process, rather than from sample to population; and (3) active learning methods with minimal lecture that included working in groups, conducting analysis, and presenting conclusions.
Mary Malliaris, Loyola University Chicago [The Ignatian Pedagogy Paradigm (IPP) and Its Application to Statistics]
Yonggang "Tim" Lu, University of Alaska Anchorage [How to Make Sense of Business Statistics]
Bethany H. Billman, St. Edward's University & David L. Olson, University of Nebraska at Lincoln [Innovative Teaching in Undergraduate Business Statistics at the University of Nebraska at Lincoln]
Use of Computational Software Tools to Enhance Learning in Introductory
1:00 PM Monday, November 20; Session Chair: Mark L. Berenson, Montclair State University
Abstract: How can we best support course delivery in order to enhance student learning in the introductory business statistics course? This panel deals with the effective use of software Excel, Minitab, JMP, etc.). Their advantages (and disadvantages) will be discussed and specific applications will be demonstrated. Audience commentary/participation is encouraged.
Mark L. Berenson, Montclair State University [Session Overview]
Rick Hesse, Lincoln Memorial University [Spreadsheet Pros-Cons]
Christopher M. Lowery, Georgia College & State University [Analytics Preparation vs. Practice Presentation]
Robert L. Andrews, Virginia Commonwealth University Office Mix for Presentation on Excel + JMP for Intro Stats
Caucus/Business Meeting of the Data, Analytics and Statistics Instruction,
DASI, Specific Interest Group of DSI
2:50 PM Monday, November 20; Session Co-Chairs: Robert Andrews, Virginia Commonwealth University & Kellie Keeling, University of Denver
Summary: The names of 151 people were recorded for those attending the 2016 DASI sessions. 58 had attended a session at a previous DSI meeting while 93 were not on the list of the previous attendees.
There was an open discussion of the DASI sessions and about Data, Analytics and Statistics Instruction in general (More detail is in the 2017 DASI report link below). Bob Andrews and Kellie Keeling agreed to organize sessions for the 2018 DSI meeting that will be in Chicago, IL. Several others also volunteered to participate and help in various capacities.
2017 Report for the Data, Analytics and Statistics Instruction DSI Specific Interest Group
General information about Data, Analytics and Statistics Instruction, DASI, a Specific Interest Group of DSI and its predecessor, Making Statistics More Effective in Schools of Business