Online Program
Program-at-a-Glance
Keynote Address | Concurrent Sessions | Poster Sessions
Short Courses (full day) | Short Courses (half day) | Tutorials | Practical Computing Demonstrations | Closing General Session with Refreshments
Keynote Address | Concurrent Sessions | Poster Sessions
Short Courses (full day) | Short Courses (half day) | Tutorials | Practical Computing Demonstrations | Closing General Session with Refreshments
Thursday, February 19 | ||
Registration
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Thu, Feb 19, 7:00 AM - 6:30 PM
Napoleon Foyer |
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SC1 Practical Data Mining: Challenges and Solutions
Fill out evaluation |
Thu, Feb 19, 8:00 AM - 5:30 PM
Napoleon C3 |
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Instructor(s): Richard D. De Veaux, Williams College
Download Handouts |
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Large data sets (or Big Data) are becoming more common as our ability to collect and store data increases. Many new tools and methods are now available to both the experienced analyst and casual user. Unfortunately, there is a strong belief—due in large part to a series of popular Big Data books—that good results are guaranteed with just powerful algorithms and a lot of data. Instead, success is dependent on the skill and domain knowledge of the analyst and the quality and relevance of the data. However, by using principles of statistical engineering and sound statistical knowledge, the chance of success in these problems is significantly increased. Through a series of case studies, we will show how to be successful in Big Data problems. We will show applications of many current and popular algorithms, as well as when and where they are most successful.
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SC2 From Statistical Consultant to Effective Leader
Fill out evaluation |
Thu, Feb 19, 8:00 AM - 5:30 PM
Napoleon C1 |
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Instructor(s): Roger W. Hoerl, Union College; Ronald D. Snee, Snee Associates, LLC
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This workshop is designed to enhance the leadership skills of statisticians working in business, industry, and government. The goal is to help statisticians transition from being viewed as passive consultants to being viewed as proactive leaders within their organizations. Issues addressed include understanding what statistical leadership is and how it differs from consulting, why it is important to be viewed as leaders, and critical leadership skills required. As part of the course, each participant will develop a personal action plan to enhance their leadership in their own work environment.
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SC3 What Can We Learn from Software Engineers?
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Thu, Feb 19, 8:00 AM - 12:00 PM
Napoleon C2 |
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Instructor(s): Paul Teetor, Quant Development LLC
Download Handouts |
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Do any of the following problems sound familiar? Your organization is swimming in SAS or R code.You’ve saved numerous versions because you can’t afford to lose anything. People are unsure of which version is best.Testing your code is difficult.You’ve cut-and-pasted your code so often you’re seeing the same parts over and over. Everyone does their work differently, and people can’t share code easily.The code is now so convoluted that newcomers cannot understand it.The thought of major changes makes your head hurt. Software engineers have spent decades dealing with these problems, and the result is a body of best practices for managing software.These best practices are an art and not well known outside the discipline.This course will explain the techniques of software engineering and how they apply to managing your software. Topics range from code-level practices to design issues and project control.The course will focus on software engineering in the context of R, which provides a rich environment for statistical programming. Participants are expected to arrive with R and Rstudio installed on their laptops. Some familiarity with R is required.
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SC4 How to Start and Run an Independent Statistical Consulting Business
Fill out evaluation |
Thu, Feb 19, 8:00 AM - 12:00 PM
Napoleon D3 |
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Instructor(s): Stephen David Simon, P.Mean Consulting
Download Handouts |
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An independent statistical consulting job is both rewarding and challenging. If you follow this career path, you will need to learn many business skills.This course will review practical issues you will face in setting up an independent consulting business. Should you set up a limited liability corporation or a subchapter S corporation? Should you bill by the hour or the project? What insurance do you need? Should you have a standard contract in place prior to any consulting work? In addition to these legal and accounting requirements, there are human issues that you as an independent consultant will have to face. Your most important job is finding new clients. The best method, by far, is “word of mouth,” and there are several strategies you can adopt to enhance your visibility and increase the number of referrals you receive. You also need to know how to keep your current clients happy. This class will include several small-group exercises during which you will share your thoughts and experiences on how to handle specific cases involving independent statistical consulting. No specific knowledge about business models, accounting, or legal issues will be assumed.
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SC5 An Overview of Clustering: Finding and Extracting Group Structure in High-Dimensional Data
Fill out evaluation |
Thu, Feb 19, 8:00 AM - 12:00 PM
Borgne |
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Instructor(s): Rebecca Nugent, Carnegie Mellon University; Samuel Ventura, Carnegie Mellon University | ||
Clustering is the search for similar or homogeneous subgroups in a population, say, of consumers, patients, genes, images, text documents, or anything that can possibly contain group structure. For example, consumers might be divided into market segments based on their preferences and spending habits. In public health, we might be interested in predicting which outcome group a patient is likely to be in given their symptoms, past history, and current treatment. In document clustering, the goal is to group similar pieces of text (e.g., blogs, emails, posts, letters, articles, etc.) based on the words used, the frequency, and other text features. In all cases, the goal is to extract structure from potentially high-dimensional data.The difficulty, however, often lies in which clustering approach to adopt, particularly given that results are rarely independent of approach.This tutorial will give an overview of algorithmic and statistical approaches to clustering with an emphasis on how to choose an approach and its related parameter. Note that while we use the statistical software package R, these methods are available on other platforms.
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SC6 Building Your Professional Brand
Fill out evaluation |
Thu, Feb 19, 1:30 PM - 5:30 PM
Napoleon D3 |
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Instructor(s): Bill Williams, Organizational Learning Consultant
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The world of work is full of people with ambition and aspirations to do bigger things as their careers progress. While the rules for success—most of which are unwritten—vary from organization to organization, two ingredients are always essential: 1) your current performance on the job and 2) the potential other people see in you. How people view your performance and potential is derived only in part by what you know and the functional expertise you possess. The rest is based on the image you project and the exposure to other people your job affords you. In this session, we’ll examine both the impression you want others to have of you as a professional—your “brand”—and how your communications can influence the impressions of others. You will define the brand you would most like people to associate with you and consider how to manage your behavior to support your brand, particularly when communicating with senior managers and leaders.
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SC7 Design of Not-Simple Graphs
Fill out evaluation |
Thu, Feb 19, 1:30 PM - 5:30 PM
Borgne |
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Instructor(s): Richard M. Heiberger, Temple University
Download Handouts |
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Complex data analyses may require complex graphs to place the full information of the analysis into a form the intended client will be able to read. In our opinion, graphs are the heart of most statistical analyses; the corresponding tabular results are formal confirmations of our visual impressions. Data analysts are responsible for the display of data with graphs and tables that summarize and represent the data and the analysis. The graphs are often the best means of communication between the data analyst and client. This course will emphasize the design of graphical displays that best represent the message of an analysis.
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SC8 Text Analytics: Integrating Topic, Opinion, and Sentiment Analysis
Fill out evaluation |
Thu, Feb 19, 1:30 PM - 5:30 PM
Napoleon C2 |
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Instructor(s): Edward R. Jones, Texas A&M Statistical Services | ||
This workshop discusses current statistical approaches to conducting a linked analysis of reviewer comments, sentiments, and rating. Today, statisticians have powerful tools available for integrating the analysis of structured and unstructured data. Reviewer and customer comments can be used with their ratings and other background information to build models linking ratings, opinions, and emotions. Done well, this provides a more complete picture of what people think and feel about services and products.
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Collaboration Corner
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Thu, Feb 19, 2:00 PM - 5:00 PM
Napoleon AB |
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Meet in the Collaboration Corner in the front of Napoleon Ballroom. Recommend a topic or sign up for a topic recommended by someone else on the bulletin boards in this area.
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PS1 Poster Session 1 & Opening Mixer
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Thu, Feb 19, 5:30 PM - 7:00 PM
Napoleon AB |
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1 Simulating Confidential Epidemiological Data Sets
View Presentation Ragheed Fadhil Al-Dulaimi, Hunter College |
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2 Here’s How I Helped a Client Forecast Sales of Her New Product!
View Presentation Michael Latta, Coastal Carolina University YTMBA Research & Consulting |
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3 The Collaborating Statistician: Writing for Peer Review in the Scientific Literature
View Presentation Alexandra L. Hanlon, University of Pennsylvania |
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4 Effective Communication with Clients to Estimate Effect Size for Power Analysis
View Presentation Min-Kyung Jung, New York Institute of Technology College of Osteopathic Medicine |
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5 Training and Evaluating New Student Consultants at a University Consulting Center
View Presentation Aaron Rendahl, University of Minnesota |
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6 Independent Means T-test or Robust Alternatives: A Guide to Selecting the Best Tool for Inferences
View Presentation Anh P. Kellermann, University of South Florida |
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7 Optimizing Medical Chart Review Sample Size Reduction with a Monte Carlo Simulation
View Presentation Qin Wen, Humana Inc. |
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8 Testing Homogeneity of Variance in One-Factor ANOVA Models: A Plethora of Approaches to Consider
View Presentation Jeffrey D. Kromrey, University of South Florida |
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9 Economic Impact of Maternal Mortality in Africa: A Panel Data Approach
View Presentation Emmanuel Thompson, Southeast Missouri State University |
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10 Statistics in Defense
View Presentation Victoria Cox, Dstl |
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11 Clustering Box Office Score Dynamics Using Dynamic Time Warping
View Presentation Kevin Harris, NC A&T State University |
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12 Using Six Sigma to Reduce Recyclables in Trash on a College Campus
View Presentation Diane Evans, Rose-Hulman Institute of Technology |
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13 Analysis of Weather, Temporal, Population, and Socioeconomic Factors in Determining Crime Rates in Five U.S. Cities and Projections for the Future
View Presentation Zhangxin Xue, Southern Methodist University |
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Exhibits Open
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Thu, Feb 19, 5:30 PM - 7:00 PM
Napoleon AB |
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Friday, February 20 | ||
Registration
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Fri, Feb 20, 7:30 AM - 5:30 PM
Napoleon Foyer |
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Continental Breakfast
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Fri, Feb 20, 7:30 AM - 8:30 AM
Napoleon AB |
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Exhibits Open
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Fri, Feb 20, 7:30 AM - 6:30 PM
Napoleon AB |
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GS1 Keynote Address
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Fri, Feb 20, 8:00 AM - 9:00 AM
Napoleon C |
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8:05 AM |
Communication: A Two-Way Street
David Morganstein, Westat |
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CS01 Mentoring
Fill out evaluation |
Fri, Feb 20, 9:15 AM - 10:45 AM
Napoleon D1&D2 |
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Chair(s): Eric Vance, LISA, Virginia Tech | ||
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How Mentoring Can Help with the Practice of Statistics: A Panel Discussion
Sarah Kalicin, Intel corporation; Amarjot Kaur, Merck Research Labs; David Kline, The Ohio State University; David Morganstein, Westat; LeAnna Stork, Monsanto Co. |
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CS02 Special Estimation
Fill out evaluation |
Fri, Feb 20, 9:15 AM - 10:45 AM
Borgne |
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Chair(s): Wei-Ting Hwang, Univ. of Pennsylvania School of Medicine | ||
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9:20 AM |
Understanding and Estimating Treatment Effect Heterogeneity Using Adaptive Ensemble Methods
Diane M. Richardson, VA Center for Health Equity Research and Promotion |
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10:05 AM |
Much Ado About Almost Nothing: How to Deal with Limited Data
View Presentation Stephen W. Looney, Georgia Regents University |
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CS03 Predictive Analytics in Health Care
Fill out evaluation |
Fri, Feb 20, 9:15 AM - 10:45 AM
Napoleon C |
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Chair(s): Nancy Wang, Celerion | ||
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9:20 AM |
Risk Quantification for Branch Management
Momoko Fukasawa, Deloitte Touche Tohmatsu, LLC |
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CS04 Software for Analytics and Data Mining
Fill out evaluation |
Fri, Feb 20, 9:15 AM - 10:45 AM
Maurepas |
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Chair(s): Michael Latta, Coastal Carolina University YTMBA Research & Consulting | ||
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9:20 AM |
Real-Time Analytics Using Business Intelligence Software
View Presentation Sam Weerahandi, Pfizer |
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10:05 AM |
Taming the Big Data Beast at Texas Parks and Wildlife: Using Business Intelligence Tools and Value-Added Data to Evolve a Culture of Data-Driven Decisionmaking
John Taylor, Texas Parks and Wildlife |
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Refreshment Break, sponsored by Texas A&M Statistical Services
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Fri, Feb 20, 10:45 AM - 11:00 AM
Napoleon AB |
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CS05 Leadership and Influence
Fill out evaluation |
Fri, Feb 20, 11:00 AM - 12:30 PM
Napoleon D1&D2 |
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Chair(s): Jay N. Mandrekar, Mayo Clinic | ||
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11:05 AM |
Understanding and Working with Difficult People
View Presentation Colleen Mangeot, Cincinnati Children's Hospital Medical Center |
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11:50 AM |
The Influential Manager: Messages That Get Buy-In
View Presentation Bill Williams, Organizational Learning Consultant |
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CS06 Business Applications
Fill out evaluation |
Fri, Feb 20, 11:00 AM - 12:30 PM
Borgne |
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Chair(s): Qin Liu, The Wistar Institute | ||
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11:05 AM |
Business Applications of Statistical Sampling
View Presentation Laura Schweitzer, PwC |
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11:50 AM |
Applying Econometric Time Series Methods to CCAR Requirements
Kenneth Sanford, SAS Institute |
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CS07 Text Analytics and Dimension Reduction Methods
Fill out evaluation |
Fri, Feb 20, 11:00 AM - 12:30 PM
Napoleon C |
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Chair(s): Elise Roberts, Johns Hopkins University Applied Physics Laboratory | ||
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11:05 AM |
Practical Text Analytics
View Presentation Heath Rushing, Adsurgo LLC |
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11:50 AM |
Sparse Partial Robust M Regression
View Presentation Sven Serneels, BASF Corp. |
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CS08 Exploratory and Interactive Graphics
Fill out evaluation |
Fri, Feb 20, 11:00 AM - 12:30 PM
Maurepas |
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Chair(s): Jim Li, Procter & Gamble | ||
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11:05 AM |
Visualizing Data with Exploratory Data Analysis
View Presentation Wendy L. Martinez, U.S. Bureau of Labor Statistics |
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11:50 AM |
Interactive Graphics Connect People to Data—with Some R Shiny Examples
View Presentation Jean V. Adams, US Geological Survey - Great Lakes Science Center |
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Lunch (on own)
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Fri, Feb 20, 12:30 PM - 2:00 PM
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CS09 Effective Collaboration
Fill out evaluation |
Fri, Feb 20, 2:00 PM - 3:30 PM
Napoleon D1&D2 |
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Chair(s): Kim Love-Myers, Statistical Consulting Center, University of Georgia | ||
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2:05 PM |
Structuring Effective Statistical Collaborations and Consultations
View Presentation Eric Vance, LISA, Virginia Tech |
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2:50 PM |
Communicating Effectively in Statistical Collaborations and Consultations
View Presentation Heather Smith, Cal Poly |
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CS10 Special Designs
Fill out evaluation |
Fri, Feb 20, 2:00 PM - 3:30 PM
Borgne |
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Chair(s): Marie Kraska, Auburn University | ||
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2:05 PM |
Predictive Statistical Modeling of Clinical Trial Operation
View Presentation Vladimir Anisimov, Quintiles |
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2:50 PM |
Analysis Plans for Doubly Repeated Measures Designs
View Presentation Jeff Burton, Pennington Biomedical Research Center |
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CS11 Bootstrapping Applications
Fill out evaluation |
Fri, Feb 20, 2:00 PM - 3:30 PM
Napoleon C |
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Chair(s): Phillippa Spencer, Dstl | ||
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2:05 PM |
Bootstrapping Time Series Data
View Presentation Paul Teetor, Quant Development LLC |
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2:50 PM |
Bootstrapping Confidence Intervals for Effect Sizes (and Other Weird Things)
View Presentation Erin Smith, University of Louisville |
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CS12 Statistical Plans and Charts
Fill out evaluation |
Fri, Feb 20, 2:00 PM - 3:30 PM
Maurepas |
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Chair(s): Mary J. Kwasny, Northwestern University | ||
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2:05 PM |
Best Practice: The Data Analysis Plan—A Blueprint for Success
View Presentation Kathleen A. Jablonski, The Biostatistics Center, The George Washington University |
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2:50 PM |
Using Charts to Present Your Results to Nonstatisticians
View Presentation Jay Arthur, KnowWare International |
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Refreshment Break, sponsored by Texas A&M Statistical Services
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Fri, Feb 20, 3:30 PM - 3:45 PM
Napoleon AB |
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CS13 Career Development
Fill out evaluation |
Fri, Feb 20, 3:45 PM - 5:15 PM
Napoleon D1&D2 |
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Chair(s): Shelley Brock Roth, Westat | ||
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3:50 PM |
Career Development Opportunities for Statisticians
View Presentation LeAnna Stork, Monsanto Co. |
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4:35 PM |
G.R.O.W.—An Empowering Model for Career Success
View Presentation Colleen Mangeot, Cincinnati Children's Hospital Medical Center |
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CS14 Interpretation
Fill out evaluation |
Fri, Feb 20, 3:45 PM - 5:15 PM
Borgne |
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Chair(s): Zoran Bursac, University of Tennessee Health Science Center | ||
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3:50 PM |
Statistical Methods for Bridging the Gap Between Interpretative and Predictive Analysis
View Presentation Michael Regier, WVU, Department of Biostatistics |
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4:35 PM |
Using the Delta Method to Generate Means and Confidence Intervals from a Linear Mixed Model on the Original Scale, When the Analysis Is Done on the Log Scale
View Presentation Brandy R. Sinco, University of Michigan School of Social Work |
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CS15 Social Media Applications
Fill out evaluation |
Fri, Feb 20, 3:45 PM - 5:15 PM
Napoleon C |
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Chair(s): Pete Doe, Nielsen | ||
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3:50 PM |
Garbage in Garbage Out: Acquisition and Quality Assessment of Social Media Data in Health Research
Yoonsang Kim, University of Illinois at Chicago |
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4:35 PM |
Use of Social Media Data as a Lead Indicator to Predict Retail Sales Performance
Li Zhang, Alliance Data Systems |
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CS16 Dynamic Reporting Tools
Fill out evaluation |
Fri, Feb 20, 3:45 PM - 5:15 PM
Maurepas |
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Chair(s): Alex Gilgur, Google | ||
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3:50 PM |
Creating an Easy-to-Use, Dynamic, Flexible Summary Table Macro with P-values in SAS for Research Studies
View Presentation Amy Arlene Gravely, VA Medical Center, CCDOR |
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4:35 PM |
Reporting Results with R, R Markdown, and Shiny
Garrett Grolemund, RStudio, Inc. |
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PS2 Poster Session 2 & Refreshments
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Fri, Feb 20, 5:15 PM - 6:30 PM
Napoleon AB |
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1 Comparing Linear Mixed Models Between Statistical Software
View Presentation Danielle Guffey, Baylor College of Medicine |
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3 Developing Successful Career Relationships: Mentoring, Coaching, and Sponsorship
View Presentation Sarah Kalicin, Intel Corporation |
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4 Please Don't Shoot the Messenger: Delivering Negative Results
View Presentation David R Bristol, Statistical Consulting Services Inc |
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5 How to Apply Missing Data Techniques in Practice
Katherine M. Wright, Loyola University Chicago, Northwestern University |
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6 Piece-Wise Mixed Effect Model for Renal Function Data Analysis in Transplantation Patients
View Presentation Zailong Wang, Novartis Pharmaceutics |
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7 Sample Size Estimation for Multiply Matched, Noninferiority Case-Control Studies with Binary Exposures
View Presentation Charles Gene Minard, Baylor College of Medicine |
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8 Classification of Time Series Using Similarity Analysis
David J. Corliss, Wayne State University, Ford Motor Company |
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9 Viewing Problems Through a Multilevel Modeling Lens
View Presentation Paul Roback, St. Olaf College |
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11 Liability Survival Analysis
Joseph Michael, Deloitte |
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12 Type 3 Statistics in SAS Procedures: What Do They Really Mean?
View Presentation Leann Myers, Tulane University |
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13 Investigating Earthquake Magnitude Interdependency Through Stochastic Declustering
View Presentation Devon Osgood Cook, California State University, Fullerton |
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14 Understanding Change Through Different Methodological Lenses
View Presentation Jie Liao, Alliance Data Systems, Inc. |
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15 A Comparison of the Forecast of Pork Carcasses Futures by Three Methods: A SETAR Model, a Seasonal ARIMA Model, and Holt-Winters Smoothing
Gustavo Ramirez-Valverde, Colegio de Postgraduados |
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16 Adjusting Survey Mode Differences: Illustration of a Linear Equating Method
View Presentation Sangeeta Agrawal, Gallup |
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Saturday, February 21 | ||
Registration
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Sat, Feb 21, 7:30 AM - 2:30 PM
Napoleon Foyer |
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Exhibits Open
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Sat, Feb 21, 8:00 AM - 1:00 PM
Napoleon AB |
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PS3 Poster Session 3 & Continental Breakfast
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Sat, Feb 21, 8:00 AM - 9:15 AM
Napoleon AB |
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1 Are You Really Who We Think You Are? Recognizing and Controlling Biases in Statistical Analyses of Linked Data
View Presentation Sigurd Wilson Hermansen, Westat |
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2 Predicting Buying Behavior: IT Software Customer Clustering with R and Weka
View Presentation Emiliana Inez Patlan, SolarWinds |
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3 Technicians to Collaborators: Changing the Paradigm of Student Consulting at the University of Georgia
View Presentation Kim Love-Myers, Statistical Consulting Center, University of Georgia |
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4 Communicating Applied Statistics Through Online Courses and Consulting
View Presentation James Landis Rosenberger, Penn State University |
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5 Enrollment Modeling with Random Staggered Site Start-Up Times
View Presentation Bradley Thomas Ferguson, Quintiles |
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6 Variability, Redundancy, and Reduction Using Principal Components Analysis
View Presentation Marie Kraska, Auburn University |
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8 Multiple Imputation for Missing Data in Longitudinal Research Synthesis: Identifying and Overcoming Assumptions in Software
David Kline, The Ohio State University |
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9 Partial Least Squares Structural Equation Modeling as an Analysis Tool in Epidemiological Studies
View Presentation kaushal Raj Chaudhary, Sanford Research |
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10 Experimental Design for Testing with Multiple Segments
View Presentation Jinguo Gao, Dr. |
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11 Lessons Learned from Observational Studies: Considerations in Propensity Score Matching
View Presentation Adin-Cristian Andrei, Northwestern University |
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12 Modeling Assessment Data with a Hierarchical Approach
View Presentation Jimmy Wong, California Polytechnic State University, San Luis Obispo |
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13 An Empirical Investigation of the Impact of Measurement Error on Propensity Score Analysis
View Presentation Patricia Rodríguez de Gil, University of South Florida |
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14 The Strength of Combining Public and Restricted Data: Tips for Using the Research Data Center (RDC)
View Presentation Ellen E. Bishop, RTI, International |
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CS17 Presenting to Executives and Other Non-Statisticians
Fill out evaluation |
Sat, Feb 21, 9:15 AM - 10:45 AM
Napoleon D1&D2 |
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Chair(s): Felicia Hardnett, Centers for Disease Control and Prevention | ||
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9:20 AM |
Communications to Boards of Directors and Nonstatisticians
Joyce Nilsson Orsini, Fordham University Graduate School of Business |
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10:05 AM |
Coming Out of the Casket: Techniques for Becoming a More Effective Speaker
Eric Stephens, Vanderbilt University Medical Center |
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CS18 Method Reviews
Fill out evaluation |
Sat, Feb 21, 9:15 AM - 10:45 AM
Borgne |
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Chair(s): Dennis Lee Eggett, Brigham Young University | ||
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9:20 AM |
Speed Dating with Regression Methods
David J. Corliss, Wayne State University, Ford Motor Company |
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10:05 AM |
Are Data Science and Analytics Just New Names for Statistics?
View Presentation Peter Bajorski, Rochester Institute of Technology |
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CS19 Analytics & Big Data Survey Review & Interpretation – Panel & Audience Discussion
Fill out evaluation |
Sat, Feb 21, 9:15 AM - 10:45 AM
Napoleon C |
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Chair(s): K. Blayne Easter, The Vanguard Group | ||
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9:20 AM |
Analytics and Big Data Survey Review and Interpretation: Panel and Audience Discussion
Edward R. Jones, Texas A&M Statistical Services; Amarjot Kaur, Merck Research Labs; Elizabeth Kolodziej, Texas A&M University; Heath Rushing, Adsurgo LLC; F. Michael Speed, SAS Institute Inc |
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CS20 Effective Graphs
Fill out evaluation |
Sat, Feb 21, 9:15 AM - 10:45 AM
Maurepas |
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Chair(s): Lasonja Kennedy, Independent Consultant | ||
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9:20 AM |
Correspondence Analysis
View Presentation Jessica Thomson, USDA Agricultural Research Service |
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10:05 AM |
How to Avoid Some Common Graphical Mistakes
View Presentation Naomi B. Robbins, NBR |
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Refreshment Break, sponsored by Texas A&M Statistical Services
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Sat, Feb 21, 10:45 AM - 11:00 AM
Napoleon AB |
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CS21 Ethics and Impact
Fill out evaluation |
Sat, Feb 21, 11:00 AM - 12:30 PM
Napoleon D1&D2 |
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Chair(s): Steven B. Cohen, Agency for Healthcare Research and Policy | ||
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11:05 AM |
Teaching Ethics in Statistical Consulting
View Presentation Alan C. Elliott, Southern Methodist University |
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11:50 AM |
Enhancing Communication Skills for Making Organizational Impact and Career Development
View Presentation Jay N. Mandrekar, Mayo Clinic |
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CS22 Trial Enrollment
Fill out evaluation |
Sat, Feb 21, 11:00 AM - 12:30 PM
Borgne |
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Chair(s): Runhua Shi, LSU School of Medicine | ||
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11:05 AM |
Modeling Enrollment with Random Staggered Site Start-Up Times
View Presentation Bradley Thomas Ferguson, Quintiles |
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11:50 AM |
Enrollment, Events Prediction, and Statistical Power Prediction for Event-Driven Trials
Vladimir Anisimov, Quintiles |
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CS23 Population Modeling
Fill out evaluation |
Sat, Feb 21, 11:00 AM - 12:30 PM
Napoleon C |
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Chair(s): Mary Sailors, Chevron | ||
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11:05 AM |
Bayesian Spatial Joint Modeling of Asthma Admission and Readmission for Identifying High-Risk Neighborhood
View Presentation Bin Huang, Cincinnati Children's Hospital Medical Center |
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11:50 AM |
Determining Different Population Distributions Using NHANES BMI Data
View Presentation William Johnson, Pennington Biomedical Research Center |
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CS24 Special Uses of R
Fill out evaluation |
Sat, Feb 21, 11:00 AM - 12:30 PM
Maurepas |
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Chair(s): Bryan Stanfill, CSIRO | ||
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11:05 AM |
Sparse Matrix Computation in R with an Application to GEEs
View Presentation Lee S. McDaniel, LSUHSC, School of Public Health |
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11:50 AM |
Using R in a Regulated Environment
Keaven M. Anderson, Merck Research Laboratories |
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Collaboration Corner
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Sat, Feb 21, 2:00 PM - 4:00 PM
Napoleon AB |
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Meet in the Collaboration Corner in the front of Napoleon Ballroom. Recommend a topic or sign up for a topic recommended by someone else on the bulletin boards in this area.
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PCD1 Interactive Predictive Modeling with JMP 12 Pro: Keeping It in the Flow
Fill out evaluation |
Sat, Feb 21, 2:00 PM - 4:00 PM
Napoleon C3 |
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Instructor(s): Mia L. Stephens, JMP Division of SAS; Scott Lee Wise, SAS Institute, JMP Division | ||
Interactive predictive modeling in JMP Statistical Software from SAS is more than building models. It allows you to take advantage of interactive and dynamic graphs and advanced analytic tools, keeping data visualization, analysis, and modeling in the flow. In this talk, we will use case studies to see how to explore and prepare data using the Column Switcher, Data Filter, Recode, and Graph Builder. We will use the Partition platform, Fit Model, and Generalized Regression platforms, as well as tools such as the Prediction Profiler and the Solution Path in JMP Pro 12, to interactively explore parameters and select potential models. Finally, we’ll compare a variety of competing models using Model Comparison.
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PCD2 Tessera: Open Source Tools for Big Data Analysis in R
Fill out evaluation |
Sat, Feb 21, 2:00 PM - 4:00 PM
Borgne |
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Instructor(s): Landon Sego, Pacific Northwest National Laboratory; Amanda White, Pacific Northwest National Laboratory | ||
Tessera is a set of R-based tools to enable data scientists to explore and analyze large, complex data.The Tessera computational environment is powered by divide and recombine (D&R), an approach for dividing data into subsets and computing on them in parallel. At the front end, the analyst programs in R. At the back end is a distributed parallel computa- tion environment such as Hadoop. In between are three Tessera packages: DataDR,Trelliscope, and RHIPE.The DataDR R package provides a high-level interface to D&R operations, making specification of divisions, analytic methods, and recombinations easy.The interface is designed to be back end agnostic, so it can harness new distributed computing tech- nologies as needed.Trelliscope is a scalable visualization tool in which data sets are divided into subsets and a visualization method is applied to each subset and shown in a multi-panel trellis display.This framework has proven to be a powerful mechanism for all data, large and small. RHIPE is the R and Hadoop Integrated Programming Environment. RHIPE allows an analyst to run Hadoop MapReduce jobs from within R. RHIPE is used by DataDR when the back end is Hadoop.
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PCD3 Mathematica and Statistical Computing
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Sat, Feb 21, 2:00 PM - 4:00 PM
Napoleon D1 |
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Instructor(s): Michael Kelly, Wolfram Research Inc. | ||
Mathematica is the world’s leading symbolic and numerical software, pioneering the use of symbolic functional programming for the representation of mathematical, statistical, and computational objects in a universal, consistent, and high-level language that has allowed for a systematic treatment of the entire area of statistical analysis. Unlike other statistical programs that are mainly numerical, Mathematica combines the many advantages of symbolic representation of mathematical statistics with the numerical capabilities of advanced and novel algorithms. See www.wolfram.com/solutions/industry/statistics.
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PCD4 Rating College Football Teams: A Case Study on Integrating Minitab with Statistical Programming Languages
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Sat, Feb 21, 2:00 PM - 4:00 PM
Napoleon D2 |
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Instructor(s): Daniel Griffith, Minitab; Eduardo Santiago, Minitab, Inc. | ||
College football is a sport with highly variable outcomes and teams that play highly unbalanced schedules due to conference affiliation, a large pool of potential opponents, and incentives that disfavor competitive balance. Despite these difficulties, it is highly desirable for fans, media, and the playoff selection committee to rate teams as accurately as possible. Using an unconventional method, the case study demonstrates how teams can be rated with minimal effect from uncontrollable aspects of the game.The method is performed using a combination of Minitab Statistical Software for its ease of use and graphical capabilities integrated with a statistical programming language for complex routines.
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T1 A Case Study in Big Data Analytics
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Sat, Feb 21, 2:00 PM - 4:00 PM
Maurepas |
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Instructor(s): Patrick Hall, SAS Enterprise Miner
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So what exactly do you do when faced with a huge data set from which you are to derive insights? This happens in banks, insurance companies, government agencies, manufacturing centers, and other institutions all the time. This tutorial illustrates best practices for mining large data sets in the context of a case study. Participants will learn real-world techniques to explore and preprocess data; to select, extract, and engineer the most predictive features; to build the best predictive model for the job at hand; and to leverage predictive analytics to make decisions for their organization. Instructors also will point out common pitfalls and trade-offs inherent to contemporary Big Data approaches. SAS Enterprise Miner will be used for the analyses, but the focus will be on the methods and not the software. Participants will have access to the example data for further study.
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T2 An Introductory Tutorial on Mixed Models
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Sat, Feb 21, 2:00 PM - 4:00 PM
Napoleon C1 |
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Instructor(s): Funda Gunes, SAS Institute Inc.
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Mixed model analysis is one of the cornerstones of modern statistics. It extends the general linear model for independent and equivariant data by allowing a more flexible covariance for the error term. Using mixed models, you can fit models to a variety of data that follow the normal distribution, including repeated measurements and data from a randomized block design. This tutorial introduces the basics of mixed model methodology and illustrates the analysis of linear mixed models in typical applications, with numerous examples using the MIXED procedure in SAS/STAT software. This tutorial also includes an overview of other mixed modeling procedures in SAS, giving a brief introduction to analyzing generalized linear models by using the GLIMMIX procedure and discussing the scenarios in which you would use the nonlinear mixed models and NLMIXED procedure. Prerequisites are a working knowledge of the general linear model and basic matrix algebra.
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T3 Speak & Connect: Harnessing PowerPoint
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Sat, Feb 21, 2:00 PM - 4:00 PM
Napoleon D3 |
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Instructor(s): Andrew Causley, Speak & Connect
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Data-heavy presentations can overload an audience with information quickly, causing them to tune out. Learn how to create and deliver PowerPoint presentations that are interesting, effective, and memorable! It’s a fresh approach, one that combines information with effective visuals and personal engagement to connect with an audience in a credible and captivating manner. If you can answer YES to any of the following questions, you should attend this tutorial. Have some of your slides been loaded with text, bullet points, or complex data? Have there been times when you’ve read off your slides? Has your audience ever looked bored, inattentive, or asleep? Learn how to share data and information and create better decks.
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T4 Tutorial on Parallel Programming in R
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Sat, Feb 21, 2:00 PM - 4:00 PM
Napoleon C2 |
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Instructor(s): Miranda Fix, Colorado State University; Josh Hewitt, Colorado State University; Henry Randall Scharf, Colorado State University
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This tutorial introduces participants to high-performance computing in R for analyzing research data and developing practical analytics. R is a free, open source programming language that concisely suppor ts a wide range of statistical computing and machine learning needs. Modern data sets are large and computational procedures can be intense, which may become prohibitive to practical data analysis projects. This tutorial introduces participants to workflows and packages that let practitioners use R to take advantage of the power of modern computing resources like multicore architectures and cloud technologies. Applications and examples include demonstrating parallel forms of popular classic and machine learning methods, using bootstrapping and cross validation to estimate uncertainty and accuracy, simulating data to analyze “what if” scenarios, and discussing related topics. Demonstrations will be presented with R. Attendees are encouraged to bring laptops with R installed so they may follow along and experiment with these tools.
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Refreshment Break, sponsored by Texas A&M Statistical Services
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Sat, Feb 21, 4:00 PM - 4:15 PM
Napoleon AB |
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GS2 Closing General Session
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Sat, Feb 21, 4:15 PM - 5:30 PM
Napoleon C3 |
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CSP Steering Committee chair, Sylvia Miller Dohrmann, and vice chair, Jim Rutherford, will lead a panel of CSP committee members as they summarize the conference and gather your feedback. Each panelist will speak for five minutes to share their conference experience. Discussion will then be extended to the audience for Q&A and feedback on how well the overall objectives of the conference were met, including areas of improvement for the future. The closing session is also a great time to let members of the CSP Steering Committee know if you are interested in helping out with future conferences.
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