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Thursday, February 20
Thu, Feb 20, 7:00 AM - 6:00 PM
Galleria B

SC1 Enhancing Big Data Projects Through Statistical Engineering
Thu, Feb 20, 8:00 AM - 5:00 PM
Bayshore V
Instructor(s): Richard De Veaux, Williams College; Roger Wesley Hoerl, Union College; Ron Snee, Snee Associates

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Massive data sets, or Big Data, have become more common recently, due to improved technology for data acquisition, storage, and processing of data. New tools have been developed to analyze such data, including classification and regression trees (CART), neural nets, and methods based on bootstrapping. These tools make high-powered statistical methods available to not only professional statisticians, but also to casual users. As with any tool, the results to be expected are proportional to the knowledge and skill of the user, as well as the quality of the data. Unfortunately, much of the professional literature may give casual users the impression that if one has a powerful enough algorithm and a lot of data, good models and good results are guaranteed at the push of a button.

Conversely, if one applies sound principles of statistical engineering to the Big Data problem, several potential pitfalls become obvious. We consider the consequences of four major issues: 1) lack of a disciplined approach to modeling, 2) use of “one shot studies” versus sequential approaches, 3) assuming all data are high-quality data, and 4) ignoring subject matter knowledge.

Outline & Objectives

Uniqueness of Big Data Projects
-Relative to traditional statistics projects
New Methods for Big Data
What Could Go Wrong?
-Big blunders with Big Data
-Sequential approaches versus one-shot studies
-Integration of analytics with sound subject matter theory
-Data quality
How Statistical Engineering Can Help
-Brief review of statistical engineering
-Theory and key principles
-Building blocks of statistical engineering (major phases)
Application of Statistical Engineering to Big Data
-Breakout exercises
Recap and Summary
-What have we learned?

• Develop a clear understanding of Big Data projects; what is new and unique versus what is not.
• Develop the skills and confidence needed to attack Big Data projects in a logical, sequential manner, applying the phases and principles of statistical engineering
• Clarify how easily significant blunders that can occur in Big Data projects if fundamentals are ignored.
• Develop a solid understanding of the theory and practice of statistical engineering.

About the Instructor

Ron Snee, Dick De Veaux, and Roger Hoerl each have significant track records within ASA, and the profession in general. De Veaux has given numerous presentations on data mining, and published in that area extensively. Snee and Hoerl are primarily responsible for the development of statistical engineering as a discipline, and again, have published extensively on that topic. By partnering on this workshop, they bring a unique set of skills and experiences to the issue of enhancing Big Data projects through application of statistical engineering principles. In an effort to keep the level of documentation reasonable, we have not included vitas; these can be forwarded if needed.

Relevance to Conference Goals

We feel that this workshop relates quite well to all three of the major conference themes. Obviously, it relates directly to Big Data analytics, in that participants will be learning tangible skills that they can apply to their current or future Big Data projects. In addition, application of statistical engineering principles – especially on large, complex, unstructured problems - enhances the leadership skills of statisticians, and thereby provides a vehicle for career advancement. These principles can also be applied to enhance more traditional modeling and analysis projects, which is the third major theme.

SC2 Design and Analysis of Experiments Using Generalized Linear Mixed Models
Thu, Feb 20, 8:00 AM - 5:00 PM
Bayshore I
Instructor(s): Elizabeth Claassen, University of Nebraska; Walt Stroup, University of Nebraska

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Course presents applications of generalized linear mixed models (GLMMs). Focus is especially on GLMMs for design and analysis of experiments with non-normal data. Material is at an applied level, accessible to those familiar with linear models.

Participants will learn that GLMMs are an encompassing family and understand the differences and similarities in estimation and inference within the family. We discuss issues in working with correlated, non-normal data such as overdispersion, marginal and conditional models, and model diagnostics. We present GLMMs for common non-normal response variables—count, binomial and multinomial, time-to-event, continuous proportion—in conjunction with common designs—blocked, split-plots, repeated measures. Numerous examples will be presented.

The afternoon continues with GLMM applications and associated issues, including comparison of estimation methods, computation of power and sample size, model selection, and inferential tasks with and without adjustments.

Numerous examples will be used to illustrate all topics. Examples use tools in SAS/STAT and R, but the principles should be applicable to any GLMM-capable software.

Outline & Objectives

1. From Linear Model to GLMM
A. General Setting
B. Linear Models and Linear Mixed Model (LM, LMM)
C. Generalized Linear Model (GLM)
D. Generalized Linear Mixed Model (GLMM)
2. Marginal or Conditional Models
A. Defining a Model from Design Properties
B. Overdispersion and Other Design-Induced Issues
C. G- and R-side Random Effects
D. GEE versus GLMM
E. Distributional Implications
3. Estimation
A. (Restricted) Maximum Likelihood
B. Quasi-Likelihood/GEE
C. Pseudo-Likelihood
D. Laplace and Quadrature
E. Model-Based and Empirical (“Sandwich”) Estimators
4. Rates and Proportions
A. Distributions
B. Binomial Proportions
C. Binary Data
D. Multinomial
E. Beta – Continuous Proportions
5. Counts
A. Distributions
B. Poisson or Negative Binomial?
C. Modeling with Offsets
D. Zero-inflated Models
6. Within-Subject Correlation
A. Repeated Measures Background
B. Review of Methods for Normally-Distributed Data
C. Extension to Non-Normal Data – Similarities and Differences
D. Spatial Variation
7. Power, Precision and Sample Size
A. Background
B. Power & Sample Size for Continuous, Count, and Binomial Data
C. Comparing Competing Designs using GLMM tools
D. Longitudinal & Spatial Data

About the Instructor

Walt is Professor in the Department of Statistics at the University of Nebraska-Lincoln. He teaches statistical modeling and design of experiments. His research concerns mixed model applications collaborating with agricultural, natural resource, medical, pharmaceutical science, education, and the behavioral science. He participated in a multi-state mixed model project that motivated the development of SAS PROC MIXED. He co-authored textbooks on SAS for linear models, SAS for mixed models, and GLMMs for Plant and Natural Resource Sciences. He authored Generalized Linear Mixed Models: Modern Concepts, Methods and Applications (2013).

Elizabeth is a doctoral candidate completing her dissertation concerning bias correction for variance component estimation in GLMMs. With Walt, she has co-taught UNL’s graduate-level design of experiments course and assisted with the advanced modeling course. She has been primary instructor of introductory undergraduate statistics courses and has consulted with researchers from a variety of disciplines. She worked at JMP testing new applications. She presented preliminary results of her research at JSM 2013. Elizabeth’s expects to graduate May, 2014.

Relevance to Conference Goals

Modeling in the 21st century has grown beyond the basic ANOVA and linear model as most people learned them. This is especially true when the data cannot reasonably be assumed to be normally distributed. When data aren’t normal, why should your statistical analysis be? With the continued improvements in computing power and a wide variety of software options, the ability to model complex experiments and quasi-experiments with a variety of response variable distributions not limited to Gaussian is a required skill for data analysts. This course will introduce (or provide a refresher to) generalized linear mixed models as an overarching family that contains all of its predecessors to participants. The course will familiarize participants with the essential thought-processes required to use GLMMs effectively and with a wide variety of practical applications.

SC3 Elegant R Graphics with ggplot2
Thu, Feb 20, 8:00 AM - 5:00 PM
Palma Ceia III
Instructor(s): Isabella R. Ghement, Ghement Statistical Consulting Company Ltd.

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R comes equipped with several packages for producing elegant graphics, and ggplot2 is one of the most powerful and versatile of these packages. This one-day course will provide participants with an in-depth introduction to ggplot2 in the context of graphics production for exploratory and confirmatory data analyses. Participants will learn how to use ggplot2 to produce, customize, and export publication-quality graphics that facilitate the communication of data-driven insights. In particular, participants will gain an understanding of the ggplot2 philosophy, syntax, and capabilities; learn how to create standard and advanced statistical graphs; and become skilled at customizing graphs through the addition of labels, titles, symbols, colors, legends, scales, annotations, layers, and themes. Participants also will learn how to combine the presentation of numerical and visual data summaries in the same graph, save ggplot2 output in a variety of standard graphical formats, and embed this output in automated reports and presentations. This hands-on course will offer participants the opportunity to practice the use of ggplot2 in real time. Participants are required to have basic knowledge of R and bring their laptops pre-installed with R and ggplot2.

Outline & Objectives

The outline for this course is as follows:

1) Overview of R Studio (e.g., installation, menus, workflow);
2) Basics of ggplot2 (e.g., philosophy, capabilities, syntax);
3) Producing standard graphics with ggplot2 (e.g., histograms, boxplots, scatter plots, time series plots, bar charts);
4) Producing advanced graphics with ggplot2 (e.g., scatterplot matrices, conditional plots obtained through faceting);
5) Customizing graphics constructed with ggplot2 (e.g., labels, titles, symbols, colours, legends, scales, annotations, layers and themes);
6) Saving graphics constructed with ggplot2 in a variety of formats (e.g., pdf, jpeg, png);
7) Embedding ggplot2 graphics in automated reports and presentations.

The objective of this course is to teach participants how to produce, customize and export publication-quality graphics using R Studio and ggplot2. By exploiting ggplot2's power and versatility, participants will be able to create elegant, complex, multi-layered statistical graphics which will facilitate the communication of data-driven insights.

About the Instructor

Dr. Isabella R. Ghement is an independent statistical consultant and trainer based in Vancouver, Canada, British Columbia. She has extensive R training experience and presented the course "A Crash R Course on Statistical Graphics" at the ASA Conference on Applied Statistical Practice held in New Orleans in February 2013. Since 2007, Dr. Ghement has taught on a yearly basis an advanced regression course using R to graduate students in the Sauder School of Business at the University of British Columbia. Dr. Ghement’s statistical consulting clients include federal and provincial government agencies, contract research organizations and academic researchers. Her research expertise covers areas such as partially linear regression modeling, robust regression modeling and mixed treatment comparisons.

Relevance to Conference Goals

This course falls under the umbrella of the following conference theme: Theme 4 (Software and Graphics). As such, the course will help participants to master and employ modern data visualization methods using the R Studio software and the R graphical package ggplot2. The course will also encourage participants to adhere to good principles of reproducible research when producing automated reports and presentations which include graphical output.

SC4 Career Development Within Your Organization
Thu, Feb 20, 8:00 AM - 12:00 PM
Palma Ceia IV
Instructor(s): William Williams, Organizational Learning Consultant

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There are two fundamental keys to successfully pursuing opportunities within your organization: self-knowledge and the ability to represent your capabilities to other people. This workshop will help you with both. Through an assessment, you’ll identify and describe specifically what you’re good at and where your strongest interests and skills lie. This will allow you to make sound decisions about where to focus your energies for enhancing your career. We will include information about how to network effectively within your organization to locate pockets of opportunity or identify potential guides and mentors.

Outline & Objectives

1. Identify your top interest and skill areas;
2. Determine the contributions those can make in your current organization - and areas within your organization that align with those skills and interests.
3. Learn to network effectively with people in those areas of your organization so that you can best position yourself to find new opportunities that support your organization's objectives.

About the Instructor

Bill Williams is an Organizational Learning Consultant and has been a part of the Conference of Statistical Practice since its inaugural year.

Relevance to Conference Goals

This session will both help participants learn how to better navigate their career and learn how to have a more positive impact on their organization.

SC5 Modern Regression for Big Data Problems
Thu, Feb 20, 8:00 AM - 12:00 PM
Bayshore VI
Instructor(s): Simon J. Sheather, Texas A&M University

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In the past, regression applications have focused on modeling relationships based upon a relatively small amount of data. Many of these arise from statistically designed experiments or field trials. However, regression modeling is being applied increasingly to problems involving massively large and complex data and retrospective data collected routinely by businesses and government organizations. Does this change the approach statisticians take to modeling using regression techniques?

This workshop explores this question and provides concrete, practical advice for applying modern regression to solving big data problems. The presenter is author of A Modern Approach to Regression with R.

Outline & Objectives

1. Challenges and Issues for Applying Regression Modeling to Big Data Problems.

2. Practical Approaches and Advice on Using Regression Modeling in Modern Applications.

3. Modeling average airline ticket price across more than 5000 routes in the USA.

4. Modeling interest in NFL games using social media and other data.

5. Modeling loan defaults.- a case study involving logistic regression.

About the Instructor

Professor Sheather brings a wide scope of experience in both management and the integration of analytics into organizations and businesses. He received his BSc (Hons) degree from Melbourne and a Ph.D. in Statistics from La Trobe. Currently Simon is Professor and Head of the Statistics Department at Texas A&M University. Previously he was a faculty member at the Australian Graduate School of Management at the University of New South Wales.

Simon has over 20 years of experience applying analytics and statistical methods in business. His clients included banks, biotechnology, hospitality service companies, fashion, transportation, real estate, consumer products and government. During this time he published over 75 papers and 2 books. He is listed on the website among the top one-half of one percent of all mathematical scientists for citations of published work.

Relevance to Conference Goals

The conference attracts statisticians involved in the practice of statistics in companies and organizations internationally. This workshop discusses how the application of a well known tool in statistical practice can be used to address modeling issues involving big data. It also discusses modern regression modeling can be misused when applied to big data problems.

SC6 Practical Bayesian Computation Using SAS
Thu, Feb 20, 8:00 AM - 12:00 PM
Bayshore VII
Instructor(s): Fang Chen, SAS Institute Inc.

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This half-day course reviews the basic concepts of Bayesian inference and focuses on the practical use of Bayesian computational methods. The objectives are to familiarize statistical programmers and practitioners with the essentials of Bayesian computing and equip them with computational tools through a series of worked-out examples that demonstrate sound practices for a variety of statistical models and Bayesian concepts.

The first part of the course provides a gentle introduction to Bayesian inference and covers the fundamentals of prior distributions and concepts in estimation. The course also will cover MCMC methods and related simulation techniques, emphasizing the interpretation of convergence diagnostics in practice.

The second part of the course involves applications using Bayesian capabilities in SAS/STAT software in the GENMOD, LIFEREG, PHREG, and FMM procedures. Examples will include methods such as linear regression, generalized linear models, survival analysis, and finite mixture models.

The third part of the course takes a topic-driven approach to cover broad Bayesian topics such as random-effects models, sensitivity analysis, prediction, and model assessment.

Outline & Objectives

Part I - Introduction to Bayesian statistics (30 to 40 minutes)
A. Concepts in Bayesian Methods
a. Motivations and Difference between Classical and Bayesian
b. Estimation (point and interval)
c. Prior Distributions
B. Computational Methods
a. Markov Chain Monte Carlo
b. Metropolis and Gibbs Samplers
C. Convergence Diagnostics
a. Terminologies
b. Diagnostics Tests
c. Visualization
d. Assessing Simulation Variability

Part II - Bayesian Computation using SAS (1 hour)
A. Introduction
B. Procedures with Bayesian Capabilities
d. FMM
C. Statistical Models and Topics (not necessarily in these order)
a. Linear Regression
b. Generalized Linear Model
c. Cox Regression and Piecewise Exponential Model
d. Frailty Model
e. Finite Mixture Model

Part III - Additional Bayesian Topics
A. Primer on PROC MCMC
B. Statistical Topics (not necessarily in these order)
a. Inference on Functions of Paramete

About the Instructor

Fang Chen is a Senior Manager of Bayesian Statistical Modeling and a
member of Advanced Analytics Division at SAS Institute Inc. Among his
responsibilities are development of Bayesian analysis software and MCMC procedure. He has written about Bayesian modeling using the MCMC
procedure and taught continuing education course on practical Bayesian
computation at JSM. Prior to joining SAS Institute, he received his
degree in statistics from Carnegie Mellon University in 2004.

Relevance to Conference Goals

Attendees will understand basic concepts and computational methods of
Bayesian statistics, and how to deal with practical issues that arise
from Bayesian analysis. Attendees will also be able to program using
SAS/STAT procedures with Bayesian capabilities to implement various
Bayesian models.

SC7 An Introduction to R for Data Analysts
Thu, Feb 20, 1:00 PM - 5:00 PM
Bayshore VI
Instructor(s): Robert Kabacoff, Management Research Group

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R has become one of the most popular languages for data analysis and graphics. This course will provide a practical introduction to this comprehensive platform. Participants will learn to import data into R from a variety of sources; clean, recode, and restructure data; and apply R’s many functions for summarizing, modeling, and graphing data. Both basic and more advanced forms of data analysis will be covered. Additional topics include navigating R’s comprehensive help systems, practical advice for processing data, common programming mistakes to avoid, and useful functions for data mining.

Outline & Objectives

I. Introduction – An introduction to R: R syntax and data structures; working interactively and in batch; alternative IDEs and GUIs; adding functionality through packages; common programming mistakes; getting unstuck – were to find answers to your questions.
II. Data Management – Importing, cleaning, and reformatting data: transforming and recoding variables; subsetting, merging, and aggregating data; control structures; user-written functions.
III. Graphics – Taking advantage of R’s powerful graphics: creating basic and advanced graphs; customizing and combining graphs; innovative methods for visualizing complex data.
IV. Statistical Analysis and Data Mining – Using R for description, prediction, and classification: descriptive statistics and multi-way tables; ANOVA variants; regression (e.g., linear, logistic, Poisson), classification trees, cluster analysis, and other multivariate methods; dealing effectively with missing data; Going further.

About the Instructor

Dr. Robert Kabacoff has twenty five years of experience teaching and consulting in the areas of statistics and computing for academia, healthcare, business, and government. Currently he is Vice President of Research for Management Research Group, a global human resource development firm, where he has provided research and statistical consultations to organizations around the world for the past 15 years. Prior to joining MRG he was a Professor of Psychology in the Center for Psychological Studies at Nova Southeastern University, where he taught graduate courses in research methodology, statistical computing and statistics.

Dr. Kabacoff is author of the book R in Action: Statistics and graphics with R ( and maintains Quick-R (, a popular tutorial site on the R language. In the past two years he has taught workshops on R programming for such organizations as the Association of Computing Machinery, the Society of Industrial and Organizational Psychology, and the United States Department of Defense.

Relevance to Conference Goals

Using freely available open source software, participants will learn practical data analytic skills covering the full range of the research endevour, from data aquisition and data munging, to building and testing models, visualizing results, and communitating finding to their constituencies. Additionally, suggested paths for continued learning (including online resources and help resources) are provided.

SC8 Peering into the Future: Introduction to Time Series Methods for Forecasting
Thu, Feb 20, 1:00 PM - 5:00 PM
Palma Ceia IV
Instructor(s): David A. Dickey, North Carolina State University

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This workshop will provide a practical guide to time series analysis and forecasting, focusing on examples and applications in modern software. Students will learn how to recognize autocorrelation when they see it and how to incorporate autocorrelation into their modeling. Models in the ARIMA class and their identification, fitting, and diagnostic testing will be emphasized and extended to models with deterministic trend functions (inputs) and ARMA errors. Diagnosing stationarity, a critical feature for proper analysis, will be demonstrated. After the course, students should be able to identify, fit, and forecast with this class of time series models and be aware of the consequences of having autocorrelated data. They should be able to recognize nonstationary cases in which the differences in the data, rather than the levels, should be analyzed. Underlying ideas and interpretation of output, rather than code, will be emphasized. No previous experience with any particular software is needed. Examples will be computed in SAS, but most modern statistical packages such as SPSS, R, STATA, etc. can be used for time series analysis.

Outline & Objectives

Outline of course topics:

(1)Identifying and fitting ARMA models,


(3)Incorporating inputs: Regression with Time Series Errors,

(4)Intervention Analysis,

(5)Nonstationarity: Unit Roots and Stochastic Trends,

(Optional: Seasonal models time permitting)

Benefits of the course include an understanding of new issues encountered when data are taken over time and how to deal with these issues. Not only are new techniques of analysis necessary, which the student will learn, but additional terminology arises in these cases.
Examples and practical interpretation along with the strengths and weaknesses of competing forecasting methodologies will be emphasized.
I hope to give examples of interesting data analyses that can be used as templates for analyzing the participants' data when they return home.

About the Instructor

David A. Dickey received his PhD in statistics in 1976 from Iowa State University working with Dr. Wayne A. Fuller. Their “Dickey-Fuller” test is a part of most modern time series software packages. He is on the ISI’s list of highly cited researchers and is an ASA Fellow. Dickey is William Neal Reynolds Professor of Statistics at North Carolina State University where he does time series research, teaches graduate level methods courses, does consulting, and mentors graduate students. He is coauthor of several books on statistics, including “The SAS System for Forecasting Time Series,” a publication of SAS Institute. He has presented at many conferences including the 2013 ASA Conference on Statistical Practice and several JSM sessions. He has been a contact instructor for SAS Institute since 1981 teaching courses in statistical methodology, including time series, and has helped write some of their course notes. Recently Dickey has been teaching for NC State University's Institute for Advanced Analytics which offers an intensive applied Master’s degree in a 9 month cohort program. He has appointments in Economics and the NCSU Financial Math program.

Relevance to Conference Goals

The student will be better able to communicate intelligently with clients having data taken over time by learning the terms and the concepts behind them. The benefits of being able to better forecast what is going to happen next should be of obvious value to any company collecting data over time. The successful student should be able to carry out an analysis of time dependent data from model identification, through fitting and diagnostic checking, all the way to producing forecasts.

SC9 Text Analytics
Thu, Feb 20, 1:00 PM - 5:00 PM
Bayshore VII
Instructor(s): Edward R. Jones, Texas A&M Statistical Services

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Text Analytics is a new interdisciplinary area that blends methodology from statistics, computer science, and natural language processing. Understanding the terminology and general approach to the statistical analysis of large collections of text data is increasingly critical to connecting statisticians to important Big Data problems.

Computer scientists have developed sophisticated algorithms for extracting and compiling complex summaries of text data. Statisticians have adaptive statistical methods for text analytics designed to solve sophisticated business and government problems. This is rapidly evolving as the available data and applications change. In the beginning, text analytics involved the analysis of simple word counts. Now, with available software for natural language processing, text analytics is challenged with the analysis of contextual information.

This half-day workshop explores the terminology, common methodology, and software for analysis of large, complex text data.

Outline & Objectives

1. Text Analytics - History and Terminology
2. Concept and Content Extraction
3. Summarization and Categorization
4. Content Management & Sentiment Analysis
5. Useful Approaches to Applying Text Analytics

About the Instructor

Dr. Jones has a Ph.D. degree Statistics from Virginia Tech and a B.S. in Computer Science from Texas A&M University - Commerce. Currently he teaches data mining and analytics at Texas A&M University. He also mentors graduate students in data mining and analytics team competitions. He is also co-founder and Vice President of Texas A&M Statistical Services.

He has over 10 years in the development of statistical and data mining software for companies in Silicon Valley and Rogue Wave Software. He supervised the design, development and testing of the IMSL (International Mathematics and Statistics Library) data mining software.

Relevance to Conference Goals

The Conference on Statistical Practice attracts hundreds of statistical practitioners and researchers. With increasing applications involving text mining and text analytics, this workshop will provide the background applied statisticians can use to expand their field of practice to include problems involving text analytics and text mining.

PS1 Poster Session I & Opening Mixer
Thu, Feb 20, 5:15 PM - 6:45 PM
Bayshore II-IV
Chair(s): Jean V. Adams, US Geological Survey - Great Lakes Science Center

1 Application of Binary Search Algorithm to Improve Response Surface Design
View Presentation View Presentation Liming Xu, FM Global Research
2 A Beginner’s Guide to Effective and Accurate Data Visualization
View Presentation View Presentation Gina G. Mosier, University of Indianapolis-Center of Excellence in Leadership of Learning
3 Statistical and Data Challenges with Modeling Continuous Longitudinal Tumor Measurements as Phase II Endpoints for Predicting Overall Survival
View Presentation View Presentation Sumithra Jay Mandrekar, Mayo Clinic
4 Equivalence Acceptance Critieria for Container Closure System Moisture Permeation Rates Using the Two One-Sided Test Approach
View Presentation View Presentation Katherine Giacoletti, McNeil Consumer Healthcare
5 Do You Know Which Key Drivers Change from Wave to Wave in Your Tracking Surveys?
View Presentation View Presentation Michael Latta, YTMBA Research & Consulting Coastal Carolina University
6 Accounting for Regression to the Mean and Natural Growth in Uncontrolled Studies
View Presentation View Presentation William D. Johnson, Pennington Biomedical Research Center
8 Interdisciplinary Research in the Social Sciences: Multilevel Modeling of Municipal Expenditure Data
View Presentation View Presentation Lori Thombs, Department of Statistics
9 Identify Interactions and Distinct Risk Groups of Time-to-Event Data Using Survival Tree Approach
View Presentation View Presentation Hui-Yi Lin, Dept. of Biostatistics, Moffitt Cancer Center & Research Ins
10 A Statistical Definition of Sustainability
View Presentation View Presentation Dennis FX Mathaisel, Babson College
11 Methods of Developing and Validating a Predictive Model
View Presentation View Presentation Yu-Hui Chang, Mayo Clinic
12 Spatiotemporal Estimation of Mountain Glacier Retreat
Nezamoddin N. Kachouie, Florida Institute of Technology
13 Neural Network--Based Pricing Models for Up-Front Customer Engagement
View Presentation View Presentation Steven Reagan, L&L Products
14 Dynamic Report Generation Using R
View Presentation View Presentation McCall Everest McIntyre, Simulmedia Inc
15 Learning R
View Presentation View Presentation Derek McCrae Norton, Revolution Analytics
16 Managing IBM Technical Service Delivery Around the Globe with Statistical Process Control
Michael Roehl, IBM
Friday, February 21
Fri, Feb 21, 7:00 AM - 5:30 PM
Galleria B

Continental Breakfast
Fri, Feb 21, 7:30 AM - 8:30 AM
Bayshore II-IV

GS1 Keynote Presentation
Fri, Feb 21, 7:45 AM - 9:00 AM
Bayshore I
Chair(s): Sylvia Dohrmann, Westat

8:00 AM The ASA, the CSP, and Career Lessons: A Buffet
View Presentation View Presentation Nathaniel Schenker, 2014 President of the American Statistical Association
CS01 Mentoring
Fri, Feb 21, 9:15 AM - 10:45 AM
Bayshore V
Chair(s): Jennifer LS Gauvin, GlaxoSmithKline

9:15 AM Mentoring Program Reflections with Panel Discussion
View Presentation View Presentation Olawale Awe, Obafemi Awolowo University; Dhuly Chowdhury, RTI International; Felicia Hardnett, CDC; Lillian Lin, CDC; David Morganstein, Westat, Inc.; Eric Vance, Virginia Tech
CS02 Interpreting Analyses
Fri, Feb 21, 9:15 AM - 10:45 AM
Bayshore I
Chair(s): Runhua Shi, Department of Medicine, Feist-Weiller Cancer Center

9:15 AM Interacting with Interactions: Understanding Interactions and Powering Studies to Detect Them
View Presentation View Presentation Bruce Alan Barton, U Mass Medical School
10:00 AM Information Value Statistic and Predictors for Logistic Regression
View Presentation View Presentation Bruce Stephen Lund, Marketing Associates, LLC
CS03 Ensemble Modeling
Fri, Feb 21, 9:15 AM - 10:45 AM
Bayshore VI
Chair(s): Edward R. Jones, Texas A&M Statistical Services

9:15 AM Using Random Model Tree Ensembles to Study Predictor Interactions
View Presentation View Presentation Barry Swanson Eggleston, RTI International
CS04 Structured Graphs and Visualization Tools
Fri, Feb 21, 9:15 AM - 10:45 AM
Bayshore VII
Chair(s): Mark S. Litaker, UAB School of Dentistry

9:15 AM Structured Sets of Graphs
View Presentation View Presentation Richard M. Heiberger, Temple University
10:00 AM Data Visualization: What’s in the Data?
View Presentation View Presentation Matt Slaughter, Nielson Audio
Fri, Feb 21, 10:45 AM - 11:00 AM
Bayshore II-IV

CS05 Collaboration
Fri, Feb 21, 11:00 AM - 12:30 PM
Bayshore V
Chair(s): Katherine Giacoletti, McNeil Consumer Healthcare

11:00 AM Creating Collaboration
View Presentation View Presentation Nicholas Skovran, Diversified Service Options
11:45 AM Collaborative Grant Development: The Statistician’s Roles and Responsibilities
View Presentation View Presentation Jonathan D. Mahnken, The University of Kansas Medical Center
CS06 Algorithmic Data Analyses
Fri, Feb 21, 11:00 AM - 12:30 PM
Bayshore VI
Chair(s): Marie Kraska, Auburn University

11:00 AM Bootstrapping Time Series Data
View Presentation View Presentation Paul Teetor, Quant Development LLC
11:45 AM Random Forest Procedure for Classification of Etiologies in Acute Liver Failure Patients
View Presentation View Presentation Jaime Lynn Speiser, Medical University of South Carolina
CS07 Big Data in the Real World
Fri, Feb 21, 11:00 AM - 12:30 PM
Bayshore I
Chair(s): Phil Scinto, The Lubrizol Corp.

11:00 AM Working with Complex Sizeable (i.e., Gigabyte) Data on a PC: A Case Study
View Presentation View Presentation Pete Michael Sherick, Lubrizol Corporation
11:45 AM Using the Open Source R Language to Model Store Sales
View Presentation View Presentation John V. Colias, Decision Analyst, Inc.
CS08 Interactive Graphics
Fri, Feb 21, 11:00 AM - 12:30 PM
Bayshore VII
Chair(s): Jim Li, Procter and Gamble

11:00 AM Hospital Pricing Interactive Visualization Techniques Using Tableau
View Presentation View Presentation Billie Sue Anderson, Bryant University
11:45 AM Creating Your First D3 Interactive Graph
View Presentation View Presentation Jean V. Adams, US Geological Survey - Great Lakes Science Center
Lunch (on own)
Fri, Feb 21, 12:30 PM - 1:30 PM

CS09 Career Advancement
Fri, Feb 21, 1:30 PM - 3:00 PM
Bayshore V
Chair(s): Denái R. Milton, MD Anderson

1:30 PM Benefits of ASA Accreditation
View Presentation View Presentation Mary Batcher, Ernst & Young
2:15 PM The Career Map
View Presentation View Presentation Sam Gardner, Eli Lilly
CS10 Not Your Usual Assumptions
Fri, Feb 21, 1:30 PM - 3:00 PM
Bayshore I
Chair(s): Dennis Eggett, Brigham Young University

1:30 PM Estimating Price Elasticities from Censored Data: Frequentist & Bayesian Approaches
View Presentation View Presentation Kenneth P. Sanford, SAS Institute
2:15 PM Statistical Approach for Prediction, Validation, and Creation of a Simple Score: Application to a Neurocritical Care Study
View Presentation View Presentation Jay N. Mandrekar, Mayo Clinic
CS11 Risk Prediction & Modeling
Fri, Feb 21, 1:30 PM - 3:00 PM
Bayshore VI
Chair(s): Simon J. Sheather, Texas A&M University

1:30 PM Understanding and Predicting Acute Cardiac Events Using Electronic Health Records
View Presentation View Presentation Benjamin A. Goldstein, Stanford University - Quantative Sciences Unit
2:15 PM Risk Intelligent Modeling: Principles for Powerful Metric-Based Risk Scoring
Robert J. Torongo, Deloitte and Touche LLP
CS12 Graphics in Oncology Drug Development
Fri, Feb 21, 1:30 PM - 3:00 PM
Bayshore VII
Chair(s): Sumona Mondal, Clarkson University

1:30 PM Statistical Graphics in GSK Oncology Drug Development: Our Road to Advanced Graphic Capabilities and Insight
View Presentation View Presentation Michael Gabriel Durante, GlaxoSmithKline
2:15 PM Graphical Exploration of Response to Anti-Cancer Medicines and Patient Characteristics
View Presentation View Presentation Jennifer LS Gauvin, GlaxoSmithKline
Fri, Feb 21, 3:00 PM - 3:15 PM
Bayshore II-IV

CS13 Organizational Impact
Fri, Feb 21, 3:15 PM - 4:45 PM
Bayshore V
Chair(s): Alexandra L. Hanlon, University of Pennsylvania

3:15 PM Best Practices to Borrow/Steal from Startups
View Presentation View Presentation Neal Fultz, UCLA
4:00 PM Beyond Reproducibility: A Framework for an Accountable Data Analysis Process (ADAP)
View Presentation View Presentation Jonathan A. Gelfond, UT Health Science Center San Antonio
CS14 Survey Design
Fri, Feb 21, 3:15 PM - 4:45 PM
Bayshore I
Chair(s): Fotios K. Kokkotos, Trinity Partners

3:15 PM Sample Allocation Using Vendor-Provided Demographic Data
View Presentation View Presentation Mike Kwanisai, Nielsen Audio
4:00 PM Integrated Survey Designs: A Framework for Enhanced Analytic Capacity and Efficiency
View Presentation View Presentation Steven B. Cohen, Agency for Healthcare Research and Quality
CS15 Rumors and Recommendations
Fri, Feb 21, 3:15 PM - 4:45 PM
Bayshore VI
Chair(s): Steven Reagan, L&L Products

3:15 PM Are the Rumors True? Using Text Mining to Predict Future Baseball Trades
View Presentation View Presentation Michael Greene, Deloitte Consulting
4:00 PM When Some Recommendations Are More Important Than Others: Combining Weighted Least Squares and Matrix Factorization for Recommender Systems
View Presentation View Presentation Calvin Price, American Express
CS16 Implementing the Tools
Fri, Feb 21, 3:15 PM - 4:45 PM
Bayshore VII
Chair(s): Blayne Easter, Vanguard

3:15 PM Phases in Dynamic Systems: Cluster Analysis of Time Series Data
View Presentation View Presentation David J. Corliss, Magnify Analytic Solutions
4:00 PM Predictive SPC
View Presentation View Presentation Alex Gilgur, Google
PS2 Poster Session II & Refreshments
Fri, Feb 21, 4:45 PM - 6:15 PM
Bayshore II-IV
Chair(s): Jean V. Adams, US Geological Survey - Great Lakes Science Center

1 Web-Based Business Intelligence and Analytics
View Presentation View Presentation Sam Weerahandi, Pfizer
2 Advanced Placement Statistics Teaching Knowledge Assessment
View Presentation View Presentation Brenna J. Haines, The George Washington University
3 Central Limit Theorem and Sampling Distributions
View Presentation View Presentation Marie Kraska, Auburn University
4 Developing a Composite Score Sensitive to Clinical Progression in Early Stages of Alzheimer's Disease (AD) Using Partial Least Squares Regression
View Presentation View Presentation Jinping Wang, Eisai Inc
5 A Margin-Based Approach to Determining Sample Sizes via Tolerance Intervals
View Presentation View Presentation Katherine E. Freeland, Sandia National Laboratories
6 Informing Clients of Negative Aspects of a Hot Topic
View Presentation View Presentation David R. Bristol, Statistical Consulting Services Inc
7 Comparing Statistical Consulting and Collaboration Practices Between Nigeria and the United States
View Presentation View Presentation Olawale Awe, Obafemi Awolowo University
8 Mixed Effects Model for Comparing Treatments That Alter Length of Life in the C. elegans Model
View Presentation View Presentation Jeffrey H. Burton, Pennington Biomedical Research Center
9 Evaluating the Effectiveness of Occupant Protection Programs
View Presentation View Presentation Alyssa Peck, Graduate Research Assistant
10 SPSS, P-Values, Standard Deviations, Oh My! Teaching Concept-Based Statistics as a Prerequisite to Graduate-Level Nursing Research in a Distance-Based Advanced Practice Nursing Education Program
View Presentation View Presentation Trish McQuillin Voss, Frontier Nursing University
11 What's So Standard About Risk-Standardization?
View Presentation View Presentation Heidi Reichert, University of Michigan
12 Skills for a Career in Applied Statistics
View Presentation View Presentation Nancy Wang, Celerion
14 Practical Statistical Issues in Analyzing Immunoassay Data
View Presentation View Presentation Xinrui Zhang, University of Florida
15 Investigation of Structure-Function Relations in Spina Bifida Population Utilizing Robust Correlations
View Presentation View Presentation Paulina A. Kulesz, University of Houston
16 Applied an Integrated Efficacy Outcome to Post-Surgical Pain Trials
View Presentation View Presentation Shiao-ping Lu, LUcid Consulting
17 A Simulation Study to Compare the Performance of Independent Means t-test and Alternatives in Terms of Type I Error and Statistical Power
View Presentation View Presentation Diep Thi Nguyen, University of South Florida
Saturday, February 22
Sat, Feb 22, 7:00 AM - 1:30 PM
Galleria B

PS3 Poster Session III & Continental Breakfast
Sat, Feb 22, 7:30 AM - 9:00 AM
Bayshore II-IV
Chair(s): Jean V. Adams, US Geological Survey - Great Lakes Science Center

1 Quantile Regression with ED Wait Time Data
View Presentation View Presentation Jie Zhou, Carolinas Healthcare System
2 Design and Analysis of Computer-Simulated Experiments for Hydrocarbon Reserves Estimation
View Presentation View Presentation Ritu Gupta, Curtin University
3 PPM Recruitment Performance Rate Forecasting
View Presentation View Presentation Renting (Sharon) Xu, Nielsen Audio
4 Fitting a GAM to Estimate Hourly Ozone Levels in the Air from Climate Variables
View Presentation View Presentation Javier Olaya, Universidad del Valle
5 A Diffusion Model with Dynamic Potential: New Applications to Industrial Sectors
View Presentation View Presentation Mariangela Guidolin, Department of Statistical Sciences, University of Padua
6 REML Sensitivity: A Common Situation When EMS Is Preferable
View Presentation View Presentation Christopher C. Breen, Eli Lilly and Company
7 Testing Differences in Glucose Profiles Using AUC and Mixed Models
View Presentation View Presentation Robbie Beyl, Pennington Biomedical Research Center
8 Becoming a Successful Young Collaborator: 20 Strategies for the MS-Level Statistician
View Presentation View Presentation Seth Lirette, University of Mississippi Medical Center, University of Alabama-Birmingham
9 An Empirical Comparison of the Accuracy and Precision of Effect Size Indices for Artificially Dichotomized Variables: A Simulation Study
View Presentation View Presentation Patricia Rodriguez de Gil, University of South Florida
10 Confidence Intervals of Differences Between Correlated Proportions: An Empirical Comparison Among Three Estimation Methods
View Presentation View Presentation Thanh Vinh Pham, University of South Florida
11 JMP Start Biostatisticians’ Quality Check on Analytical Results
View Presentation View Presentation Chun Feng, Celerion
12 Conceptualizing Statistics: A Heuristic Approach
View Presentation View Presentation Heidi Reichert, University of Michigan
13 Estimation of Error in Electronically Available Variables in a Large Hospital Database by Simulation and Comparison with Manual Data Abstraction
View Presentation View Presentation Baevin S. Carbery, Division of Infectious Diseases, Department of Medicine, Beth Israel Deaconess Medical Center
14 When Good Experiments Go Bad: A Case Study of Outliers
View Presentation View Presentation Paige Lee Fisher, ACHRI
15 Training Statistical Consultants Using Case-Based Examples
James Landis Rosenberger, Penn State University
16 An Introduction to the Generalized Eta-Squared Effect Size Associated with Analysis of Variance Models
View Presentation View Presentation Anh P. Kellermann, University of South Florida
17 Test of Equivalence for Repeated Measurements in a Clinical Study for Hepatocellular Carcinoma (HCC) Patients
Yiyi Chen, Oregon Health and Science University
18 Using a Statistical Software Tool as a Communication Tool
View Presentation View Presentation Jessica Ann Behrle, Janssen R&D
CS17 Communication
Sat, Feb 22, 9:00 AM - 10:30 AM
Bayshore V
Chair(s): Yueh-Yun Chi, University of Florida

9:00 AM Coming Out of the Casket: Techniques for Becoming a More Effective Speaker
View Presentation View Presentation Eric Stephens, SESAC
9:45 AM Techniques for More Effective Presentations
View Presentation View Presentation William Williams, Organizational Learning Consultant
CS18 Modeling Events in Time
Sat, Feb 22, 9:00 AM - 10:30 AM
Bayshore VII
Chair(s): Jo Martinez, Chevron Oronite Company LLC

9:00 AM Is Event Interval Analysis History?
View Presentation View Presentation James Arthur Lemon, University of NSW
9:45 AM Defect Initiation, Growth, and Failure: A General Statistical Model and Data Analyses
View Presentation View Presentation Wayne Bryce Nelson, Wayne Nelson Statistical Consulting
CS19 Problems of Size and Variety
Sat, Feb 22, 9:00 AM - 10:30 AM
Bayshore VI
Chair(s): Edward R. Jones, Texas A&M Statistical Services

9:00 AM Panel discussion of Big Data & Analytics Survey
View Presentation View Presentation Roger Wesley Hoerl, Union College; Phil Scinto, The Lubrizol Corp.; Simon J. Sheather, Texas A&M University
9:45 AM LP Comoment Multivariate Mixed Data Modeling and Application to Big Data
Subhadeep (Deep) Mukhopadhyay, Temple University, Fox Business School
CS20 Using R – Graphics, Geostatistics, and Maps
Sat, Feb 22, 9:00 AM - 10:30 AM
Bayshore I
Chair(s): Michael Latta, Coastal Carolina University

9:00 AM R for Data Visualization and Graphics
View Presentation View Presentation Robert Kabacoff, Management Research Group
Sat, Feb 22, 10:30 AM - 10:45 AM
Bayshore II-IV

CS22 Survey Analysis
Sat, Feb 22, 10:45 AM - 12:15 PM
Bayshore I
Chair(s): Jay N. Mandrekar, Mayo Clinic

10:45 AM Weighting and Sample Matching Techniques for Reducing Bias in Online Convenience Panels
View Presentation View Presentation Pete Doe, Nielsen
11:30 AM Forecasting Panel Turnover Utilizing Survival Analysis
View Presentation View Presentation David Burtnick, Nielsen Audio
CS23 Modeling Techniques
Sat, Feb 22, 10:45 AM - 12:15 PM
Bayshore VI
Chair(s): Mariangela Guidolin, Department of Statistical Sciences, University of Padua

10:45 AM Modeling Curvilinearity, Interactions, and Curvilinear Interactions in Logistic Regression: Having More Fun with Your Data
View Presentation View Presentation Jason W. Osborne, University of Louisville
11:30 AM Fractional Polynomials: Flexible, Interpretable, and an Alternative to Splines
View Presentation View Presentation Michael D. Regier, West Virginia University, Department of Biostatistics
CS24 Using Graphs in Decisionmaking and Quality Control
Sat, Feb 22, 10:45 AM - 12:15 PM
Bayshore VII
Chair(s): Sam Gardner, Eli Lilly

10:45 AM Improved Decisionmaking When Balancing Multiple Objectives
View Presentation View Presentation Christine Anderson-Cook, Los Alamos National Laboratory
11:30 AM Posterior Predictive Checks for Interference in a 3D Printing Experiment
View Presentation View Presentation Arman Sabbaghi, Harvard University Department of Statistics
Lunch (on own)
Sat, Feb 22, 12:15 PM - 1:30 PM

PCE1 Introduction to Visual Analytics and Analyzing Big Data
Sat, Feb 22, 1:30 PM - 3:30 PM
Bayshore I
Instructor(s): Tom Bohannon, SAS; Michael Speed, SAS
This course teaches the basics of exploring data and building reports using SAS Visual Analytics and High Performance Techniques.

PCE2 Hear What Your Data is Telling You with JMP & JMP Pro 11
Sat, Feb 22, 1:30 PM - 3:30 PM
Palma Ceia I
Instructor(s): Scott Lee Wise, SAS Institute Inc., JMP Business Division
New JMP & JMP Pro 11 software from the SAS Institute will help make finding the story in your data faster and easier. Newly available techniques will allow you to really separate the signal from the noise (get robust, handle messy data, etc.) and follow clues to new breakthroughs (transform variables, screen important variables, etc.). These customer-inspired advances will really help speed up the pace of discovery, from data import to analysis to presentation.

PCE3 Statistical Analysis and Data Visualization Using Statgraphics
Sat, Feb 22, 1:30 PM - 3:30 PM
Bayshore VI
Instructor(s): Neil W. Polhemus, Statpoint Technologies, Inc.
This presentation will demonstrate techniques for the analysis and visualization of data using Statgraphics. It will cover methods for modeling a single variable, comparing multiple samples, visualizing multivariate data, determining relationships between variables, and modeling time series data. It also will consider graphical methods that are useful for constructing and analyzing designed experiments. Special attention will be devoted to Statlets, a set of procedures added to the latest version of Statgraphics that use graphical methods in a dynamic fashion. The presentation will include a set of Statlets created to interactively select good statistical models. The methodologies covered are applicable to all data analysts and will be demonstrated using data of the types commonly used in practice.

T1 Creating Statistical Graphics with ODS in SAS® Software
Sat, Feb 22, 1:30 PM - 3:30 PM
Bayshore V
Instructor(s): Warren F. Kuhfeld, SAS

Download Handouts
SAS 9.2 provides ODS Graphics, new functionality used by statistical procedures to create statistical graphics as automatically as they create tables. ODS Graphics is also used by new Base SAS procedures designed for graphical exploration of data. This tutorial is intended for statistical users and covers the use of ODS Graphics in statistical analysis.

You will learn how to:
- Request graphs created by statistical procedures
- Use the new SGPLOT, SGPANEL, SGSCATTER, and SGRENDER procedures to create customized graphs
- Access and manage your graphs for inclusion in web pages, papers, and presentations
- Modify graph styles
- Make immediate changes to your graphs using a point-and-click editor
- Make permanent changes to your graphs with template changes
- Specify other options related to ODS Graphics

Outline & Objectives

1. Introduction:

a. The basics of ODS Graphics

i. Enable ODS Graphics

ii. Procedures that support ODS Graphics

b. One step beyond the basics

i. Destinations

ii. Optional graphs

iii. Change graph style

iv. Editable graphs

v. Commonly used options

2. Graph and Style Template Languages

a. Templates and item stores

i. Template source

ii. SAS libraries

iii. ODS path

iv. Clean up

b. Graph template modification

i. Titles, ticks, axis labels, grids

ii. Adding date and project information

iii. Axis labels

iv. Graph data object

v. Dynamic variables

vi. Survival plot

c. Style template modification

3. The SG procedures and the GTL

a. Graph Template Language (GTL)

b. PROC SGPLOT - scatter plot, fit plot, histogram and density plots, bar chart, box plot, text insets, unicode, series plot and drop lines

c. PROC SGSCATTER - scatter plot matrix, panel of scatter plots

d. PROC SGPANEL - Data panel

e. Residual Panel (with GTL)

4. Conclusions

About the Instructor

Warren F. Kuhfeld is director of the SAS Advanced Regression Models R&D group. He received his Ph.D. in psychometrics from UNC Chapel Hill in 1985 and joined SAS in 1987. He has used SAS since 1979 and has developed SAS procedures since 1984. Warren wrote the SAS/STAT documentation chapters, “Using the Output Delivery System,” “Statistical Graphics Using ODS,” “ODS Graphics Template Modification,” and the SAS Press book Statistical Graphics in SAS: An Introduction to the Graph Template Language and the Statistical Graphics Procedures. Warren currently develops eleven SAS/STAT procedures and over twenty SAS macros for experimental design.

Relevance to Conference Goals

This tutorial addresses Theme 4: Software and Graphics.
Participants will learn how to create, manage, and customize a variety of graphs. Modern statistical graphics enable statisticians to better communicate statistical information. Knowledge of ODS Graphics is an indispensable skill for statisticians who use SAS to manage and analyze their data.

T3 Model Selection for Linear Models with SAS/STAT® Software
Sat, Feb 22, 1:30 PM - 3:30 PM
Bayshore VII
Instructor(s): Funda Gunes, Research Statistician at SAS Institute

Download Handouts
When you are faced with a predictive modeling problem that has many possible predictor effects, a natural question is, ”What subset of the effects provides the best model for the data?” This workshop explains how you can address this question with model selection methods in SAS/STAT software. The workshop also explores the practical pitfalls of model selection. The workshop focuses on the GLMSELECT procedure and shows how it can be used to mitigate the intrinsic difficulties of model selection. You will learn how to use model selection diagnostics, including graphics, for detecting problems; use of validation data to detect and prevent under-fitting and over-fitting; modern penalty-based methods, including LASSO and adaptive LASSO, as alternatives to traditional methods such as stepwise selection; and bootstrap-based model averaging to reduce selection bias and improve predictive performance. This workshop requires an understanding of basic regression techniques.

Outline & Objectives

Traditional model selection methods,
Customizing the selection process,
Penalized regression methods,
Model averaging,
Selection for nonparametric models with spline effects

The objective of this course is to teach the goals of model selection and difficulties of implementing it for a real data. The participants will learn how to mitigate these difficulties using extensive capabilities of the GLMSELECT procedure of SAS/STAT software.

About the Instructor

I am a Research Statistician in the Statistical Applications Department at SAS. I completed my PhD in statistics from North Carolina State University with focus in model selection methods.

As a research statistician in statistical R&D at SAS, I often give expository talks and two-hour tutorials at JSM and ENAR on a variety of topics, including mixed models, model selection, and Bayesian statistics. These presentations emphasize basic concepts, and they introduce applied statisticians to new methodology with relevant examples.

Relevance to Conference Goals

I attended the first CSP in 2012 and was impressed by the goals of the conference. I especially enjoyed meeting statistical practitioners and learning how they use statistics in their work. Based on that experience, I think the CSP audience is a perfect fit for the content and level of the presentations that I give, and I would love to participate.

T4 Learning and Improving Skills to become an Effective Statistical Collaborator
Sat, Feb 22, 1:30 PM - 3:30 PM
Palma Ceia II
Instructor(s): Eric Vance, Virginia Tech

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This fast-paced tutorial will get you up to speed on how to effectively collaborate with nonstatisticians to solve real-world problems and implement solutions. Based on Vance’s methods used to train more than 130 graduate students to become effective statistical collaborators for LISA (Virginia Tech’s Laboratory for Interdisciplinary Statistical Analysis), this tutorial will introduce you to the POWER process for structuring efficient meetings, lead you through role plays to practice what you have learned, and show you how to systematically improve your statistical collaboration skills through the recording and analysis of video.

Outline & Objectives

Tutorial participants will have read about the POWER process before arriving at the conference and will work in groups at the beginning of the tutorial to apply their understanding of the readings to answer questions and solve problems in the context of statistical consultations and collaborations. Dr. Vance will briefly lecture on any concepts of the POWER process the participants had trouble understanding or applying. At the end of this section of the tutorial, participants will solidly understand the five steps in the POWER process that lead to effective statistical collaborations.

The second section of the tutorial will consist of role plays in which the participants practice the key concepts of the POWER process. Alternating between the roles of client, statistical collaborator, and observer—while giving feedback to their peers—the participants will practice the skills they will use to unlock their collaborative potential.

The last section will show participants how to video record themselves during meetings with clients and, more importantly, how to analyze that video to systematically improve their statistical collaboration skills upon their return to their organizations.

About the Instructor

Dr. Eric Vance is a statistician and data scientist in the Department of Statistics at Virginia Tech. He is the Director of LISA (Laboratory for Interdisciplinary Statistical Analysis). LISA’s mission is to train statisticians to become statistical collaborators and promote the value of statistical thinking in all phases of scientific research by helping researchers design experiments; collect, analyze, and plot data; run statistical software; interpret results; and communicate statistical concepts to non-statisticians. From 2008-2012, LISA has helped solve research problems on 1,629 collaborative projects in 76 departments at Virginia Tech, helped 1,262 visitors during Walk-in Consulting, and taught the application of statistics to 2,223 attendees at LISA Short Courses for a total of 5,114 LISA clients in its first five years in existence.

Relevance to Conference Goals

This tutorial will help the participants develop statistical collaboration skills that will improve their personal effectiveness as statisticians. Participants will learn best practices in statistical consulting and collaboration that will enhance their organizational impact and potentially lead to career development and advancement. Participants will return to their jobs with new ideas, techniques, and strategies to improve their ability to communicate and collaborate effectively, resulting in a greater impact on their organizations.

GS2 Closing General Session with Refreshments
Sat, Feb 22, 3:45 PM - 5:00 PM
Bayshore I
Please join us for a feedback session and great prize give-aways for the third annual ASA Conference on Statistical Practice. CSP Steering Committee Chair, LeAnna Stork and Vice-Chair, Sylvia Dohrmann will lead a panel of CSP Committee members in a final session to 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. Your feedback is crucial to ensuring a successful future for the conference!

Refreshments will be served and ASA staff will be raffling great prizes. The closing session is also a great time to let the CSP Steering Committee know if you are interested in helping out with future conferences. Please plan to attend!