Online Program
Thu, Feb 16 |
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SC1: Analysis of Messy Data: Design and Analysis of Experiments Requiring Mixed Models |
Thu, Feb 16, 8:30 AM - 5:30 PM
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Instructor(s): George Milliken, Kansas State University (retired) Download Handouts |
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Four basic design structures that are building blocks for complex designs will be presented visually and conceptually. The analyses of the four design structures will be described in such a way that one can determine the appropriate error terms and associated degrees of freedom. Three of the four design structures are mixed models. The concept of fixed and random effects will be discussed, as well as analysis of a basic mixed model. Several examples will be used to demonstrate the use of the basic design structures to extract an appropriate model for complex designs. Examples will include incomplete block designs, repeated measures designs, split-plot designs, strip-plot designs, and combinations of these designs. SAS’s PROC MIXED and PROC GLIMMIX will be used to demonstrate the construction of code from the results of developing models for complex designs. |
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SC2: Regression Modeling with Many Correlated Predictors: High-Dimensional Data Analysis in Practice |
Thu, Feb 16, 8:30 AM - 5:30 PM
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Instructor(s): Tony Babinec, AB Analytics; Jay Magidson, Statistical Innovations Inc. Download Handouts |
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The availability of a vast amount of data in fields such as genomics, marketing research, and signal processing has led to recent advances in high-dimensional data analysis. It is now possible to develop reliable regression models, even when the number of predictors exceeds the number of cases. In this course, we will begin by reviewing problems and limitations with traditional linear and logistic regression. We will then introduce the two primary regularization approaches for analyzing such data—penalized regression and component methods—related software, and recent advances in feature selection. Our applications-oriented presentation provides insight into how the new approaches work in examples with both low- and high-dimensional data and an overview of the relevant theory, supplemented by supporting equations. We will use real and simulated data sets to illustrate the different methods. The material presented will be included in a forthcoming book on this topic by the presenter. |
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SC3: Introducing R for Statistical Analysis |
Thu, Feb 16, 8:30 AM - 12:30 PM
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Instructor(s): Eric Nantz, Eli Lilly and Company Download Handouts |
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R is a powerful statistical and data analysis software package and has rapidly become the software of choice for understanding data and applying statistical methodologies. Unlike the traditional statistical analysis software, R is free and open-source. This course will introduce R and supplemental tools to enhance the user experience. We will then discuss how to import common data file formats into R for analysis. We will use example data sets to illustrate how to compute basic statistics, conduct inference procedures, fit linear models, create visualizations of the data, and perform data manipulations. Last, we will discuss best practices for writing R programs and additional resources for learning more about the capabilities of R. When finished, you will be able to use R for a comprehensive data analysis with basic data operations, statistical methods, and visualization. Attendees should have a basic understanding of descriptive statistics and statistical methods such as t-tests, linear regression, and chi-square test. Before the course, instructions for installing R and supplemental materials will be given to all participants. While not required, it is beneficial for attendees to install this software before the course. |
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SC4: Why Don’t They Get It? |
Thu, Feb 16, 8:30 AM - 12:30 PM
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Instructor(s): Bill Williams, Organizational Learning Consultant Download Handouts |
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Many of us make presentations to others as part of our work. These range in length and complexity of the situation, from presenting ideas to your supervisor, to presenting analysis results in a small group meeting, to more formal, stand-up affairs in front of rooms full of people. Regardless, if you have ever felt as though you were being tuned out or misunderstood by an audience, it may be time to learn to “see” through their eyes. In this short workshop, you’ll do a basic assessment of your preferred modes communication and identify some strategies for adjusting to audiences who have different cognitive styles and communication preferences from yours. |
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SC5: Using Statistical Engineering to Solve Large, Unstructured Problems |
Thu, Feb 16, 1:30 PM - 5:30 PM
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Instructor(s): Ronald D Snee, Snee Associates, LLC Download Handouts |
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This course was designed to enhance the skills of statisticians in using statistical engineering to solve large, complex, unstructured problems encountered in business, industry, and government. We will identify important gaps in the theory of statistical engineering for which research is needed. Several case studies of the use of statistical engineering in a variety of fields will be presented. Issues to be addressed include understanding what statistical engineering is, why it is important, and how to use it, as well as identifying research gaps. We also will discuss how statistical engineering differs from the classic application of statistics. Participants will be introduced to the critical leadership skills needed for the successful use of statistical engineering. Each participant will develop a personal action plan for using statistical engineering in their work environment, whether in academia, government, or the private sector. Participants will gain insight into increasing the impact of their work and how to transition from being viewed as passive consultants to proactive leaders within their organizations. We will use presentation and discussion of material from articles about statistical engineering, as well as share personal experiences (participants and course leaders) in solving large, unstructured problems. The course will be highly interactive, enabling extensive participation by all. |
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SC6: Managing Your Time and Priorities |
Thu, Feb 16, 1:30 PM - 5:30 PM
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Instructor(s): Bill Williams, Organizational Learning Consultant Download Handouts |
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Time management isn’t a skill so much as a collection of around a dozen skills, from broader behaviors like goal setting to management skills like delegation, to more granular tactics like scheduling. Most of us excel at some and fall short on others. In this course, you will take a step back from the day-to-day to identify your most important priorities, look at what gets in the way of addressing them, identify your time management strengths and areas for improvement, and explore means of shoring up your weak areas. |
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GS1 Opening General Session |
Thu, Feb 16, 7:00 PM - 8:00 PM
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Chair(s): Philip Rocco Scinto, The Lubrizol Corporation |
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Opening Remarks
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Statistical Practice in the World of Business Analytics: What Do We Need to Succeed?
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Fri, Feb 17 |
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PS1 R&D, Operations and Engineering Session 1 |
Fri, Feb 17, 8:30 AM - 10:00 AM
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Chair(s): Jyoti Rayamajhi, Eli Lilly & Company |
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8:35 AM |
Modeling the Reliability of Complex Systems with Multiple Data Sources: A Statistical Engineering Case Study
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9:10 AM |
Applications of Statistics in Aircraft Maintenance
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PS2 Business Analytics Session 1 |
Fri, Feb 17, 8:30 AM - 10:00 AM
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Chair(s): Dominique Haughton, Bentley University |
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8:35 AM |
DNA of a great data miner
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9:10 AM |
Uplift Modeling: The Enabler “To Do More with Less”
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PS3 Communication, Impact and Career Development Session 1 |
Fri, Feb 17, 8:30 AM - 10:00 AM
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Chair(s): Dennis Kunimura, England Logistics |
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8:35 AM |
Accreditation: a perspective from the committee
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9:10 AM |
ASA Professional Statistician Accreditation: Why it might be right for you!
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PS4 R&D, Operations and Engineering Session 2 |
Fri, Feb 17, 10:30 AM - 12:00 PM
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Chair(s): Winson Taam, The Boeing Company |
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10:35 AM |
Statistical Influence on Regulatory Processes
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11:10 AM |
Statistical Issues in the Product Life Cycle of Implantable Medical Devices
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PS5 Business Analytics Session 2 |
Fri, Feb 17, 10:30 AM - 12:00 PM
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Chair(s): Dennis Kunimura, England Logistics |
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10:35 AM |
Customer Loyalty in Uncertain Times
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11:10 AM |
Best Practices
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PS6 Communication, Impact and Career Development Session 2 |
Fri, Feb 17, 10:30 AM - 12:00 PM
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Chair(s): LeAnna Stork, Monsanto Company |
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10:35 AM |
Statistical Consulting - Working Collaboratively with Non-statisticians
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11:00 AM |
Statistical Consulting – Best Practices
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11:25 AM |
Panel Discussion: Statistical Consulting - Working Collaboratively Across Disciplines
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PS7 R&D, Operations and Engineering Session 3 |
Fri, Feb 17, 1:30 PM - 3:00 PM
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Chair(s): Jyoti Rayamajhi, Eli Lilly & Company |
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1:35 PM |
Statistics and Lean+/Six Sigma at Boeing
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2:10 PM |
Practicing Statistics Can Lead to Interesting Research Problems
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PS8 Business Analytics Session 3 |
Fri, Feb 17, 1:30 PM - 3:00 PM
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Chair(s): Dave Nickerson, University of Central Florida |
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1:35 PM |
Statistical Analytics in Marketing Decision Making
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2:10 PM |
Alternative approaches to LGD modeling
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PS9 Communication, Impact and Career Development Session 3 |
Fri, Feb 17, 1:30 PM - 3:00 PM
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Chair(s): Jennifer H. Van Mullekom, DuPont |
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1:35 PM |
Panel Discussion: Career Development
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PS10 R&D, Operations and Engineering Session 4 |
Fri, Feb 17, 3:15 PM - 4:45 PM
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Chair(s): Jim Rutherford, Chevron Oronite Company, LLC |
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3:20 PM |
Screening for Fuel Economy: A Case Study of Supersaturated Designs in Practice
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4:00 PM |
Effective Experimentation Strategies in Aerospace Research: Case Studies and Lessons Learned
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PS11 Business Analytics Session 4 |
Fri, Feb 17, 3:15 PM - 4:45 PM
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Chair(s): Don McCormack, SAS Institute |
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3:20 PM |
Who's afraid of big bad data?
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3:55 PM |
Panel Discussion
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PS12 Communication, Impact and Career Development Session 4 |
Fri, Feb 17, 3:15 PM - 4:45 PM
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Chair(s): Sylvia Dohrmann, Westat |
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3:20 PM |
Networking
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Poster Presentations |
Fri, Feb 17, 4:45 PM - 6:00 PM
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Chair(s): LeAnna Stork, Monsanto Company |
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Using Zero-Inflated Count Models to Predict Software Defects in NASA Data
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Three-Part Mixture Models for Longitudinal Data with Non-Random Drop out and Semi-continuous Outcomes
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Use of Statistical Tools to Monitor Bioburden Levels in a Manufacturing Process
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You’ve Been Asked to Conduct a Systematic Review: Now What?
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Statistical Practice in North America Telecom Industry
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Monitoring quality of Skiing Instructors by nonparametric permutation methods
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The use of Simulation Modeling in the Protocol Development of an Outcomes Trial Involving Multiple Collaborators
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A Biostatistician’s Experience as a Statistical Consulting Intern
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A Successful Model for Creating and Sustaining a Statistical Consulting Center at a Mid-sized University
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Statistical Sampling for Business
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Practical Applications of Zero-Inflated Models in a Manufacturing Setting
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Statistical Challenges in Analyzing Government R&D Project Data for the Effective Budget Compilation
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Preparing Future Applied Statisticians: Enhancing Graduate Education through Consulting
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Client Attrition Analytics at IBM
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The Association Between Patient Satisfaction and Waiting Time
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When is a Ratio Estimator not a Ratio Estimator? Evaluation of Catch Estimators for Alaska Groundfish Fisheries.
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Explaining Sampling Distributions
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Proper Variance Estimation for Event-Level Data Files Representing Subpopulations of Health Care Users in a National Survey
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Sat, Feb 18 |
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PS13 R&D, Operations and Engineering Session 5 |
Sat, Feb 18, 8:30 AM - 10:00 AM
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Chair(s): Winson Taam, The Boeing Company |
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8:35 AM |
Sample size determination using applets
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9:10 AM |
Enhancements to Acceptance Sampling Methods for Crop Seed Lot Purity Testing
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PS14 Business Analytics Session 5 |
Sat, Feb 18, 8:30 AM - 10:00 AM
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Chair(s): Dave Nickerson, University of Central Florida |
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8:35 AM |
Inference: Just what can be said?
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9:10 AM |
Opportunities in Causal Business Analytics
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PS15 Communication, Impact and Career Development Session 5 |
Sat, Feb 18, 8:30 AM - 10:00 AM
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Chair(s): Shelley Brock-Roth, Westat |
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8:35 AM |
It's Not What You Say, It's How You Say It
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9:10 AM |
Misconceptions about Statistics in an Industrial Setting
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PS16 R&D, Operations and Engineering Session 6 |
Sat, Feb 18, 10:30 AM - 12:00 PM
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Chair(s): Jim Rutherford, Chevron Oronite Company, LLC |
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10:30 AM |
Graphing and Testing Interval Censored Time to Event Data: Practical Aspects of Using the interval R package
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11:15 AM |
Monte Carlo: An Underutilized Tool for Understanding Statistics
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PS17 Business Analytics Session 6 |
Sat, Feb 18, 10:30 AM - 12:00 PM
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Chair(s): Don McCormack, SAS Institute |
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10:35 AM |
Testing at Capital One - Part 1
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11:10 AM |
Testing at Capital One - Part 2
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PS18 Communication, Impact and Career Development Session 6 |
Sat, Feb 18, 10:30 AM - 12:00 PM
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Chair(s): Shelley Brock-Roth, Westat |
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10:35 AM |
The TV Guide to Volunteering in the ASA
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11:10 AM |
Statisticians as Leaders
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Poster Presentations |
Sat, Feb 18, 12:00 PM - 1:30 PM
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Latent Class Analysis of the Immune Phenotypes in Children
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Evaluation of Natural Parks by Nonparametric Techniques
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Using Full Propensity Score Matching to Estimate Causal Effects in a Non-Experimental Study: Effects of a Nursing Intervention on Rehospitalizations of Cognitively Impaired Older Adults
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Measuring the Spatial Pattern of Illiteracy Problem in Iraq-2007
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A Strategy for Imputing Missing Body Mass Index Values in a Large, Longitudinal Database
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Using DOE with Tolerance Intervals
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Modeling Document Counts in Indian Trust Archive Box Searches
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Transformation of non-normally distributed outcomes
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Optimal Designs for Simultaneous Measurements in Compartment Models
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A Bayesian Approach to Meta-analysis for Patient Outcomes in United States Teaching versus Nonteaching Hospitals
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Modeling Bivariate Zero- Inflated Longitudinal Ordinal Data: an Application to Marijuana and Cocaine Use
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A SAS macro to calculate sample size for evaluating mediation effect: the linear model case
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A Risk-based Methodology to De-identify Protected Health Information for the Heritage Health Prize
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Scoring The Effectiveness Of Physician Sales Interactions
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Working in HIPPA-compliant environments with large datasets: Pepper Informatics
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Novel Use of Predictive Marginals in Interpretation of Complex Models
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Fitting parametric random effects models in very large data sets
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Finite Mixture Models: Know What You Don't Know
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Is GEE always valid? – Exploring the discrepancies between the score test results and least square results
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Identify gene-gene interactions using two-stage machine learning approach
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T1: Putting Your Best Loafer Forward |
Sat, Feb 18, 1:30 PM - 3:30 PM
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Instructor(s): Bill Williams, Organizational Learning Consultant Download Handouts |
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As professionals, the outcomes of our work are a critical part of our success. But we don't always give sufficient attention to how we achieve those outcomes and, particularly, the impressions co-workers, managers, clients, and others get from interacting with us. The purpose of this tutorial is to help you honestly assess what “foot” you put forward as a professional, enabling you to do the following: clarify the impressions you want others to have of you as a professional; and identify specific actions you can take to convey those impressions at work, in work-related activities after hours, and online. |
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T2: Promoting Your Consulting Career in the Era of Web 2.0 |
Sat, Feb 18, 1:30 PM - 3:30 PM
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Instructor(s): Stephen David Simon, P.Mean Consulting Download Handouts |
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Web 2.0, defined by Wikipedia as "web applications that facilitate participatory information sharing, interoperability, user-centered design, and collaboration on the World Wide Web" offers new opportunities for you to promote your consulting career. These tools are mostly free or very inexpensive, though they are labor intensive. In this talk, I will describe some Web 2.0 tools that I have used to promote my independent consulting career (Facebook, Twitter, LinkedIn), as well as some of the unwritten rules about appropriate usage of these tools. I will contrast these tools with simpler Internet methods (static websites, email newsletters) and some non-computer methods that can help promote your consulting career. Web 2.0 will not replace more traditional modes of career promotion, but it offers some unique opportunities to supplement these efforts. |
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T3: Measurement Systems Analysis |
Sat, Feb 18, 1:30 PM - 3:30 PM
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Instructor(s): Jennifer H. Van Mullekom, DuPont Download Handouts |
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Measurement Systems Analysis is critical to any development or improvement effort in your business. A poor measurement system can result in the inability to distinguish between product development candidates or the inability to determine the success of an improvement. This tutorial will provide basic instruction on how to complete and communicate measurement systems analysis for continuous variables, attribute (discrete) variables, and in-line systems. Multiple software packages will be demonstrated. Examples will include both transactional and engineering/R&D examples. The emphasis in this tutorial will include the practical aspects of designing, executing, analyzing, and communicating the measurement system study within the context of multi-disciplinary, cross-functional teams. Tutorial content prepared by Patrick DeFeo, DuPont. |
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T4: Bayesian Analysis in SAS |
Sat, Feb 18, 1:30 PM - 3:30 PM
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Instructor(s): Mike Patetta, SAS Institute Download Handouts |
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The tutorial focuses on Bayesian analyses using the PHREG, GENMOD, and MCMC procedures. Most of the examples are in the area of clinical trials. The specific topics that will be covered are: • Fit a logistic regression model in PROC GENMOD • Fit a survival model in PROC PHREG • Use prior distributions in a Bayesian analysis • Fit a logistic regression model, general linear mixed model, and zero-inflated Poisson model in PROC MCMC • Illustrate a Bayesian approach to clinical trials using PROC MCMC • Illustrate the Bayesian approach to meta-analysis |
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Feedback Plenary Panel Session |
Sat, Feb 18, 4:00 PM - 5:00 PM
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Chair(s): Philip Rocco Scinto, The Lubrizol Corporation |
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Please join us for a wrap-up discussion and feedback session for the inaugural Conference on Statistical Practice. Phil Scinto will lead a panel of CSP Organizing Committee members and conference participants in a final session to summarize the conference and to gather feedback. Each panelist will speak for 5 minutes to critique the conference (the good, the bad, areas for improvement). Discussion will then be extended to the audience for questions and feedback on how well the overall objectives of the conference were met, including how they can be better met in the future. For the purposes of this discussion, please note that the conference was designed to aid applied statisticians in improving their abilities in consulting with and helping customers and organizations solve real-world problems. Specific attendee objectives include: 1. Learn statistical techniques that apply to your job as an applied statistician 2. Learn how to better communicate with customers 3. Learn how to have a positive impact on your organization Additional feedback on additional objectives and needs of practicing statisticians, and how these can be best accomplished in the future is also welcome. Refreshments will be provided at the close of the session along with a raffle of prizes. |
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Feedback Panel Session Slides
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