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

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Saturday, February 25
T1 Understanding and Working with Different (and Sometimes Difficult) People
Sat, Feb 25, 2:00 PM - 4:00 PM
City Terrace 7
Instructor(s): Colleen Mangeot, Cincinnati Children's Hospital Medical Center

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Do you have coworkers, researchers, or clients that are difficult to work with? Do you feel frustrated and/or confused about how to work with them? Do you wonder sometimes why they just don’t get it? This session will introduce the DISC model for understanding and working with different and sometimes difficult people. It will involve case studies and examples. The result? Improved relationships, increased effectiveness, greater influence, and ability to motivate others.

Outline & Objectives

1. Determine the different communication styles 2. Identify your style and those of others 3. Develop strategies for working with each of the styles

About the Instructor

Colleen Mangeot's diverse career includes 10 years in the actuarial field, 10 years in coaching and leadership development, and 7 years in biostatistics. Highlights of her coaching business include: Successfully working with clients to increase efficiency and sales by 30% or more; Attained the Professional Coach Certification from the International Coach Federation in 2003; Monthly columnist for the Dayton Business Journal; National speaker with over 200 hours of paid speaking engagements; Contractor with the Anthony Robbins Companies. She received her MS in Statistics from Miami University in 2008. She received the NSA National Research Council Fellowship at NIOSH, and worked in statistical quality improvement at the VA. Now, in addition to working in the Biostatistical Consulting Unit at Cincinnati Children’s Hospital Medical Center, she is also an internal coach working with executives to further their careers. She was a panelist for the invited session at JSM 2013, Secrets to Effective Communication for Statistical Consultants. She also had two very well received presentations at the CSP 2015 and conducted a successful short course and tutorial at CSP 2016.

Relevance to Conference Goals

This session will develop important communication skills for career advancement, leadership and management effectiveness, and successful selling for consultants. We all have someone that is difficult to work with. The most successful people are able to work with a variety of people and appreciate and leverage their contributions.

Software Packages

None.

 
T2 Penalized Regression Methods for Generalized Linear Models in SAS/STAT
Sat, Feb 25, 2:00 PM - 4:00 PM
City Terrace 9
Instructor(s): G Gordon Brown, SAS Institute, Inc.
Regression problems that have large numbers of candidate predictor variables occur in a wide variety of scientific fields and in business. These problems require you to perform statistical model selection to find an optimum model that is simple and has good predictive performance. For linear and generalized linear models you will see how to use the forward, backward, stepwise, and LASSO methods of variable selection. This tutorial presents modern variable selection methods for linear models using the adaptive LASSO, group LASSO, and elastic net penalized regression techniques, plus various screening methods. Penalized regression techniques yield a sequence of models and require at least one tuning method to choose the optimum model that has the minimum estimated prediction error. You will learn how to use fit criteria (such as AIC, SBC, and the Cp statistic), average square error on the validation data, and cross validation as tuning methods for penalized regression. Various examples will be provided using the GLMSELECT and HPGENSELECT procedures of SAS/STAT, which offer extensive customization options and powerful graphs for performing statistical model selection.

Outline & Objectives

Outline:
1. Introduction
a. Goals of model selection
2. Model Selection methods
a. PROC GLMSELECT
b. PROC HPGENSELECT
c. Traditional Selection Methods
d. Modern Selection Methods
3. Penalized Regression Methods
a. LASSO
b. Adaptive LASSO
c. Elastic Net
d. Group LASSO
e. Validation
4. Model Averaging
5. Screening
6. Summary

Objectives
1. Introduce variable selection methods and penalized regression methods
2. Illustrated practical applications of using variable selection methods for both linear and generalized linear models
3. Provide guidelines for choosing the ‘best’ model fitting tools for a given problem
4. Demystify the methodology.

About the Instructor

Dr. G. Gordon Brown is a Senior Research Statistician in the Statistical Applications R&D department at the SAS Institute. Before joining SAS in 2015 Dr. Brown performed contract research for 14 years specializing in survey data analysis, regression modeling, and environmental statistics. Since joining SAS he has given several presentations and tutorials at various conferences and SAS users' group meetings. He has a Ph.D. in Statistics from North Carolina State University and has been a SAS user since 1989.

Relevance to Conference Goals

The term ‘Big Data’ typically conjures up images of data sets with a large number of observations. However, it is becoming increasingly common for data sets to have a large number of variables as well. Selecting a set of variables that accurately predict an outcome of interest without overfitting the model is difficult in this situation. The modern penalized regression methods presented in this tutorial provide the data analyst with the tools they need to build parsimonious regression models.

Software Packages

SAS

 
T3 Introduction to Spatial Analysis Through Statistics
Sat, Feb 25, 2:00 PM - 4:00 PM
River Terrace 3
Instructor(s): Michael Devin Floyd, Saint Software; Phillip Stedman Floyd, Segal Consulting

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The word spatial means related to space or geography. Thus, spatial analysis is an analysis that takes into consideration the location of the observation. This course is about using spatial elements to derive conclusions or eliminate dependence based on location. This course assumes no prior knowledge of spatial analysis. It starts from the beginning by defining spatial data and reasoning why spatial analysis is relevant. Different mapping techniques are explored to visualize spatial information to get a better understanding. Test for spatial dependence in datasets are discussed. Then, it is shown that spatial dependence can influence results. Spatial regression techniques are discussed to mitigate the spatial dependence. For the conclusion, I talk about how accounting for the spatial dependence influenced the research I did at the Louisiana Public Health Institute. Basic knowledge of regression and linear modeling is assumed.

Outline & Objectives

Introduction: Define spatial data and spatial objects to start the discussion of spatial analysis. Talk about mapping techniques that can be used to visualize the spatial data. Give examples in different software packages.

Spatial dependence: Give an example of spatial dependent data. Talk about the necessity to deal with the spatial dependence/relevance. Define the spatial weight matrix. Talk about the different ways of creating this matrix. Talk about spatial autocorrelation, global indexes, and local indexes. Describe how they are defined/formed. Comment on the differences.

Spatial regression: Spatial weight matrix can be added to any linear model to account for spatial autocorrelation. Two main forms are spatial lag and spatial error models. Describe the difference. Redo previous example with added spatial weight matrix. Give code for different programs.

Research example: Talk about research done at the Louisiana Public Health Institute (LPHI). Goal of the research was to determine if the number of tobacco stores present in an area of New Orleans was related to different economic and demographic conditions. Describe modeling process. Talk about results.

About the Instructor

Phillip Floyd:
B.S. Pure Mathematics - Louisiana State University
M.S. Statistics - The University of New Orleans
3 years of actuarial and statistical consulting experience
GStat Accredited
Poster presentation at CSP 2016

Michael Floyd:
B.S. Pure Mathematics - The University of Louisiana at Lafayette
M.S. Biostatistics - Washington University in St. Louis
2 years of statistical research experience
Poster presentation at CSP 2016

Phillip Floyd was a statistical consultant for the Louisiana Public Health Institute where he used spatial techniques to analyze the data of a tobacco study in the city of New Orleans. His methods would be used continuously in the future so longitudinal results can be formed and causation can be concluded. A paper was pending publication when his statistical consulting work ended.


Relevance to Conference Goals

Spatial analysis can be used across many industries. When any analysis is being done and a variable is derived based on location, spatial effects should at least be tested for. If found to be significant, spatial techniques can be easily added to any linear model. There are a lot of techniques that can be used but the basic concept is to add a weight matrix to the model to eliminate the autocorrelation in the data. It’s not a topic seen often but a statistician should be aware that these techniques exists in case the need arises.

Software Packages

Examples will given in SAS, R, and STATA. The code will be given for all of the examples.

 
T4 How to Find (the Right) Clients for Your Independent Statistical Consulting Business
Sat, Feb 25, 2:00 PM - 4:00 PM
River Terrace 2
Instructor(s): Karen Grace-Martin, The Analysis Factor

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If you are starting an independent statistical consulting business, you will need to learn many business skills. The most important, yet intimidating, of these is finding and attracting clients.

Clients will hire (and re-hire) you only if they know, like, and trust you. This will only happen when you build a solid marketing process that conveys your strengths and what you can offer to the right clients. In this tutorial, you will learn about how to approach and get started creating a simple, yet solid, marketing plan that allows the right clients to know, like, and trust you.

The instructor will share her personal experiences and case studies of colleagues who built a consulting business and guide you through small group exercises.

Outline & Objectives

The tutorial will be set up in two parts. In the first, we will build the foundation of your marketing plan. This will include deciding what you want to communicate and to whom. We’ll focus on some fundamentals of establishing credibility through how you present yourself and your business through written and web material.
In the second part, the instructor will share approaches on how to develop your reputation as an expert statistician and get your message out to the world. Social media offers many new and interesting ways to get recognition, but you should not ignore some of the more traditional approaches.
Both parts will include small group exercises to develop a message and strategy that emphasizes your unique skills and strengths.

About the Instructor

Karen Grace-Martin is the founder of The Analysis Factor LLC, which provides statistical consulting and training to researchers and was previously a statistical consultant at Cornell University for seven years. She has consulted on thousands of research projects, from undergrad honor's theses to large-scale randomized trials. She is well versed in the challenges, rewards, and differences in consulting as an academic employee and self-employed business owner.

She runs the popular The Analysis Factor blog, StatWise newsletter, and The Data Analysis Brown Bag webinar series. Learn more about Karen at http://TheAnalysisFactor.com.

Relevance to Conference Goals

This tutorial directly addresses the communication, impact, and career development theme of this conference. If you are beginning a career as an independent statistical consulting, you will need to develop management competencies in marketing and promotion. Your clients need to know who you are and they need to know the expertise that you can provide them. Your success as an independent consultant will depend on your ability to communicate clearing and interact in a friendly but professional manner. This helps you build a collaborative working relationship that will win you repeat business and referrals.

Software Packages

Not applicable.