CSP 2017 Online Program

Outline & Objectives

Outline:

1. Review of classification trees. Comparisons of algorithms on prediction accuracy, computation, and selection bias.

2. Review of regression trees for least squares, quantile, Poisson, and relative risk regression. Effect of collinearity, nonlinearity, variance heterogeneity, and missing data.

3. Importance scoring of variables.

4. Inference for tree models. Bootstrap trees and confidence intervals.

5. Tree ensembles.

6. Step-by-step analysis of real data using free software, from data exploration to final model. Examples include:

(a) Subgroup identification for precision medicine in a breast cancer trial with censored response.

(b) Subgroup identification in a diabetes trial with longitudinal responses.

(c) Missing value imputation and propensity score estimation for the U.S. Consumer Expenditure Survey.

(d) Analysis of data on circuit board soldering from a factorial design.

(e) Joint modeling of mother's stress and child's morbidity in a longitudinal study.

Objectives:

1. Reveal the power and versatility of tree models.

2. Show how to exploit advanced features of existing software.

About the Instructor

Wei-Yin Loh has been actively doing research in the subject for almost thirty years. He is the developer or co-developer of the FACT, QUEST, CRUISE, LOTUS and GUIDE algorithms and has supervised more than twenty PhD theses in this area. He has given one and two-day courses on classification and regression trees to professional societies (KDD 1999, 2001; JSM 2007, 2011, 2013, 2015; U.S. Army Applied Statistics Conference 1995, 1999; Interface Conference 2013; ASA Northeastern Illinois Chapter 2014; ICSA Applied Statistics Symposium 2015; Midwest Biopharmaceutical Statistics Workshop 2015; Washington Statistical Society 2015), major biopharmaceutical companies, and overseas universities (National University of Singapore 2010 and 2014; East China Normal University 2012; National Tsinghua University, Taiwan, 2012; City University of Hong Kong 2014). He is a consultant on regression tree methods to government and industry. He regularly teaches a semester-long graduate course on the subject at the University of Wisconsin, Madison.

Relevance to Conference Goals

The course will introduce to beginners a new set of statistical tools that is time-tested and is used increasingly in academia and industry. It will teach non-beginners how to exploit the features of existing software, such as their use in simulation experiments. In addition, the course will teach attendees how to respond to questions about tree models, such as their interpretation and statistical significance, and how they compare with models from traditional methods in terms of prediction accuracy, underlying assumptions, and sensitivity to outliers, collinearity, and missing values. The course will have an immediate positive impact on a statistician's job by increasing his/her array of tools.

Software Packages

GUIDE: http://www.stat.wisc.edu/~loh/guide.html

R packages: rpart, randomForest, party

Outline & Objectives

1. Gain a better understanding of leadership, including:
- How established leaders in our profession have developed their leadership skills;
- Insights and perspectives on leadership from other professional statisticians gained through interactions, discussions and group work that will improve each attendee’s ability to lead
- Inspiration to become a leader

2. Improved ability to recognize and develop characteristics of leaders, including:
- Ideas on how to acquire greater knowledge in organizational/business acumen
- Insights into the importance communication plays in leadership;
- Other qualities that impact a leader’s ability to influence;

3. Develop a path for your own leadership development, including:
- A draft of your own leadership principles and a plan for continuing your leadership journey;
- Membership in a small peer leadership group that will continue to share learning, perspectives, experiences and ideas on leadership after completing the workshop

About the Instructor

Gary R. Sullivan, Ph.D., is the senior director of Non-Clinical Statistics at Eli Lilly and Company, a major pharmaceutical manufacturer headquartered in Indianapolis, IN. Gary joined Lilly in 1989 and has 13 years of experience as a project statistician where he collaborated with pharmaceutical formulators, chemists, biologists, and engineers on formulation design, process optimization & modeling, assay development & characterization, and production monitoring. He has spent the last 14 years in various leadership positions with responsibilities for statisticians collaborating in manufacturing, product development, discovery research, and biomarker research. His personal passions include quality management, experimental design, process optimization and leadership development. Gary has developed leadership training for the Biometrics organization at Eli Lilly, proposed and facilitated the initial JSM leadership course in 2014, and is the current chair for the ASA Ad Hoc Leadership Committee. Gary received his Ph.D. in statistics in 1989 from Iowa State University working with Dr. Wayne A. Fuller.

Matthew J. Gurka, Ph.D., is a Professor in the Department of Health Outcomes and Policy at the University of Florida (UF), where he is also the Associate Director of the Institute for Child Health Policy. Prior to his recent appointment at UF, Matthew was the Founding Chair of the Department of Biostatistics in the School of Public Health at West Virginia University, where he also led the Clinical Research Design, Epidemiology, and Biostatistics Program of the West Virginia Clinical and Translational Science Institute (WVCTSI). Matthew received a Ph.D. in biostatistics in 2004 from the University of North Carolina at Chapel Hill. In addition to his interests in mixed models and power analysis, Matthew has extensive collaborative and independent research experience in pediatrics. Recently he has focused on childhood and adult obesity, where he has obtained PI funding from the NIH to develop and validate improved measures of the metabolic syndrome. He recently completed a term on the Executive Editorial Board of the journal Pediatrics and is currently on the Editorial Board of the Journal of Pediatrics. Matthew has participated in the JSM leadership course since 2014, and is a member of the ASA Ad Hoc Leadership Committee.

Relevance to Conference Goals

The above objectives of this course align with the goal of the Conference on Statistical Practice to “provide opportunities for attendees to further their career development and strengthen relationships in the statistical community.” Understanding leadership and learning ways to develop leadership skills further are important for the continued development of one’s career. In addition, this course provides the opportunity to build peer relationships among fellow participants who share an interest in leadership.

Software Packages

NA

Outline & Objectives

Outline of course topics:

(1)Identifying and fitting ARMA models,

(2)Diagnostics,

(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.

Software Packages

The instructor will also demonstrate to produce the above mentioned high-quality figures in listing (EMF, EPS, etc.) and document formats (RTF, PDF etc.).

Outline & Objectives

The proposed half day short course will cover the followings.

1) Publication requirements on high-quality figures

2) SAS/GRAPH options and formats for high-quality figures

3) Producing high-quality figures in listing format (EMF, WMF, PS, EPS, etc.) and document format (PDF, RTF, etc.)

4) Demonstrate how to produce some commonly used types of figures, including line plots, jittered scatter plots, line-up jittered scatter plots (AKA bee swarm plot), box plots, scatter plot overlaid with box plots etc.

5) Producing high-quality figures in SAS Enterprise Guide (EG) environment

About the Instructor

Charlie Liu, PhD is the author of the book "Producing High-quality Figures Using SAS/GRAPH and ODS GRAPHICS Procedures", by CRC Press, Taylor & Francis Group in 2015. (https://www.crcpress.com/Producing-High-Quality-Figures-Using-SASGRAPH-and-ODS-Graphics-Procedures/Liu/9781482207019)

Dr. Liu has worked as a SAS programmer and project statistician for more than a decade in several research institutions, and pharmaceutical companies, including US EPA, National Institute of Statistical Sciences (NISS), Washington University Medical School at St. Louis, Eli Lilly and Company, Allergan Inc. and Kythera Biopharmaceuticals. He is now an associate director of biostatistics at Allergan PLC.

Dr. Liu is an excellent conference speaker and has presented at various SAS user conferences, including the SAS Global Forum 2013 and JSM 2015. He won an Outstanding Speaker Award at the Mid-West SAS User Group (MWSUG) Conference in 2007, Des Moines, IA.

Relevance to Conference Goals

The proposed half day short course will help the participants in areas outlined below.
1) Learn principles and techniques to produce high-quality figures in SAS to meet publication requirements. 2) Learn how to produce some commonly used graphs in SAS, including line plots, scatter plots, bee swarm plots, thunderstorm scatter plots, and box plots etc. 3) Have a positive impact on statisticians/programmers to present scientific research data using high-quality graphs

Software Packages

SAS 9.3 or higher

These topics will be addressed through the analysis of data from two studies: (1) a school-based intervention program designed to impact students’ body mass index (BMI); and (2) an animal xenograft experiment designed to assess the effects of a drug and of radiotherapy on tumor growth.

Outline & Objectives

This course is appropriate for an audience that has knowledge of statistics at the level of applied regression. The main requirement is a basic understanding of concepts of confidence intervals, statistical testing, and general regression modeling. The audience may consist of:
a. undergraduate or graduate students in statistics and related quantitative fields with biomedical focus; and
b. consulting/applied statisticians analyzing multi-level data or longitudinal data with repeated measures in the health sciences.

Participants will learn how to apply, evaluate, and interpret linear mixed-effects regression models, through two health sciences applications. Specifically, they will learn how to:
a. Perform linear mixed-effects regression modeling in SAS or Stata.
b. Specify and perform appropriate comparisons/contrasts.
c. Display results in tabular and graphical form.
d. Assess model assumptions, evaluate model fit, and compare alternative specifications/models.
e. Interpret statistical results (estimates, p-values, etc.) to address scientific objectives.
f. Communicate findings to non-statistical audiences.

About the Instructor

Dr. Daskalakis is Associate Professor of Biostatistics at Thomas Jefferson University and has 15 years of experience as a collaborating statistician in a biomedical environment. He has worked on numerous published studies involving mixed-effects modeling of both hierarchical and longitudinal data. He has taught biostatistics, clinical trials, and regression methods to non-statistical audiences (including a shorter version of the proposed course). Dr. Daskalakis has been very active in ASA’s professional activities through the Section of Teaching of Statistics in the Health Sciences.

Relevance to Conference Goals

The course is aligned with the conference’s second theme, “Data Modeling and Analysis.” In line with the conference’s goal, the course will allow participants to enhance their programming, analysis, and communication skills. It has been designed as a practical hands-on tutorial on linear mixed models, a modern regression approach to the analysis of correlated hierarchical, clustered, and/or longitudinal data. The course may have special value for consulting/applied statisticians who are a large fraction of CSP’s attendees.

Software Packages

Participants are strongly encouraged to bring their computers for hands-on practice.
The course will use SAS (Proc Mixed) and Stata (–mixed–) code and output. Both of those programs have strong capabilities in fitting linear mixed models, with user-friendly modules. In contrast, R’s packages for fitting linear mixed models (nlme and lme4) have limitations, and their syntax and use (beyond fitting simple models) can be quite complicated. For these reasons, R will not be used in the course.
Extensive SAS and Stata code for fitting linear mixed models will be provided to participants. R code will also be provided but will not be discussed.

Outline & Objectives

The course is organized into three main sections designed to cover topics ranging in degree of difficulty from the basic to the advanced:
1. Basics of Text Analytics & Techniques for Acquiring and Pre-Processing Text Data
2. Solving the Primary Text Analytics Problem - Topic Analysis
3. Integrating Structured and Unstructured Text Data

Participants with statistical programming experience will gain information on incorporating text analysis into their statistical analyses.

About the Instructor

Dr. Jones has a PhD degree Statistics from Virginia Tech and a B.S. in Computer Science from Texas A&M University - Commerce. He has over 10 years in the development of statistical and data mining software for companies in Silicon Valley and Rogue Wave Software. He designed and wrote the data mining software incorporated in IMSL, the International Mathematical and Statistics Library.?Currently he teaches data mining and analytics at Texas A&M University. He also consults with companies on business analytics and quality assurance, and is co-founder of Texas A&M Statistical Services.

Relevance to Conference Goals

Software Packages

* The three key properties of a function. * A proven strategy for writing new functions. * How to use functions to reduce duplication in your code. * How `lapply()` works and why it's so important. * A handful of FP tools that increase the clarity of your code.

This workshop is suitable for beginning and intermediate R users. You need to know the basics of R (like importing your data and executing basic instructions). If you're an advanced R user, you probably won't learning anything completely new, but you will learn techniques that allow you to solve new challenges with greater ease.

The workshop will be hands-on and interactive, so please make sure to bring along your laptop with R installed!

Outline & Objectives

In this mini-workshop you'll learn how to better express yourself in R. To express yourself clearly in R you need to know how to write high quality functions and how to use a little functional programming (FP) to solve common programming challenges.

About the Instructor

Hadley is Chief Scientist at RStudio and a member of the R Foundation. He builds tools (both computational and cognitive) that make data science easier, faster, and more fun. His work includes packages for data science (the tidyverse: ggplot2, dplyr, tidyr, purrr, readr, ...), and principled software development (roxygen2, testthat, devtools). He is also a writer, educator, and frequent speaker promoting the use of R for data science. Learn more on his website, http://hadley.nz.

Relevance to Conference Goals

Modern data analysis must be performed on a computer, and if you're doing data analysis on a computer, it's worth the investment to learn a programming language. In this tutorial, you'll learn some useful tools in R that improve your ability to automate repeated parts of your analyses.

Software Packages

R + purrr package

This short course aims to address the problems of missing data. The concept of different missing data mechanisms or typologies including missing completely at random (MCAR), missing at random (MAR) and missing not at random (MNAR) will be discussed with illustration from real clinical trial examples. Moreover, this short course will introduce how to conduct two commonly-used model-based methods for missing data analysis including the multiple imputation (Little & Rubin, 2002; Reiter & Raghunathan, 2007) and maximum likelihood (Allison, 2012) using R/SAS. Some sensitivity analysis approaches to handle missing data under MNAR will also be discussed briefly.

Outline & Objectives

This short course will:

a) review missing data issues and different missing data mechanisms (i.e., MCAR, MAR and NMAR);


b) introduce the multiple imputation and maximum likelihood methods in regression modeling and discuss the advantages and disadvantages of each method;


c) illustrate using R/SAS to analyze real data from clinical trial studies and compare the consistence from different software.

About the Instructor

Dr. Din Chen is a Fellow of ASA. He is now the Wallace H. Kuralt distinguished professor, Director of Consortium for Statistical Development and Consultation, School of Social Work and professor in biostatistics at the Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill. He was a professor in biostatistics at the University of Rochester and the Karl E. Peace endowed eminent scholar chair in biostatistics at Georgia Southern University. Professor Chen is also a senior statistics consultant for biopharmaceuticals and government agencies with extensive expertise in clinical trials and bioinformatics. He has more than 100 referred professional publications and co-authored 10 books in clinical trial methodology and public health applications. Professor Chen was honored with the "Award of Recognition" in 2014 by the Deming Conference Committee for highly successful biostatistics workshop tutorials.

His “Applied Meta-analysis” short course in 2016 at CSP was well received by his attendees.
Dr. Frank Liu is Distinguished Scientist, Biostatistics at Merck Research Laboratories. He has over 21 years of pharmaceutical industry working experiences on supporting multiple therapeutic areas including neuroscience, psychiatry, infectious disease, and vaccine products. His research of interests includes methods for longitudinal trials, missing data issues, safety data analysis, and design and analysis of non-inferiority trials. He has published more than 30 statistical papers/book chapters, and presented talks at professional meetings regularly. He has co-led a subteam for Bayesian missing data analysis in DIA Bayesian Working Groups, and co-taught short courses on “Sensitivity analysis using Bayesian and Imputation Approaches” at Deming conference 2015, and on “SAS Biopharmaceutical Applications” at Regulatory-Industry Workshop 2016. He has been leading working groups within Merck on developing several guidance documents on analysis of missing data.

Relevance to Conference Goals

1. To give an up-to-date development in missing data analysis so to guide the participants to learn the techniques about missing data imputation and maximum likelihood methods


2. To give an overview of R/SAS implementations for missing data analysis

3. To emphasize the applied aspects of how to deal with missing data with real examples and help attendees to solve their real-life problems from research and consulting as an applied statisticians and analysts

Software Packages

R, SAS

We consider a variety of statistics (mean, trimmed mean, regression, etc.), and a number of sampling situations (one-sample, two-sample, stratified, finite-population), stressing the common techniques that apply in these situations. We'll look at applications from a variety of fields, including telecommunications, finance, and biopharm.

These methods let us do confidence intervals and hypothesis tests when formulas are not available. This lets us do better statistics, e.g. use robust methods (we can use a median or trimmed mean instead of a mean, for example). They can help clients understand statistical variability. And some of the methods are more accurate than standard methods.

Outline & Objectives

Introduction to Bootstrapping
General procedure
Why does bootstrapping work?
Sampling distribution and bootstrap distribution

Bootstrap Distributions and Standard Errors
Distribution of the sample mean
Bootstrap distributions of other statistics
Simple confidence intervals
Two-sample applications

How Accurate Is a Bootstrap Distribution?

Bootstrap Confidence Intervals
Bootstrap percentiles as a check for standard intervals
More accurate bootstrap confidence intervals

Significance Testing Using Permutation Tests
Two-sample applications
Other settings

Wider variety of statistics
Variety of applications
Examples where things go wrong, and what to look for

Wider variety of sampling methods
Stratified sampling, hierarchical sampling
Finite population
Regression
Time series

Participants will learn how to use resampling methods:
* to compute standard errors,
* to check the accuracy of the usual Gaussian-based methods,
* to compute both quick and more accurate confidence intervals,
* for a variety of statistics and
* for a variety of sampling methods, and
* to perform significance tests in some settings.

About the Instructor

Dr. Tim Hesterberg is a Senior Statistician at Google. He previously worked at Insightful (S-PLUS), Franklin & Marshall College, and Pacific Gas & Electric Co. He received his Ph.D. in Statistics from Stanford University, under Brad Efron.

Hesterberg is author of the "Resample" package for R and primary author of the "S+Resample" package for bootstrapping, permutation tests, jackknife, and other resampling procedures, is co-author of Chihara and Hesterberg "Mathematical Statistics with Resampling and R" (2011), and is lead author of "Bootstrap Methods and Permutation Tests" (2010), W. H. Freeman, ISBN 0-7167-5726-5, and technical articles on resampling. See http://www.timhesterberg.net/bootstrap.

Hesterberg is on the executive boards of the National Institute of Statistical Sciences and the Interface Foundation of North America (Interface between Computing Science and Statistics).

He teaches kids to make water bottle rockets, leads groups of high school students to set up computer labs abroad, and actively fights climate chaos.

Relevance to Conference Goals

Resampling methods are important in statistical practice, but have been omitted or poorly covered in may old-style statistics courses. These methods are an important part of the toolbox of any practicing statistician.

It is important when using these methods to have some understanding of the ideas behind these methods, to understand when they should or should not be used.

They are not a panacea. People tend to think of bootstrapping in small samples, when they don't trust the central limit theorem. However, the common combinations of nonparametric bootstrap and percentile intervals is actually accurate than t procedures. We discuss why, remedies, and better procedures that are only slightly more complicated.

These tools also show how poor common rules of thumb are -- in particular, n >= 30 is woefully inadequate for judging whether t procedures should be OK.

Software Packages

I mention the R resample and boot packages, but this is not a focus of the course.

The single most important thing we need to recognize about marketing mix modeling is that it is a causal question. This means, we are not looking for a prediction of an outcome variable based on the observation of marketing variables. Rather, we are looking to manipulate marketing variables to optimize an outcome variable. Thus, we are performing an intervention, which requires us to perform causal inference. This leads us to the Holy Grail of statistics, i.e. causal inference from observational data.

In this workshop, we introduce the basic concepts of graphical models and how they can help us perform causal identification, e.g. using causal assumptions and the well-known Adjustment Criterion. While this is straightforward in theory, the complexity of the marketing domain prevents the practical application of this criterion. Thus, we introduce a new criterion (Shpitser and VanderWeele, 2011) that reduces the number of assumptions that we require for confounder selection and causal identification.

Implementation with BayesiaLab With the confounders identified, we can now build a high-dimensional statistical model that represents the joint probability distribution of all marketing variables. We do that using the machine-learning algorithms of the BayesiaLab software platform. We obtain a Bayesian network that represents a multitude of relationships between all marketing variables and the outcome variable. Using BayesiaLab’s visualization functions, we can compare the machine-learned graph to our understanding of the domain. Furthermore, we can examine the (mostly nonlinear) response curves of the outcome variable as a function of the marketing variables. Most importantly, we use BayesiaLab to perform Likelihood Matching on all confounders to establish the causal response of the outcome variable.

With all causal response curves computed, we introduce cost functions for the marketing variables via BayesiaLab’s Function Node. On that basis, we proceed to BayesiaLab’s Target Optimization function, which, by means of a genetic algorithm, searches for an optimal combination of all marketing variables, while being subject to constraints of individual variables and an overall marketing budget constraint. The optimization report shows feasible solutions along with the degree of achievement.

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.

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

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.

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.