Abstracts

Delicate Brute Force

John Sall, SAS

Sometimes the way to solve a challenging problem is to just take advantage of the speed of computers and apply a brute-force approach. In one situation, the problem was to find a design space (a set of factor limits that leads to satisfying specification limits on the responses), specifically finding the space that fits within the highly nonlinear constraints implied by the model from limits on the responses. Another problem was to perform hierarchical clustering on the kinds of huge data sets encountered in genomics research, something that used to perform poorly. In each case, rather than solving the problem directly, there are short cuts that combine brute-force computation with delicate application.

Facets of a Diverse Career

Alyson Wilson, North Carolina State University

My career has embodied John Tukey's opinion that “The best thing about being a statistician is that you get to play in everyone’s backyard.” I have been fortunate to work in academia, industry, and government; for a small business and a large bureaucracy; on collaborative projects with partners from many disciplines; on topics ranging from reliability to counterproliferation to atomic-scale characterization of materials. I will highlight some of the inflection points in my career and share lessons I have learned about the value of diverse practitioners with diverse perspectives collaborating on solutions.

Beginner:	1
Intermediate:	2
Advanced:	3
Power User:	4

Session ID: 2021-US-30MP-809

47 Years (So Far) with Designed Experiments from One-Factor-at-a-Time Designs to GO-SSD Designs

Ronald Andrews, Sole Proprietor, Practical Stats

Topic: Data Exploration
Level: I

Like most young engineers at Kodak, I started out running one-factor-at-a-time designs, until we needed to optimize a five-component solution. After successfully optimizing this solution that had two statistically significant three-way interactions, I was hooked on designed experiments. As computer tools progressed, we moved from pencil and paper to in-house mainframe programs. In the 1980s we started using mainframe SAS. In the 1990s we had JMP, but our DOEs were still basically catalog designs. In the late 1990s when JMP added optimal designs, we had some new tools. Experiments didn’t have to be orthogonal. We could run smaller experiments and still get the necessary data, which was very important in the waning days of the Kodak Research Labs when strict quotas were placed on the number and size of experiments. When Bradley Jones and Christopher Nachtsheim introduced definitive screening experiments in 2011, I had moved on to Bausch + Lomb. We have had more than one case where DSDs identified antagonistic interactions early enough to avoid program delays. DOEs in retirement are simpler, focusing on things like optimizing an ice cream recipe and reducing the amount of time to produce maple syrup on a conventional stove.
Session ID: 2021-US-30MP-911

A JMP Script That Determines a Simultaneous 95% Bound Using a K-Nearest Neighbor Approach

Rich Newman, Statistician, Intel

Don Kent, Data Analytics and Machine Learning Manager, Intel

Topic: Data Exploration
Level: II

We have a set of responses that follow some continuous, unknown distribution with responses that are most likely not independent. We want to determine the simultaneous 95% upper or lower bound for each response. As an example, we may want the lower y1 and y2 and upper y3 and y4 bounds such that 95% of the data is simultaneously above y1 and y2 and below y3 and y4. Finding the 95% bound for each response leads to inaccurate coverage. The solution: a method to calculate the simultaneous 95% upper or lower bound for each response using nearest neighbor principles by writing a JMP script to perform the calculations.
Session ID: 2021-US-45MP-804

A MaxDiff Bake-Off Study: Finding the Most Preferred Among Dozens of Products

Bradley Jones, JMP Distinguished Research Fellow, SAS

Rob Reul, Managing Director, Isometric Solutions

Topic: Data Visualization
Level: II

A MaxDiff study is a way of ordering consumers’ preferences for a group of similar currently marketed products. As the number of products to be compared increases, the cognitive burden on respondents can rise to the point that few will complete the survey, which could lead to potential bias. In our case study, we were tasked with ordering preferences for three dozen (36) products. We addressed the problem by first dividing the 36 products into four mutually exclusive groups of nine products and did separate MaxDiff studies on each. The “bake-off” consisted of a new MaxDiff study pitting the most preferred products from each of the four groups.

We present the analytical results of the study using JMP. We also show the economic savings resulting from streamlining the original offering of 36 products to a substantially smaller group of preferred products.
Session ID: 2021-US-30MP-920

A Model for COVID-19 Vaccine Adverse Reaction

Suling Lee, SMU, Singapore Management University

Topic: Design of Experiments
Level: I

The COVID-19 vaccines are crucial to ending the global pandemic that has caused surges of infections and deaths globally. However, the unprecedent rate at which they were developed and administered had raised doubts in the community regarding their safety. Data from the United States Vaccine Adverse Event Reporting System, VAERS, has the potential to help determine if the safety concerns of the vaccines are founded. This paper uses the combination of both structured and unstructured variables from VAERS to model the adverse reactions of COVID-19 vaccines.

The severity of an adverse reaction is first derived from the variables describing the vaccine recipient outcome following a reaction. Next, unstructured data in the from of text describing symptoms, medical history, medication and allergies are converted into a document term matrix and then combined with the structured variables to build a model to predict for the severity of the adverse reaction.

The predictive model is build using JMP Pro 16 and SAS Enterprise Miner 14.1, using logistic regression and decision tree with both binary document term matrix and term frequency inverse document frequency, with the model evaluation based on lowest misclassification area. The best fit model is a logistic regression model for ordinal target variables. The key determinants contributing to an adverse reaction from the optimal model are age, number of symptoms, period between vaccination onset, sex, state, type of vaccine, how the vaccine was administered, symptoms, history of dementia, and history of chronic obstructive pulmonary disease.
Session ID: 2021-US-30MP-916

A New Resolve to Dissolve: Model Drug Dissolution with Functional DOE

Amanjot Kaur, Statistician, Perrigo

Rob Lievense, JMP Senior Systems Engineer, SAS

Topic: Design of Experiments
Level: II

Formulation scientists put forth significant hours of work attempting to find an extended release formulation that matches drug release targets. For generic (ANDA) products, the primary objective is to match critical quality attributes (including dissolution over time) to the reference listed drug (RLD). This paper illustrates how functional DOE is an extremely robust and easy-to-use technique for optimizing to a target dissolution profile rapidly with fewer resources.

Dissolution data is collected over time during development and in-depth analyses are required to understand the effect of the formulation/process variables on the product performance. Regression models for specific time points (e.g., 1, 2, 6, and 12 hours) have been typically used to correlate the release responses with the formulation/process variables; however, such analyses violate the assumption of independence for the responses. There is great need for robust statistical tools used to determine the levels of inputs needed to get the closest profile of the developmental product to the reference drug. Functional DOE in JMP for model dissolution is a new and critical tool to use within a drug development program.
Session ID: 2021-US-45MP-807

Accelerating Innovation with Space Filling Mixture Designs, Neural Networks and SVEM

Philip Ramsey, Data Scientist and Professor, North Haven Group and University of New Hampshire

Marie Gaudard, Professor, Predictum

Wayne Levin, Owner, Predictum

Topic: Design of Experiments
Level: II

Although critical to process/product development and improvement, constrained mixture and mixture process designs are challenging to generate and analyze. Classical mixture and mixture process designs typically do not provide adequate coverage of the design space, assigning too many design points to boundary areas. Also, classical mixture process designs are often too large to be feasible. Furthermore, the large amount of partial aliasing inherent in constrained mixture designs renders them difficult to analyze, especially for predictive modeling purposes.

In this talk, we propose new classes of efficient constrained mixture and mixture process designs based upon the fast flexible space filling designs available in JMP. These designs provide much improved coverage of the design space and are generated without the requirement to pre-specify a model.

Through simulation and case studies we show how these new designs, coupled with self-validating ensemble modeling (SVEM) to fit neural networks, result in effective predictive models. These models provide a basis for process characterization and optimization, thereby ensuring better scale-up and scale-down model performance. We demonstrate how to generate the designs and conduct the SVEM analyses using JMP 16.
Session ID: 2021-US-30MP-905

An Analysis of ETFs in China's Stock Market Using Dynamic Time Warping

Lim Yong Kai, Student, Singapore Management University

Chen Li Student, Singapore Management University

Topic: Data Exploration
Level: I

Economic theories are supposed to interpret why and how the economy behaves and then determine the best solutions to influence or solve the economic phenomena. However, these theories are full of assumptions, hypotheses and contexts, in terms of moral values and politics. Price movements of stock markets have never been explained well by any economic theories. Hence, our question as investors: Is there a holistic way to understand the price movements in the market without application of any economic theory? An option would be to use unsupervised learning to detect objective patterns of the subject without the requirement of any domain knowledge. We believe one approach to understand real-world complexity is to get the pattern first, followed by forming and studying the theories.

In this paper, we explore more than 100 ETFs in China's stock market without prior domain knowledge of each ETF by using dynamic time warping clustering (DTW) and the agglomerative hierarchical clustering method to detect the similarities of their price movements. Our results show that clusters from the DTW method largely coincide with the type of industries that ETFs involve. Analysis of the clusters’ price movement also revealed that certain industries performed better after 2019, when compared to 2018 in light of China’s new self-reliance economic direction.
Session ID: 2021-US-45MP-880

Analysis of a Kamyr Digester Continuous Flow Process Stream Using JMP Pro 16

Stanley Siranovich, Principal Analyst, Crucial Connection LLC

Topic: Quality and Reliability
Level: III

In this session, we analyze actual process data from a Kamyr digester at a Canadian paper mill. Our data set consists of 301 measurements of 23 variables, taken over more than 12 days. Our dependent variable, which we want to predict and control, is the Kappa Number. It is roughly proportional to the residual lignin content in the pulp and is a measure of the effectiveness of the pulping process. For independent variables, we have a combination of chip, steam, air, and liquor flow rates, along with temperature, level, and moisture measurements. Several characteristics make this data set particularly challenging:

The division of the continuous reactor into separate zones, each of which performs a different function.
Separate heat inputs, plus liquid inputs and outputs for each zone.
Countercurrent liquor flows.

Because of the above (and because we want to understand the process, not just predict an outcome variable), we use several of JMP’s platforms from the Predictive Modeling, Screening, and Multivariate Methods menus. Finally, to demonstrate analytical flow and successive discovery, we conduct most of the session as a live demo.
Session ID: 2021-US-30MP-913

Analyzing Functional Data with Direct Functional PCA in Functional Data Explorer

Ryan Parker, JMP Senior Research Statistician Developer, SAS

Topic: Design of Experiments
Level: III

In JMP Pro 16, the Direct Functional PCA (DFPCA) modeling option has been added to the Functional Data Explorer (FDE) to provide a way to perform functional PCA without first fitting basis function models. This approach not only makes larger functional data more tractable, but it also provides a more hands-off approach to analyzing functional data. This presentation details how DFPCA works and presents examples that highlight how and when to use DFPCA to analyze functional data.
Session ID: 2021-US-45MP-878

1:30-2:15 p.m. ET

Analyzing Spectral Data: Preprocessing and Beyond

Bill Worley, JMP Senior Global Enablement Engineer, SAS

Jeremy Ash, JMP Analytics Software Tester, SAS

Topic: Data Exploration
Level: II

JMP is advancing the capabilities for analyzing spectral data from preprocessing to building predictive models. Visualization, preprocessing and model development are shown in this presentation. Spectroscopists, analytical chemists and material scientists will take something away from this presentation. We review and demonstrate standard normal variate (SNV) and Savitsky-Golay (SG) smoothing using JMP. We then share more advanced preprocessing tools and show how Functional Data Explorer can be used to visualize and qualitatively analyze spectra. Using JMP, partial least squares and generalized regression are demonstrated as model development tools for spectral data.
Session ID: 2021-US-30MP-910

As Easy as Uploading a Photo: Building, Sharing, and Updating Add-ins Automatically

Michael Akerman, Senior Data Scientist, Novozymes

Johan Pelck Olsen, Data Scientist, Novozymes

Topic: Data Access and Manipulation
Level: III

The difficulty introduced by a democratizing software like JMP is handling the vast number of users creating and sharing tools and scripts across a large, global corporation. At Novozymes, we've built an automatic toolset called Y'all to help address that need. Y'all is a Python build script, automated workflow, and JMP add-in package that makes building, sharing, and updating JMP add-ins as easy as uploading a photo to Facebook.

Utilizing the continuous integration/continuous deployment capabilities of GitLab (an open-source competitor to GitHub), Y'all allows users to upload raw JSL scripts saved from any platform in JMP through a web user interface, immediately launching an automated Python build script that structures and packages a JMP add-in on the GitLab server. The next time JMP loads, our tool automatically checks for updates (based on work shared by Jeff Perkinson in the JMP community), sending new or updated scripts to users within seconds.

In addition to these capabilities, Y'all allows users to specify the menu name and tooltip of their launchable script, automatically loads a variety of convenient utility functions for JSL coding, automatically adds imported functions to the Scripting Index, and leverages GitLab to allow parallel development of scripts while retaining a full version history of each JSL script and the add-in package, ensuring that valuable work is archived and protected in a centralized system.
Session ID: 2021-US-45MP-821

Beyond A/B: Case Study of Multivariate Test Design and Advanced Analytics for Webpage Optimization

Steven Crist, Analytics Consultant, Wells Fargo

Topic: Data Exploration
Level: II

It is well known that optimization of the layout and content of webpages can be achieved through thoughtful pre-test design of experiment (DOE), post-test analysis, and identification and productionization of a winning variant webpage. The present case study demonstrates the use of the JMP custom DOE platform to create a fractional factorial multivariate DOE for a financial services checking account webpage. It effectively managed business constraints while providing the necessary data that led to a 7% increase in application volume as compared to the legacy webpage. Additionally, leveraging the JMP partitioning model platform, an additional key insight was discovered that mobile visitors who clicked on the "compare accounts" link were two times more likely to submit an application. This compare accounts insight was not the main inquiry of the original test but provided guidance for future testing to further optimize the webpage. The presented case study demonstrates the effectiveness of the testing continuum of a test leading to actionable insights resulting in the next optimization test and so on.
Session ID: 2021-US-30MP-887

Biopharmaceuticals' New Frontier: Modeling Patient/Donor Variability for Gene/Cell Therapy in JMP

Andrew Karl, Senior Management Consultant, Adsurgo LLC

Heath Rushing, Principal Consultant, Adsurgo LLC

Topic: Design of Experiments
Level: II

The most novel, innovative and promising therapeutics in biopharmaceuticals are gene and cell therapies. Gene therapies modify a patient’s existing genes, while cell therapies transfer human cells into a patient to treat disease. These cells either come from the patient themselves or a healthy (cell) donor. The first such therapy approved in the United States was Kymriah in 2017. Since that time, there have been countless companies exploring these novel approaches to treating patients. One of the main barriers to regulatory approval is the development of a process that consistently meets the needs of patients (in both efficacy and safety). One obvious challenge is modeling either patient-to-patient or donor-to-donor variability that can (and usually does) account for a large portion of the variability.
This talk focuses on different methods of modeling donor-to-donor variability in JMP to answer important research questions: determining significant process parameters (independent of patients/donors), constructing proven acceptable and normal operating ranges (PAR and NOR), and estimating process capability. After demonstrating the implications of modeling donor as a fixed block effect, the authors then present the advantages to modeling donor as a random effect, first by presenting modeling and simulation features in JMP and then additional features in JMP Pro.
Session ID: 2021-US-30MP-823

But Now What Do I Do? Using a Reliability Model to Make Event Predictions in JMP

Bradford Foulkes, GE Renewables, GE

Topic: Quality and Reliability
Level: II

After spending weeks or months pulling together data and building reliability models, often the feeling is "Now what?" Or maybe the question is, “How many will fail?” In JMP Pro, there is a platform that can answer these questions, with some tweaks. The repairable systems simulation (RSS) platform allows you to enter a reliability model, or a system of models, to see how frequently the event will occur and what the impact could be. In this presentation, I explain how to go from reliability model to event prediction, via a method to automate the generation of the RSS platform. Once you have the output, I show how to build reports to answer the question around event prediction, annual downtime values, and individual models in a system. This presentation covers hands-on examples, as well as how to use JSL to make the platform and report generation easier.
Session ID: 2021-US-30MP-877

Combining DOE and First-Principles Science to Maximize Yield and Minimize Impurity with Curve DOE

Brian Taylor, Statistician, AstraZeneca

Chris Gotwalt, Chief Data Scientist, JMP

Topic: Design of Experiments
Level: II

Recent releases of JMP Pro have greatly expanded the modeling of functional response data with the Functional Data Explorer (FDE). FDE fits flexible smoothing splines to the response trajectories, extracts functional principle components (FPC) scores from the spline models, and then applies DOE methods to these FPCs. This empirical, data-centric approach ignores the knowledge of subject matter experts who may know the equations that describe the response curves based on first-principles science. In JMP Pro 16, Curve DOE is a new addition to the Fit Curve platform. With it, one chooses an equation suggested by the first-principles subject matter knowledge. The first-principles model is then fit separately to the individual response curves; the parameters of these nonlinear equations are modeled using generalized regression, just as FDE models FPC scores. By directly incorporating engineering and scientific knowledge via these equations, models more realistically extrapolate to new combinations of factor settings not present in the data, while also being more robust to missing data and functions sampled over different ranges of X. Applying this approach to pharmaceutical product development (simultaneously to product yield and impurity reaction curves) illustrates the ease of use and practical statistical advantages of using model selection criteria.
Session ID: 2021-US-45MP-838

Controlling Extrapolation in the Prediction Profiler in JMP Pro 16

Laura Lancaster, JMP Principal Research Statistician Developer, SAS

Jeremy Ash, JMP Analytics Software Tester, SAS

Christopher Gotwalt, JMP Director of Statistical R&D, SAS

Topic: Data Visualization
Level: II

Uncontrolled model extrapolation leads to two serious kinds of errors: (1) the model may be completely invalid far from the data, and (2) the combinations of variable values may not be physically realizable. Using the Profiler to optimize models that are fit to observational data can lead to extrapolated solutions that are of no practical use without any warning. JMP Pro 16 introduces extrapolation control into many predictive modeling platforms and the Profiler platform itself. This new feature in the Prediction Profiler alerts the user to possible extrapolation or completely avoids drawing extrapolated points where the model may not be valid. Additionally, the user can perform optimization over a constrained region that avoids extrapolation. In this presentation, we discuss the motivation and usefulness of extrapolation control, demonstrate how it can be easily used in JMP, and describe details of our methods.
Session ID: 2021-US-30MP-896

Custom Constraints Solves Mixture DOE Problem at Fuchs Lubricants

Jerry Fish, JMP Systems Engineer, SAS

Dr. Na Liu, Group Leader/R&D Scientist, FUCHS LUBRICANTS CO.

Ryan Bottos, Research and Development Lab Technician, Fuchs Lubricants Co.

Topic: Data Exploration
Level: II

Fuchs Lubricants wanted to plan a Designed Experiment to test mixture components for one of their formulations. One of the mixture components could be obtained from different sources. However, this particular component needed different ranges depending on material source (e.g. if material came from Source A, range was 5%-10% of the mixture. If from Source B, range was 7% to 14% of mixture.) JMP’s Custom DOE platform was employed, along with a special constraint script, to produce an efficient design. We will discuss the constraint script, along with experimental results.
Session ID: 2021-US-30MP-885

Defect Subpopulation Models

David Trindade, Founder and Chief Consultant, STAT-TECH

Topic: Quality and Reliability
Level: II

Suppose a small proportion of a population of components is highly susceptible to a certain failure mechanism. Consider these units to be manufacturing defects in a reliability sense: working originally and not detectable as damaged by standard end-of-the-line tests and inspections, yet after a short period of use, they fail. The rest of the components in the population are either not susceptible to this failure mechanism, or else they fail much later in time for possibly some other cause.
Occasionally, field or life test data indicates this bimodal behavior by showing an early incidence of failures that seem to slow down to almost nothing long before a significant proportion of the population has failed. A good model for this kind of behavior is the defective subpopulation model (DS).
The population of components is really a mixture of two populations: a reliability defective small subpopulation that fails early and the remaining components that may possibly fail later due to wear-out mechanisms that only are seen much further out in time (designated as “immortals”). This model, where a (hopefully small!) proportion of the population follows an early life failure model, has wide applicability.
This talk first describes how such data can be analyzed the Life Distribution platform in JMP through the DS options for fitting the data and then demonstrates how to apply a simple test of the hypothesis that there may be a defective subpopulation based on the output of the Life Distribution platform.
Session ID: 2021-US-30MP-872

Developing a JMP-Based Six Sigma DMAIC Training Curriculum for Data Scientists

Charles Chen, Master Black Belt Q&R, Applied Materials

Mason Chen, Black Belt Student, Stanford OHS

Topic: Quality and Reliability
Level: II

Traditional Six Sigma curricula that include DMAIC, DFSS and Lean were not developed specifically and effectively for today’s AI data scientists. This paper demonstrates an innovative Six Sigma training curriculum for data scientists, using several objectives:

Adopt modern JMP Text Explorer and datamMining techniques for root cause analysis and problem solving.
Integrate various JMP platforms holistically to analyze pattern recognition or discover the insights.
Enhance predictive modeling capability through neural, partition, and principal component analysis.
Utilize modern quality and process platforms such as goal plot model driven multivariate SPC.
Map these modern JMP platforms into the Six Sigma DMAIC/DFSS/Lean framework for Six Sigma Project execution.

This innovative Six Sigma curriculum is applicable to industrial professionals, both for managers and individual contributors who make critical decisions in big data AI business. This curriculum is not just for data scientists; it is also a powerful tool for design and process engineers, quality and reliability engineers, supply chain engineers, business analysts, statisticians, and marketers who want to become reliable decision makers and true project leaders.
Session ID: 2021-US-30MP-816

Employment Application Arrival Model for Talent Acquisition Simulation and Management

Thor Osborn, Principal Systems Research Analyst, Sandia National Laboratories

Topic: Data Visualization
Level: II

Talent acquisition is a critical element of the talent management cycle. Employment application arrival is a stochastic process that poses limitations and delays on the subsequent vetting, decision making, and negotiation steps necessary to hire and onboard talent. Analytical comprehension of this process may be useful for guiding the content of job postings and the expectations of hiring managers, as well as for simulating flows and timing from the issuance of job requisitions to onboarding. This presentation demonstrates that application arrival behavior may be effectively modeled using an ensemble of Gamma-Poisson distributions with mean rate and overdispersion parameter distributions correlated to work site, field of practice, and career stage, using application data from a large research organization collected over several years.
Session ID: 2021-US-30MP-897

Evaluating Meditation Program Using the SEM Platform in JMP

Anantharam Narayanan, Principal Scientist, Procter & Gamble

Annie Weisbrod, Principal Sustainability Scientist, Procter & Gamble

Mark Bailey, Human Resources Expert, Procter & Gamble

Topic: Data Exploration
Level: II

During the Covid-19 crisis, P&G's efforts were guided by three priorities: the safety of P&G people, serving consumers and customers, and supporting our communities. As part of our efforts to ensure ongoing wellness for our employees, we offered live online classes to teach meditation and stress resiliency; this program was developed internally and provided globally. In this paper, we evaluate the impact this new meditation program by using four major dimensions: resiliency, empathy, stress and mindfulness, as well as other subdimensions. Changes in participants’ attitudes and behaviors were surveyed over eight weeks using 45 medical-based survey questions. Using the SEM platform in JMP 16, we measured the scale properties and change over time of these constructs in evaluating the success of our meditation/mindfulness program, specifically in terms of the wellbeing and productivity of our employees. To date, our results are encouraging.
Session ID: 2021-US-45MP-851

Expanding Our Text Mining Toolkit: Sentiment Analysis and Term Selection in JMP Pro 16

Ross Metusalem, JMP Systems Engineer, SAS

Topic: Data Exploration
Level: II

Text mining techniques enable extraction of quantitative information from unstructured text data. JMP Pro 16 has expanded the information it can extract from text, thanks to two additions to Text Explorer’s capabilities: Sentiment Analysis and Term Selection. Sentiment Analysis quantifies the degree of positive or negative emotion expressed in a text by mapping that text’s words to a customizable dictionary of emotion-carrying terms and associated scores (e.g., “wonderful”, +90; “terrible”, -90). Resulting sentiment scores can be used for assessing people’s subjective feelings at scale, exploring subjective feelings in relation to objective concepts, and enriching further analyses. Term Selection automatically finds terms most strongly predictive of a response variable by applying regularized regression capabilities from JMP Pro’s Generalized Regression platform, which is called to from inside the Text Explorer. Term Selection is useful for easily identifying relationships between an important outcome measure and the occurrence of specific terms in associated unstructured texts.

This presentation provides an overview of both Sentiment Analysis and Term Selection techniques, demonstrates their application to real-world data, and shares some best practices for using each effectively.
Session ID: 2021-US-45MP-841

Exploring Interactions in Regression Models with JMP and the Feasible Solutions Algorithm

Joshua Lambert, Assistant Professor, University of Cincinnati

Topic: Data Exploration
Level: I

During the process of building a regression model, scientists are sometimes tasked with assessing the effects of one or more variables of interest. With an additive regression model, effects (e.g., treatment) are assumed to be equal across all possible subgroups (e.g., sex, race, age). Checking all possible interaction effects is either too time consuming or impossible with a desktop computer. A new JMP add-in implements an algorithm, the Feasible Solutions Algorithm (FSA), which is meant to explore subgroup-specific effects in large data sets by identifying two- and three-way interactions to add to multivariable regression models. This talk gives a short introduction to the FSA , explains how I moved my R package to JSL, and provides a tutorial on how to use the FSA JMP add-in to explore interactions in your own regression models.
Session ID: 2021-US-30MP-890

From Coping Strategies to a Piece of Cake: Making Sense of Curves with the Functional Data Explorer

Andrea Coombs, Sr. Systems Engineer, JMP

Topic: Design of Experiments
Level: II

Ten years ago, I gave a JMP Discovery talk entitled, "JSL to the Rescue: Coping Strategies for Making Sense of Real-Time Production Data in Vaccine Manufacturing," which proposed some approaches for creating a custom workflow to make sense of curve data from a manufacturing process. The amount of data, data processing, and modeling was overwhelming, and a considerable amount of scripting was necessary to monitor, characterize, and improve the process. Ten years later, we now have the Functional Data Explorer, which would have made this effort not only much easier, but also more informative in term of process performance and process understanding. In this presentation, I show how making sense of manufacturing data in the Functional Data Explorer is a piece of cake and how the results can bring peace of mind -- no coping strategies necessary!
Session ID: 2021-US-45MP-859

How to Build an Analytics Culture: Building a Champion Within Your Organization

Sarah Springer, Account Executive, Global Premier Team, JMP

Biljana Besker, Account Executive, Global Premier Team, JMP

Topic: Data Exploration
Level: I

Do you want to build an analytics culture within your organization? We discuss how to develop an analytics strategy and champion. There are facets to an analytics culture that require significant change. This change must begin with leadership and champions within the organization who can set the tone and lead from the front. The analytics champion must work to promote data as a strategic asset.

This presentation addresses how champions can facilitate change, overcome resistance to change, promote collaboration, and educate and empower their workforces. They must find additional stakeholders to help execute a unified vision for change within the organization and adopt a plan that can result in an educated workforce to foster a successful analytics program.

As a tool to help upskill your organization’s workforce, this presentation also outlines and highlights unique ways companies can use content from Statistical Thinking for Industrial Problem-Solving (STIPS), an online statistics course available – for free – to anyone interested in building practical skills in using data to solve problems better.
Session ID: 2021-US-45MP-884

In My Defense, I Was Left Unsupervised: Hacking an Aquarium with JMP and the Stuff in my Junk Drawer

Mike Anderson, JMP Senior Systems Engineer, SAS

Topic: Data Access and Manipulation
Level: II

What do a handful of Legos, a webcam, two razor blades, a DVD-R, and some double-stick tape have in common? Well, if you happen to have an interest in aquaculture and a background in chemistry, they have the makings of a decent spectrometer. If you throw JMP into the mix, then you have the makings of a system that can be used to detect the presence of harmful waste byproducts in a fish tank with startling accuracy. Join Mike Anderson on his adventure in setting up and “cycling” a new 36-gallon fish tank using a homemade spectrometer and JMP Pro’s Functional Data Explorer to monitor the process.
Session ID: 2021-US-30MP-867

Industrial Analytics: Condition Monitoring of Wind Turbines and Preventative Maintenance Using JMP

Alan Rezazadeh, Analytics Research Scientist, Southern Alberta Institute of Technology

Topic: Quality and Reliability
Level: III

Condition-based monitoring (CBM) on large sensory data sets requires complex specific capabilities, detecting gradual change over extended time in industrial processes. Application of CBM using SCADA (Supervisory Control and Data Acquisition) is an emerging big data analytics practice to detect the gradual degradation of equipment and find the most effective time to repair equipment, which is also a component of preventative maintenance discipline.

JMP offers a rich set of statistical and machine learning tools that can be used for wind turbine condition-based monitoring. Wind turbines that convert turbulent and uncertain wind energy to highly regulated electricity is a multivariate process with many parameters, subcomponents and interactions that require advanced analytical capabilitiesto detect subtle changes within the operations. This paper uses JMP to build wind turbine bearing temperature predictive models. After comparing predicted against the actuals, residuals are used for condition monitoring. The residuals resulting from the predictive models are used within JMP process control methodologies to quantify wind turbine degradation for decision making and monitoring. It also includes applications of advanced statistical tools such as PCA, clustering, neural networks, and non-normal distributions for data mining and process control within a wind farm.
Session ID: 2021-US-45MP-831

Integrating Ancient with Cutting-Edge Technologies for Carnivore Conservation Across the Americas

Zoe Jewell, President and Co-Founder, WildTrack

Sky Alibhai, Dr, WildTrack

Jody Tucker, Dr, US Forest Service

Juarez Pezutti, Dr, Federal University of Pará | UFPA · Center for Amazonian Studies (NAEA)

Jonah Evans, Texas Parks and Wildlife Department

Topic: Data Visualization
Level: II

Carnivores across the globe are particularly vulnerable to threats from human activity. We humans repurpose their natural habitat, create conflict over resources, hunt and poach them -- and now, even climate change is impacting them. As generally shy and elusive animals, with high mobility, low density, and the need for large home ranges, they are particularly vulnerable to these impacts. More than ever there is a critical need for robust data on the numbers and distribution to help develop and implement conservation strategies.

We share how WildTrack’s unique "from the ground up" approach blends the traditional knowledge of local communities with cutting-edge technology and analytics meshing JMP software and AI.

This talk is co-presented with colleagues working at very different ends of the institutional and geographic spectra. They explain the unique challenges they face in monitoring three iconic carnivore species from the very large to the very small, who range from the north to south and east to west of the Americas: the fisher, jaguar and puma.

We also share how YOU can be involved in this effort and hope you’ll join us!
Session ID: 2021-US-30MP-902

JMP Add-ins and Scripts for Evaluation of Multiple Studies

Stan Young, CEO, CGStat

Paul Fogel, Consultant, Paris

Topic: Data Exploration
Level: II

Researchers produce thousands of studies each year where multiple studies addressing the same or similar questions are evaluated together in a meta analysis. There is a need to understand the reliability of these studies and the underlying studies. Our idea is to look at the reliability of the individual studies, as well as the statistical methods for combining individual study information, usually a risk ratio and its confidence limits. We have now examined about 100 meta-analysis studies, plus complete or random samples of the underlying individual studies. We have developed JMP add-ins and scripts and made them available to facilitate the evaluation of the reliability of the meta-analysis studies, p-value plots (two add-ins), Fisher’s combining of p-values (one script). In short, the meta-analysis studies are not statistically reliable. Using multiple examples, our presentation shares our results that support the observation that well over half of claims made in the literature are unlikely to replicate.
Session ID: 2021-US-30MP-893

Latent Class Analysis of NHANES Respondents to Check Validity of PHQ9 as Depression Severity Measure

Karishma Yadav, Student, Singapore Management University

SEET Fei Fei Sue-ann, Student, Singapore Management University

TAN Yi Ying, Student, Singapore Management University

Tin Seong, Associate Professor, School of Computing & Information Systems, Singapore Management University

Topic: Data Access and Manipulation
Level: I

According to WHO, “Depression is a leading cause of disability worldwide and is a major contributor to the overall global burden of disease.” A major stumbling block in the care of depressed patients remains the accurate diagnosis of the severity of depression. Patient Health Questionnaire (PHQ-9), a nine-question instrument, is widely used for diagnosing and determining the severity of depression. It suggests five categories of depression severity based on the sum of responses to the nine questions.

This study strives to use a data-driven approach to conduct latent class analysis (LCA) to segment approximately 5,000 respondents based on the Mental Health-Depression Screener from the National Health and Nutrition Examination Survey (NHANES) 2017-2018, conducted by the Centers for Disease Control and Prevention in the US. The study further conducts a contingency analysis to compare the observed LCA clusters and the expected PHQ-9 clusters. It found that the effect sizes are different for all nine questions in the five cluster model, suggesting that mere summation of scores of the nine questions in the PHQ-9 may not be the right way to define depression severity levels. From the contingency analysis results, we conclude that PHQ-9 can lead to either upgrading or downgrading the severity of depression.
Session ID: 2021-US-30MP-875

Make Analytics Your Ace in the Hole: Help Leaders Trade Organizational Gambles for Analytics Wins

Rob Lievense, JMP Senior Systems Engineer, SAS

Robyn Godfrey, Account Executive for Federal Civilian Customers, JMP

Ted Berry, Inside Sales Expert, JMP

Topic: Data Exploration
Level: I

Gambling on decisions without a data-driven culture yields mediocrity and the risk of significant reductions in competitiveness. Busy managers may not realize that they are taking risks using “free” open source analytics tools, holding onto routine techniques to solve problems, and limiting odds of success by not using tested and reliable analytical tools.

The four R’s of JMP (reproducibility, robustness, rapidness, and recognition) build competencies that can eliminate the gamble involved in solving problems. JMP is thoroughly tested, includes detailed documentation, and has the ability to easily save and share scripts making analyses very REPRODUCABLE. Answers derived from analyses are ROBUST, justified by accurate estimates of error and straightforward diagnostics, ensuring that even new users are using the most appropriate technique. The point-and-click, dynamic interface enables users of all skill levels to extract RAPID answers from their data. Trading organizational gambles with a data-driven culture ensures that both technical staff and leadership are RECOGNIZED as highly effective.

This presentation is an interactive journey through examples of how using JMP can stack the deck to help an organization win every time!
Session ID: 2021-US-30MP-868

Making an Easy Button for Data Access

Hadley Myers, JMP Sr Systems Engineer, SAS

Peter Hersh, JMP Senior Systems Engineer, SAS

Topic: Data Access and Manipulation
Level: II

Accessing data is often a very time-consuming and aggravating step of the data workflow. It can delay solutions being implemented or allow problems to persist undetected, often with disastrous consequences. Why can’t someone create an “easy button” for data access, allowing you to pick your data source and filter down to what you want? Could it be used to combine data from multiple sources, if needed, and even automate reporting so that issues are flagged immediately rather than at the end of a lengthy and tedious “data munging” exercise? In this talk, we show how to create an interface from JMP’s application builder utilizing SQL, and then build a WebAPI example from scratch. The methods shown could also be equally applied to data stored in other formats. The final application will make data access as easy and fast as pushing a button.
Session ID: 2021-EU-30MP-735

Milking the Data at Dairygold: From Bar Charts to Neural Networks

Kieran O'Mahony, Data Science & Analytics Manager, Dairygold

Topic: Data Visualization
Level: 2
Session ID: 2021-US-45MP-846

Model Screening in JMP Pro 16: Streamlining the Predictive Modeling Workflow

Mia Stephens, JMP Principal Product Manager, SAS

Topic: Statistics and Modeling
Level: II

Predictive modeling is all about finding the model, or combination of models, that most accurately predicts the outcome of interest. But, not all problems (or data) are created equal. For any given scenario, there several possible predictive models you can fit, and no one type of model works best for all problems. In some cases, a regression model might be the top performer, in others it might be a tree-based model or a neural network.

In the search for the best performing model, you might fit all of the available models, one at a time, using cross-validation. Then, you might save the individual models to the data table, or to the Formula Depot, and then use Model Comparison to compare the performance of the models on the validation set to select the best one. Now, with the new Model Screening platform in JMP Pro 16, this workflow has been streamlined. In this talk, you learn how to use Model Screening to simultaneously fit, validate, compare, explore, select and then deploy the best performing predictive model.
Session ID: 2021-US-45MP-914

Modeling Trajectories over Time with Structural Equation Models

Laura Castro-Schilo, JMP Sr. Research Statistician Developer, SAS

Topic: Data Exploration
Level: II

The structural equation modeling (SEM) framework enables analysts to study associations between observed and unobserved (i.e., latent) variables. Many applications of SEM use cross-sectional data. However, this framework provides great flexibility for modeling longitudinal data too. In this presentation, we describe latent growth curve modeling (LGCM) as a flexible tool for characterizing trajectories of growth over time. After a brief review of basic SEM concepts, we show how means are incorporated into the analysis to test theories of growth. We further compare LGCM to random effects models and describe pros and cons of both techniques. We illustrate LGCM by replicating findings from a study (Yuan, 2021) on adolescents practicing mindfulness meditation during the COVID-19 pandemic.
Session ID: 2021-US-30MP-874

Modern Approaches in Classification of Covalent Organic Frameworks by Textural Properties Using JSL

Michael Nazarkovsky, Pontifical Catholic University of Rio de Janeiro, Pontifical Catholic University of Rio de Janeiro

Felipe Lopes Oliveira, Mr., Federal University of Rio de Janeiro

Topic: Data Exploration
Level: II

Covalent organic frameworks (COFs) are an emerging class of crystalline organic nanoporous materials, designed in a bottom-up approach by the covalent bonding of one or more building blocks. Due to the substantial quantity and diversity of building blocks, there is a massive number of different COFs that can be synthesized. In this sense, the development of machine learning techniques that can guide the synthesis of new materials is extremely important. In this work, after a set of 580 structures was selected, DFT and Grand-Canonical Monte Carlo methods were used to determine the enthalpy of adsorption and capture of CO2 or H2 by these structures.

The structures were classified by means of unsupervised (hierarchical clustering, PCA, factor analysis) and supervised (multiple logistic regression, naïve Bayes, KNN, SVM) methods coded in JSL (JMP 15). The COFs were separated in 2D and 3D. The largest clusters were selected to perform supervised machine learning stratifying the data by the structure (62.9% 2D and 37.1% 3D) to avoid disproportion in training/validation ratio (80%/20%). The 2D/3D classification by textural properties was successfully accomplished with the 100% accuracy (validation) for all four models at real estimates of the parameters.
Session ID: 2021-US-30MP-803

One Multi-tabbed, Filterable Dashboard

Kevin Doran, Senior Staff Engineer, Intel Corporation

Topic: Data Exploration
Level: II

Data collection teams within companies amass amazingly large amounts of information. And while these data are very valuable, it is not often tranlated into a format that is usuable by the recipient. Further compounding the problem is that different business units consume the information in different ways, which, unfortunately, often meants that valuable information is never consumed or utilized.

How does one best translate the needed information into an easy-to-use format? Furthermore, can statistically significant information be communicated through one interface? This paper describles the creation of one multi-tabbed, filterable dashboard to communicate computer laptop systems data to both architecture and engineering organizations.
Session ID: 2021-US-45MP-927

Optimizing Multivariate Models: From Data Collection to Optimal Conditions and Scoring Models

Isaac Himanga, Senior Analytics Engineer, ADM

Topic: Data Access and Manipulation
Level: II

Manufacturing processes are highly correlated systems that are difficult to optimize. This correlation provides an opportunity to identify outliers. Here, we demonstrate an entire workflow, which involves:

Obtaining data from the OSISoft historian.
Modeling and optimizing with multivariate models that account for process constraints.
Using the MDMCC platform.
Scoring models online and writing abnormal conditions back to the historian.

In practice, many processes are hard to optimize. Sometimes a system cannot be reduced to a set of independent variables that can be tested; in other cases, the process can become unstable under different conditions. To address these issues, we are using new features in JMP 16 to optimize for cost and quality in the latent variable space, while accounting for constraints on observed variables. In order to remain at those optimal conditions, we use the MDMCC platform to quickly identify deviations from normal.
We developed two custom add-ins to minimize the time spent gathering data and scoring models: one to pull process data into JMP from the popular PI historian, and a second to score models online and send results to the historian, including contributions from the MDMCC.
Session ID: 2021-US-30MP-809

Outlier and Clustering Tools: Uncovering Important Weather Factors in the 2021 Texas Power Crisis

Mason Chen, Mr., Stanford OHS

Topic: Data Exploration
Level: I

The 2021 Texas power crisis was the costliest disaster in the state’s history. This presentation implements JMP outlier and cluster tools to uncover which weather indicators had the greatest effect. Air and dew point temperature were identified as the most crucial factors in the power outages as they directly affected the bursting of pipes. To compare the weather patterns across previous years, cluster sampling was used to collect 12 different variables for each day of February in Houston from 2012 to 2021. Although the quantile range outliers detected that air temperature was the main outlier in 2021, the analysis assumed all weather variables were independent (but humidity depended on both air and dew temperature). The principal component statistical process control chart similarly showed inconclusive results, as the eigenvalues considered the situations spanning all 10 years instead of revealing the main causes specific to this year. Cluster variables helped identify the most representative variables, and dew point (which could not be seen in the outlier analysis) was now observed in the SPC analysis, which was aligned to the scientific research. The heat map and the score plot were further used to assess the difference between the 2021 climate and the past decade.
Session ID: 2021-US-45MP-808

Pictures from the Gallery 6: Select Advanced Graph Builder Views

Scott Wise, Principal Analytical Training Consultant, SAS

Topic: Data Visualization
Level: 1

If a picture is said to be worth a thousand words, then the visuals that can be created in JMP Graph Builder should be considered fine works of art in their ability to convey compelling information to the viewer. This journal presentation features how to build popular and captivating advanced graph views using JMP Graph Builder. Based on the popular Pictures from the Gallery journals, this sixth installment highlights new views available in the latest versions of JMP. It features several popular industry graph formats that you may not have known could be built easily within JMP. Views such as graphlets, run ordered spirals, map cartograms, time series and more are included so that you can breathe life into your graphs and provide a compelling platform to manage your results.
Session ID: 2021-US-30MP-892

Reducing the Fit Variation in Denim Jeans Manufacturing

Lori Rothenberg, Associate Professor, NC State University

Anuja Khairnar, Quality Control Systems Manager, Surry Chemicals Inc

Helmut Hergeth, Associate Professor, NC State University

Topic: Data Exploration
Level: II

VF Jeanswear experienced a decline in sales due to the fit inconsistency in its jeans. Specifically, customers complained of inconsistent waist sizes and irregular inseam length in the same size jeans. The objectives of the research were to identify the sources of variation in the manufacturing process that led to poor fit, design a solution to reduce that variation and implement that solution. The tools used in JMP were Gage R&R and DOE. Several experiments were performed throughout the manufacturing process. Shrinkage variation was the outcome of interest. The factors were fabric type (cotton, bi-blend tri-blend), wash type (rinse, stone wash, hot bleach, cold bleach), washer type (high-speed extraction washer, no extraction washer), dryer type (dryer with humidity sensor mechanism, dryer with algorithm mechanism), type of laundry (product development, industrial), and mill (several mills). The results of the study indicated that by increasing the bi-blends and tri-blends the variation in fit was reduced by 23% in men’s and 12.5% in women’s jeans. To improve the fit further, VF needs to work with the mills closely to help everyone understand the variability in raw materials beforehand.
Session ID: 2021-US-30MP-869

Saving You 15 Minutes: Navigating Your Data Table and Recording Your Actions

Peter Hersh, JMP Senior Systems Engineer, SAS

Mary Loveless, Manager, Pre-Sales Support, SAS

Topic: Data Access and Manipulation
Level: I

Whether you want to sleep more, be more productive at work, leave for happy hour early or get an insurance quote, who doesn’t want an additional 15 minutes? Pete and Mary walk through some of their favorite time-saving tips:

Interactively build a repeatable data workflow with Action Recorder.
Organize and clean up columns all at once.
Identify changes between data tables.

The semi-live session is an interactive session with a panel fielding questions from attendees.
Session ID: 2021-US-45MP-854

Solving an Industrial Problem Using Advanced Statistical Tools

Matheus Sguissardi, Lean Six Sigma Consultant, Plana Consultoria e Treinamentos

Topic: Quality and Reliability
Level: I

The worst problem that a company can have is the customer dissatisfaction. The problem in question happened on the customer's assembly line, when disassembling and assembling the cover of an electric motor connection box. The disassembly and assembly concept was specified for at least 10 cycles, but instead the customer was having problems in the first cycle, when connecting his cables to the purchased electric motor.

To solve the problem, some statistical tools and methods of the Six Sigma methodology were used, in a sequential and structured way, validating and invalidating the main hypotheses of the problem and making all decisions in the process and product in an appropriate way and all based on facts and data analysis.
Session ID: 2021-US-45MP-870

Steal This Code! Three Upgrades for Scripts Obtained from the Enhanced Log

Jordan Hiller, JMP Senior Systems Engineer, SAS

Topic: Data Access and Manipulation
Level: II

JMP 16 made us all coders. With Action Recording in the Enhanced Log, point-and-click data work can now be saved as a replayable script. Learn some intermediate JSL techniques to future-proof your captured code, making it more general, more robust, and easier to use. After a brief review of basic JSL syntax, we discuss case studies illustrating three techniques:

Using loops to perform actions on many columns at once.
Extracting numeric values from a JMP report, for reuse later.
Allowing users to specify data files or columns at run time.
Session ID: 2021-US-30MP-881

Survival Prediction of Patients with Heart Failure from Limited Clinical Laboratory Data Using JMP

Stanley Siranovich, Principal Analyst, Crucial Connection LLC

Topic: Data Exploration
Level: II

Cardiovascular diseases (CVDs) are a leading cause of death, accounting for about 30% of all deaths worldwide. Hence, early detection and treatment, especially for high-risk cases, would be helpful in preventing loss of life. In this session, we examine data set with 299 rows and13 columns, recently published on Kaggle and used in a study published in BMC Medical Informatics and Decision Making, 20, Article number 16 (2020). In particular, we examine a number of predictors such as age, hypertension, smoking, and creatinine, and their ability to predict heart failure. We use several different platforms in the predictor screening and multivariate methods menus. Also, we conduct the analyses as a live demo to demonstrate how visualization, interactivity, and analytical flow can lead to shorter time to discovery when compared to more traditional methods.
Session ID: 2021-US-45MP-847

SVEM: Machine Learning Applied to DOE

Peter Hersh, JMP Senior Systems Engineer, SAS

Trent Lemkus, Doctoral Student, University of New Hampshire

Phil Kay, Learning Manager, JMP

Topic: Design of Experiments
Level: II

We design experiments to understand our processes and systems: to know what input factors we need to control and how we need to control them to consistently achieve our desired outcomes. Statistical design of experiments (DOE) is the most effective way to build models that yield this understanding. With DOE we can maximize our understanding from our experimental effort.

But to use these methods, we often need to accept certain assumptions (including effect sparsity, effect heredity, and active/inactive effects) that don’t always sit comfortably with our knowledge and experience of the domain. Can machine learning approaches overcome some of these challenges and provide a richer understanding from experiments without extra cost? Could this be a revolution in the design and analysis of experiments?

This talk explores the innovative method of self-validated ensemble modeling (SVEM). With animated visuals, you can see how fractional weighting of small data sets enables self-validation in model fitting for designed experiments. You can also see the results of rigorous simulation studies that demonstrate the predictive robustness of ensemble models built using this validation method. And case study examples are presented to demonstrate how scientists and engineers can get improved understanding from their experiments without extra effort using SVEM.
Session ID: 2021-US-45MP-826

That Can’t Be My Weight! Is My Scale Broken? An Exploration of Measurement Systems Analysis (MSA)

Jason Wiggins, JMP Senior Systems Engineer, SAS

Topic: Design of Experiments
Level: II

From our homes to the lab to the production floor, measurements systems are everywhere. At home, there is the dreaded bathroom scale. In test labs, measurement systems are used to measure important physical properties like material strength. Production systems rely on measurements of quality characteristics such as the shape of a machined part. The measurement systems we interact with have a variety of behaviors. They may be noisy or precise, accurate or biased. They may or may not alter or destroy our samples. Regardless of behavior, we need to know enough about the noise in our measurement systems to make informed choices about how they are used to avoid costly errors. In the early '90s the Hubble telescope’s main mirror was overground due to an errant measurement. The mistake, discovered post launch, nearly sidelined the project. Understanding and addressing the source of variation through a MSA pre-launch could have saved NASA the $1.5 billion trip to space to repair it. This presentation covers the ins and outs of applying MSA results from the Evaluating the Measurement Process (EMP) method pioneered by Dr. Donald Wheeler. An overview of EMP, along with examples from home and industry for nondestructive and destructive measurement systems, is explored. EMP gauge classification, reporting precision and guard banding specification limits to ensure product conformance are demonstrated in JMP.
Session ID: 2021-US-30MP-879

The New JMP 16 Limits of Detection in Design of Experiments and Data Analysis

Laura Higgins, JMP Global Technical Enablement Engineer, SAS JMP

Topic: Design of Experiments
Level: I

JMP 16 has new features in its DOE platform that incorporate the limits of detection into Column Properties and are automatically applied during modeling with JMP Pro’s Generalized Regression platform. I demonstrate these new features and discuss the impact of limits of detection on data analysis.

When we look for very small amounts of a substance, we are limited by the technology we are using. A substance could be present but undetectable is the lower limit of detection and has a huge practical importance for detecting low levels of a substance in critical situations, such as identifying the start of an infection or correctly identifying small amounts of impurities. Ignoring the limits of detection in data analysis has serious impacts; basic statistics such as means and confidence intervals become biased and misleading. Modeling data with incorrect assumptions about the limits of detection lead to erroneous models. Limits of detection have implications in such industrial applications as pharmaceutical and chemical manufacturing, analytical chemistry, and diagnostic test design.
Session ID: 2021-US-45MP-855

Toward Predicting the Fate of Reef Corals: 2021 Update

Anderson Mayfield, Assistant Scientist, University of Miami

Topic: Data Exploration
Level: II

Coral reefs around the globe are threatened by a plethora of anthropogenic stressors, most notably climate change. There has consequently been an urgent push to better understand the fundamental biology of key coral reef inhabitants, understand how these organisms will respond to future changes in their environments, and make data-driven predictions as to the relative resilience of disparate coral populations. In this talk, l showcase a complex, "molecules-to-ocean basins" data set featuring molecular (e.g., proteins), physiological (e.g., growth), environmental (e.g., coral abundance), and oceanographic (e.g., temperature) data to showcase how I have been using JMP Pro 15 and (more recently) JMP Pro 16 to model coral behavior and make predictions as to which colonies/reefs may be at most risk. I also examine which reefs are expected to demonstrate resilience to global climate change and disease. Through this update to my Discovery Summit Americas 2020 presentation, I hope to, more generally, show progress in forecasting coral health and resilience using the ever-growing data sets and analytical power now at our disposal.
Session ID: 2021-US-30MP-844

Utilizing Extrapolation Control Feature in JMP® Pro 16 to Optimize Real-World Mining Process

Xinjie Tong, Senior Research Statistician, Dow

Topic: Design of Experiments
Level: I

The case study aims to assess 12 types of frothers in terms of their performance in mineral recovery in mining. In addition to frother type, the input variables also include particle size (coded value: 0, 0.35, 1) as a continuous variable and water type (Type A, Type B, Type C) as a categorical variable. The response variable is mineral recovery efficiency.

Using extrapolation control feature in the prediction profiler, a possible extrapolation was observed for Water Type B when particle size = 1 based on regularized T square metric. This extrapolation was caused by an imbalance in the DOE where the measurement for Water Type B was only taken with particle size = 0.35. The extrapolation control was turned on in the effort of optimizing mineral recovery efficiency to achieve reliable optimal solutions. As an alternative, extrapolation control was also enforced in optimization by incorporating the equation of leverages in the desirability functions. The two approaches were compared and discussed.

The extrapolation issue disappeared when the DOE was augmented to be more balanced. The extrapolation control feature provided immediate understanding on how augmenting an imbalanced DOE could largely enhance the usability of the predictive model.
Session ID: 2021-US-30MP-842

Virtual Classrooms Are Here to Stay. How Do You Conduct a Practical DOE Exercise?

Ledi Trutna, JMP Education, SAS

Diana Ballard, Statistician, Brewer Science

Topic: Design of Experiments
Level: I

The challenge of teaching remote DOE classes is in the live exercise. Students put the pieces of DOE together by actually designing an experiment together, gathering data, analyzing it, generating models and running confirmation runs. But how do we, as instructors, manage these exercises when we are so physically far apart from our students? After presenting a practical DOE conducted with students in a remote setting, we offer suggestions on how you could manage and run similar experiments for any size of group.
Session ID: 2021-US-30MP-845

Which Forecasts Should I Use? An Add-in for Comparing ARIMA and State Space Smoothing Models

Jacob Rhyne, Sr Analytics Software Tester, JMP

Topic: JSL Application Development
Level: I

The time series platform in JMP enables users to explore time series data and to forecast future observations. Two popular model classes, ARIMA models and State Space Smoothing models, are both available in JMP 16. JMP users may want to fit a set of ARIMA models, as well as a set of State Space Smoothing models, and choose one model for forecasting. However, choosing between an ARIMA and ETS model can be a difficult task. While models of the same class can be compared to each other, ARIMA and State Space Smoothing models cannot be directly compared using a model selection criterion such as AIC.An add-in to help users choose between candidate ARIMA and State Space Smoothing models will be presented. The add-in enables JMP users to compare model classes using time series cross-validation or to evaluate candidate models using a test set. After giving an overview of ARIMA and State Space Smoothing models in JMP, this presentation demonatrates how the add-in can be used to recommend which model to use for forecasting.

Session ID: 2021-US-EPO-930

AgQHub, a JMP Add-in for Breeding Pipeline Data Analysis and Reporting

Thomas Walk, Large Plant Breeding Pipeline DB Manger, North Dakota State University

Ana Heilman Morales, Large Plant Breeding Pipeline DB Manger, North Dakota State University

Didier Murillo, Data Analyst, North Dakota State University

Richard Horsley, Head of the Department of Plant Sciences, North Dakota State University

Topic: Data Visualization
Level: II

Crop breeders, often managing numerous experiments involving thousands of experimental breeding lines grown at multiple locations over many years, have developed valuable data management and analysis tools. Here, we report on more efficient crop evaluation with a suite of tools integrated into the JMP add-in dubbed AgQHub. This add-in provides an interface for users to first query MS SQL Server databases, and then calculate best linear unbiased predictors (BLUPs) of crop performance through the mixed model features of JMP. Then, to further assist in selection processes, users can sort and filter data within the add-in, with filtered data available for building reports in an interactive dashboard. Within the dashboard, users segregate selected crop genotypes into test and check categories. Separate variety release tables are automatically generated for each test line in head-to-head comparisons with selected check varieties. The dashboard also provides users the option to produce figures for quickly comparing results across tested lines and multiple traits. The tables and figures produced in the dashboard can be output to files that users can readily incorporate into variety release documentation. In short, AgQHub is a one-stop add-in that crop breeders can use to query databases, calculate BLUPs, and generate report tables and figures.
Session ID: 2021-US-EPO-871

Analyzing and Improving an MLB Pitcher's Decision Making and Execution with Machine Learning

Ryan Cooper, Graduate Student, Georgia Institute of Technology; and Senior Engineer, Samsung Austin Semiconductor

Topic: Data Access and Manipulation
Level: II

Pitchers in Major League Baseball rely on a combination of strategy, deception, skill, and execution to be successful. Their sole job is to get an out for each batter they face.

To do so, they analyze the situation for each pitch and ask themselves such questions as "How many balls and strikes are there? Are any runners on base? What’s the score?” They also consider which pitch to throw where and how fast to throw it.

Based on the answers to these questions, the pitcher will carefully decide which approach he thinks has the best chance of sending each batter back to the dugout. The release point of the ball, spin rate, and breaking amount all contribute towards physically executing each pitch to the best of his ability.

Most pitchers are likely aware of their biggest strengths, but do they have any hidden strengths that aren’t being used to their full potential? How does a pitcher’s actual success and potential success stack up with others?

These questions are answered using JMP 16 Pro’s new enhanced log and model screening features, JMP’s R functions to access Bill Petti’s “baseballr” package from Baseball Savant at MLB.com, and more.
Session ID: 2021-US-EPO-888

Analyzing COVID-19 Vaccines Tweets using JMP Pro 15

Tuan Le, Student, Oklahoma State University

Topic: Data Visualization
Level: I

The COVID-19 pandemic is the most severe worldwide public health crisis in our generation, with more than 2 million associated deaths as of January 2021 (Dong E, Du H, Gardener). Posing a great challenge to our modern medicine, this pandemic was met with the rapid deployment of several types of vaccines all over the world. Since the vaccines are new and were deployed quickly in a fragmented information environment, we are interested in finding out how they are perceived among world populations. Using a sample of more than 60,000 tweets and JMP Pro 16, this paper analyzes and extracts insights about people’s perception regarding COVID-19’s vaccine. We found that while Moderna and Pfizer are the two most talked about vaccines, the Russian Sputnik has become very popular outside of the US. A large portion of the tweets carry hashtags that convey support for the vaccines. According to the tweets, some people have displayed concerns about side effects, but in general the sentiment on the vaccines is positive.
Session ID: 2021-US-EPO-866

Design of Experiments to Characterize and Predict Polymer Acoustic Properties

Valerie Rennoll, Johns Hopkins University

Ian McLane, Graduate student, Johns Hopkins University

Drew Grant, Graduate student, Johns Hopkins University

Mounya Elhilali, Professor, Johns Hopkins University

James West, Professor, Johns Hopkins University

Topic: Design of Experiments
Level: 1

Acoustic impedance and attenuation are important properties that impact sound propagation in underwater and medical applications. To target specific acoustic properties, multiple polymers with a range of densities and stiffnesses can be used and then further tuned with the addition of dopants. Using an I-optimal design of experiments in JMP, the relationship between multiple fabrication and characterization conditions on the acoustic impedance and attenuation of polymers with nanoparticle dopants was explored. A total of 98 samples was fabricated and characterized with factors of polymer type (PDMS, ecoflex, and polyurethane), sample thickness, dopant density, dopant concentration, dopant size, and characterization frequency and temperature. The obtained statistical models specify the conditions necessary to match the polymers to acoustic impedances in the range of 1 to 2.2 MRayls, while minimizing or maximizing attenuation. Nine validation samples were fabricated to target five impedance values with minimum or maximum attenuations and demonstrated an average predicted error of 1.4% compared to the measured impedance values. The model is used to construct sensors that are acoustically matched to materials such as human skin, wood, and water.
Session ID: 2021-US-EPO-931

DOE Application for Multiple Responses Aiming NVh Reduction in Electric Motors for Appliance Market

Danilo Toniato, Engineer, WEG

Andre Caron Zanezi, Engineer, WEG

Topic: Design of Experiments
Level: II

Faced with a vast and competitive field, appliance manufacturers are constantly seeking for quality improvement, aiming to meet the needs of an increasingly demanding market. A reflection of this requirement can be observed in the progressively restricted specifications to the sound power level (NWS) emitted by the product. To meet and exceed customers' expectations, this work addresses the use of design of experiments (DOE) to analyze multiple electromechanical characteristics and its respective relation to motor noise across the frequency spectrum, specifically in washing machines. Using Graph Builder and DOE in JMP to better fit models to perform these analyses, it was possible to define the most significant factors for reducing noise without impacting motor manufacture costs.
Session ID: 2021-US-EPO-929

JMP Can Be Shiny Too

Caroll Co, Research Scientist, Social & Scientific Systems

Helen Cunny, Toxicologist, Division of the National Toxicology Program, NIEHS

Keith Shockley, Staff Scientist, Biostatistics & Computational Biology, NIEHS

David Umbach, Staff Scientist, Biostatistics & Computational Biology, NIEHS

Topic: Data Exploration
Level: I

The dashboard functionality in JMP is a fantastic tool that can serve as a good alternative to R Shiny. Compared to R Shiny, JMP dashboards are easier and faster to build and can save lots of time in app development. In this poster, I talk about how JMP dashboards can be a tool for:

         Sharing the excitement of data discovery with others.
         Personalizing the presentation of graphics.
         Creating a visual storyboard to convey information
Session ID: 2021-US-EPO-813

Netflix or AMC: Predicting Release Strategies in the Age of Options

Lavada Blanton, Business Analytics Student, Oklahoma State University

Topic: Data Exploration
Level: II

As the challenges of COVID-19 made more businesses switch to virtual resources, the film industry was no exception. While billions are staying indoors and taking socially distanced precautions, production companies must decide the risks involved in the traditional movie release in theaters. Alternatives such as Netflix, Amazon Prime Video, and HBO Max were once frowned upon by critics but now have become respected players in the film industry. We now know that some movies released during COVID-19 were less than critically favorable but recent hits such as Godzilla vs Kong and Soul have given a glimmer of hope to decision makers. This project analyzed 1,605 movies, released through either the traditional movie theater format or through a streaming service since 2010. We look at key indicators in box office success such as IMDB score, critic reviews and audience reviews to evaluate the best “Movie Mix” to be distributed either in theaters or on a streaming service. In order to compare distribution type, box office success in streaming movies is predicted based on a theatrical release model. Then box office revenue is compared between streaming and theatrical to profile movies based on categories such as Genre and Release Month. Finally, a decision tree is used to streamline recommendations. These recommendations can be used by production companies in formulating the optimum release strategy and resource allocation.
Session ID: 2021-US-EPO-894

Optimization of Subsample Size for Sphenophorus levis on Sugarcane Experiments

Paulo Peloia, Data Analytics Lead LATAM, Syngenta Crop Protection

Topic: Design of Experiments
Level: II

Field experiments with products against Sphenophorus levis on sugarcane in Brazil evaluate the percentage of stalks attacked in four subsamples of 0.5 row-meter per plot. Nevertheless, about 40% of the experiments did not detect a significant difference between treatments (p-value < .05). The increase in the number of subsamples (n) initially leads to a lower experimental error up to an optimum point (n*), from which the gain in detecting differences between treatments is very small. Thus, two blank experiments with 10 treatments, four blocks and eight subsamples/plot were conducted. To estimate the median experimental error, 100 bootstrap samples for each value of n, from 8 to 1, were performed. To determine n*, the quadratic plateau model (QPM) was fitted by least squares Gauss-Newton method for nonlinear models using JMP 15.2. In the following season, five real experiments were conducted with n* subsamples and their capacity to detect significant differences was compared as if they had been conducted with n = 4, using 100 bootstrap samples for each experiment. The QPM estimated n* = 6 subsamples (R² = .97). Experiments with n = 6 detected a significant difference between treatments in 63% of the cases and with n = 4, 41%.
Session ID: 2021-US-EPO-922

Power Delivery Network Model Prediction and Correlation

Zoe Toigo, Signal and Power Integrity Engineer, Microsoft

Priya Pathmanathan, Senior Signal and Power Integrity Engineer, Microsoft

Martin Rodriguez, Power Integrity Engineer, Microsoft

Doug White, Princial Signal and Power Integrity Engineering Manager, Microsoft

Topic: Design of Experiments
Level: II

Ever-increasing complexity of computer systems demands electrical power delivered efficiently to the chip. The design challenge of a power delivery network (PDN) is to provide stable, low-noise voltage through low-impedance paths, which influence overall system performance. Accurate models of a proposed PDN are necessary for initial system architecture decisions and continue to drive layout requirements as the physical design matures.

One portion of the PDN design process involves creating a model of the chip’s package in a 3D electromagnetic field-solver tool (HFSS). Complex S-parameter models from FEM (Finite Element Method) field solvers are often simplified to circuit element approximations. Previously, input parameters to a two-dimensional circuit approximation of the package were manually fitted until the circuit matched the 3D model. However, custom DOE and response surface fitting in JMP reduced the number of experimental simulations and development time for model creation and correlation. The prediction profile revealed the polynomial relationship between 12 factors and six responses. Desirability functions were utilized to determine the values of the factors required to obtain the desired responses. Using this data, predicted responses were correlated to circuit simulations.
Session ID: 2021-US-EPO-909

Statistical DOE and Modeling for Repeated Measures in Rubber Research

Wenzhao Yang, Statistician, Dow Chemical Company

Topic: Design of Experiments
Level: II

EPDM is a synthetic rubber widely used in such applications as transportation, infrastructure, sports, leisure, and appliance. Dow, as a leading manufacturer of EPDM, continuously innovates in the development of EPDM products and applications to achieve superior properties, including color aging.

In this Dow case study, the color-aging properties of different EPDM rubbers were repeatedly measured over time (repeated measures). The objective of this study is to develop fundamental understanding of EPDM weatherstrip discoloration mechanism and validate hypotheses on influence factors including EPDM microstructure, formulation and process factors. Efficient DOE strategy and proper statistical models are developed for causal inference. We analyzed the data using three methods: linear regression, covariance structure and random coefficient regression. Linear regression completely pools the data by assuming a common variance for all samples across time. Covariance structure specifies a certain correlation pattern in the repeated measures. Random coefficient regression incorporates the sample-specific effects and provides more inference in variability between samples over time. We identified significant EPDM structure effects for color-aging properties by comparing different methods. In this poster, we demonstrate the power of DOE and statistical modeling for research and fundamental study.
Session ID: 2021-US-EPO-925

Time-Series Data Analysis of Flight Delays at US Airports, January-June 2020

Linli Zhong, Student,

Ruiyun Yan, Student, Singapore Management University

Aishwarya Krishna Prasad, Student, Singapore Management University

Topic: Data Exploration
Level: I

There are several reasons for a flight to be delayed, such as air system issues, weather, airline delays, security issues, and so on. But interestingly, the most frequent reason for a flight delay is not about weather but about air system issues. The Federal Aviation Administration (FAA) usually considers a flight to be delayed when it is 15 minutes or more late in arriving or departing than its scheduled time. Flight delays are inconvenient for both airlines and customers. This paper employs dynamic time warping (DTW) techniques for 54 airports in the US. The study aims to cluster airports with similar delay patterns over time. In addition, the paper builds some explanatory models to explain the similarity between different airports or distances. In this analysis, we aim to use the time-series techniques to discover the similarity in the top 15% busiest American airports. This paper first filters the top 15% busiest American airports and calculates the departure delay rate for each airport and then uses DTW to cluster these airports based on departure similarities. Next, the similarities and differences between clusters are identified. This analysis will help inform passengers and airport officials about departure delays at 54 American airports from January to June 2020.
Session ID: 2021-US-EPO-852

Use of the Interaction Profiler to Assess Medical Therapies in the Management of SARS from COVID-19

Bobby Nossaman, Senior Physician & Associate Professor, Ochsner Clinical School–University of Queensland Medical Center, Ochsner Health

Topic: Data Visualization
Level: II

Most COVID-19 patients with comorbidities are prone to developing a pneumonic process, leading to severe acute respiratory syndrome (SARS) requiring positive pressure pulmonary ventilation. We examined the roles of medical therapies during the management of SARS during the initial weeks of the COVID-19 epidemic at medical centers in New Orleans.

Following IRB approval, all 211 adult patients from March 8- April 8, 2020, with a diagnosis for COVID-19 infection and who required invasive positive pressure pulmonary ventilation from SARS were entered into this study. Measures of effect size were expressed as percentages with 95% confidence intervals (CI). Logistic regression developed interaction profiles to visualize the roles of medical therapies on hospital outcomes.

The incidence of hospital mortality was 64.4% (CI 57.8-70.6%). Logistic regression allowed development of Interaction Profiles to study the association of the five therapies provided to this group to hospital mortality. The use of the Interaction Profiler clarified the magnitude of hospital mortality associated with these five therapies and provides a model for future evaluation of therapeutic interventions in patients with SARS from COVID-19.

Prediction Profilers help us understand therapeutic interactions and could assist in future therapies.
Session ID: 2021-US-EPO-858

Using Definitive Screening Design (DSD) to Guide Lubricants Formulation Development

Na Liu, Group Leader, FUCHS Lubricants Co.

Adam C. Kyrouac, R&D Technician II, FUCHS LUBRICANTS CO.

Topic: Design of Experiments
Level: I

Definitive screening design (DSD) allows studies of a large number of factors in a relatively small experiment. It is appropriate for early-stage experimentation work to identify main factors for further evaluation. Lubricants formulation may be composed of numerous functional additives, which have interactions among each other. It is important to use a DOE approach to understand and validate assumptions on the additive interactions and impacts on product performance. The goal of this study is to identify the main factors impacting film density and lubricity. The narrowed-down factor list will be used for mixture design to optimize formulation performance.

We started the design with eight additives as factors and three properties as responses. First, we prepared 21 formulations and collected experimentation data on viscosity, film density, and lubricity. Next, we fit the response surface using DSD fitting, generalized regression, and stepwise methods. The models are compared on R-squared and AICc. Main factors, two-way interactions, and quadratic effects have been identified and will be used for future mixture design.
Session ID: 2021-US-EPO-860

Using JMP for Analytic Problem Solving: A Real-World Case-Based Learning Experience

Anita Lee-Post, Professor, University of Kentucky

Ram Pakath, Professor, University of Kentucky

Topic: Data Access and Manipulation
Level: I

We created a real-world application use case to provide students with a case-based learning experience using JMP for analytic problem solving. The use case is concerned with acquiring, cleansing, and analyzing COVID vaccination-related data to help students generate analytics-based insights about COVID vaccine acceptance in the US and to better appreciate the value that analytics-based sense-making, insight-generation, and decision-making offers. Specifically, students perform cluster analysis, association rule analysis, and text mining in conjunction with one another. Results from cluster analysis can help answer questions such as whether there are geographical differences in attitudes towards vaccination and which locations need to increase efforts to overcome vaccine hesitation. Results from association rule analysis help reveal what level of attitude toward vaccination associates most strongly with vaccine uptake across locations and whether there are temporal differences in the associations. Results from text mining help identify words that are frequently used to describe the vaccination experience and help distinguish between positive and negative perceptions of the experience. Empirical evidence from student learning outcomes assessment lends credence to the effectiveness of using real-world use cases in analytic problem-solving pedagogy with JMP.
Session ID: 2021-US-EPO-843

Using JMP Prediction Profiler to Assess Optimal SNP-Assay Allelic Discrimination in Corn Samples

Madisun Hembree, Engineer II, Product Development, Thermo Fisher Scientific

Topic: Data Exploration
Level: II

Since the field of in vitro diagnostics (IVD) is ever-evolving, new technologies in the field require a process by which to measure its benefit. Optimization requires output variables to be defined and their hypothesized key factor variables to be tested. Experiments can be run under conditions where the defined factor variables lead to the most desirable conditions of the output variables. Single nucleotide polymorphism (SNP) genotyping assays, one example of an IVD, detect and distinguish SNP variations in a sample. Optimization for these assays requires the discrimination between allele types to be maximized. Before new formulations or process changes, it is important to establish baseline allelic discrimination patterns with all control materials.

We can use JMP’s Fit Model, Prediction Profiler, and Simulator to create a toolset to quantify the desirability of different conditions for multiple key factor variables. This study aims to create a toolset that considers the key factor variables of corn kernel type and SNP-assay type to characterize the effect on three output variables: call rate, cluster spread, and cluster to NTC distance. This toolset can then be added to validate new formulations or process changes in the future.
Session ID: 2021-US-EPO-848

Variation in Knee Pain Treatment for Active Duty Soldiers: Military Versus Civilian Hospitals

Ken Kovats, Senior Nurse Analyst, US Army Medical Command

Charles (Dennis) Sissel, Decision Science Analyst, US Army Medical Command

Ruby Sharp, Decision Science Analyst, US Army Medical Command

Ernesto Negron, Decision Science Analyst, US Army Medical Command

Topic: Data Exploration
Level: I

Is there a difference in the rate of surgical intervention (arthroscopy with meniscectomy) for active duty soldiers with diagnosis of meniscal tear between the direct care military treatment facilities and civilian purchased care (network) providers?

The Department of Defense’s (DoD) Military Health System (MHS) ensures America’s military personnel are healthy and medically ready to complete their national security missions. Health services are provided to DoD beneficiaries via two means: the direct care system, consisting of DoD-operated hospitals and clinics at military installations around the world, and the purchased care system, where beneficiaries are referred to civilian providers, practices, and institutions, which then bill the DoD for services provided.

This study looks at the rates of surgical intervention of Army active duty soldiers with a diagnosis of meniscal tear over a three-year window to determine if the rate of surgical intervention differs between soldiers receiving care at military treatment facilities versus those referred to the civilian network system. Longitudinal data is extracted from MHS Systems via SAS. Text Explorer in JMP is used to identify referrals, diagnostic procedures, and whether surgical intervention occurred. The Contingency Table feature in JMP is also used to determine if there are differences in the rates of surgical interventions between the direct and purchased care patients.

Abstracts

Delicate Brute Force

Facets of a Diverse Career

Ronald Andrews, Sole Proprietor, Practical Stats

Rich Newman, Statistician, Intel

Don Kent, Data Analytics and Machine Learning Manager, Intel

Bradley Jones, JMP Distinguished Research Fellow, SAS

Rob Reul, Managing Director, Isometric Solutions

Suling Lee, SMU, Singapore Management University

Amanjot Kaur, Statistician, Perrigo

Rob Lievense, JMP Senior Systems Engineer, SAS

Philip Ramsey, Data Scientist and Professor, North Haven Group and University of New Hampshire

Marie Gaudard, Professor, Predictum

Wayne Levin, Owner, Predictum

Lim Yong Kai, Student, Singapore Management University

Chen Li Student, Singapore Management University

Stanley Siranovich, Principal Analyst, Crucial Connection LLC

Ryan Parker, JMP Senior Research Statistician Developer, SAS

Bill Worley, JMP Senior Global Enablement Engineer, SAS

Jeremy Ash, JMP Analytics Software Tester, SAS

Michael Akerman, Senior Data Scientist, Novozymes

Johan Pelck Olsen, Data Scientist, Novozymes

Steven Crist, Analytics Consultant, Wells Fargo

Andrew Karl, Senior Management Consultant, Adsurgo LLC

Heath Rushing, Principal Consultant, Adsurgo LLC

Bradford Foulkes, GE Renewables, GE

Brian Taylor, Statistician, AstraZeneca

Chris Gotwalt, Chief Data Scientist, JMP

Laura Lancaster, JMP Principal Research Statistician Developer, SAS

Jeremy Ash, JMP Analytics Software Tester, SAS

Christopher Gotwalt, JMP Director of Statistical R&D, SAS

Jerry Fish, JMP Systems Engineer, SAS

Dr. Na Liu, Group Leader/R&D Scientist, FUCHS LUBRICANTS CO.

Ryan Bottos, Research and Development Lab Technician, Fuchs Lubricants Co.

David Trindade, Founder and Chief Consultant, STAT-TECH

Charles Chen, Master Black Belt Q&R, Applied Materials

Mason Chen, Black Belt Student, Stanford OHS

Thor Osborn, Principal Systems Research Analyst, Sandia National Laboratories

Anantharam Narayanan, Principal Scientist, Procter & Gamble

Annie Weisbrod, Principal Sustainability Scientist, Procter & Gamble

Mark Bailey, Human Resources Expert, Procter & Gamble

Ross Metusalem, JMP Systems Engineer, SAS

Joshua Lambert, Assistant Professor, University of Cincinnati

Andrea Coombs, Sr. Systems Engineer, JMP

Sarah Springer, Account Executive, Global Premier Team, JMP

Biljana Besker, Account Executive, Global Premier Team, JMP

Mike Anderson, JMP Senior Systems Engineer, SAS

Alan Rezazadeh, Analytics Research Scientist, Southern Alberta Institute of Technology

Zoe Jewell, President and Co-Founder, WildTrack

Sky Alibhai, Dr, WildTrack

Jody Tucker, Dr, US Forest Service

Juarez Pezutti, Dr, Federal University of Pará | UFPA · Center for Amazonian Studies (NAEA)

Jonah Evans, Texas Parks and Wildlife Department

Stan Young, CEO, CGStat

Paul Fogel, Consultant, Paris

Karishma Yadav, Student, Singapore Management University

SEET Fei Fei Sue-ann, Student, Singapore Management University

TAN Yi Ying, Student, Singapore Management University

Tin Seong, Associate Professor, School of Computing & Information Systems, Singapore Management University

Rob Lievense, JMP Senior Systems Engineer, SAS

Robyn Godfrey, Account Executive for Federal Civilian Customers, JMP

Ted Berry, Inside Sales Expert, JMP

Hadley Myers, JMP Sr Systems Engineer, SAS

Peter Hersh, JMP Senior Systems Engineer, SAS

Kieran O'Mahony, Data Science & Analytics Manager, Dairygold

Mia Stephens, JMP Principal Product Manager, SAS

Laura Castro-Schilo, JMP Sr. Research Statistician Developer, SAS

Michael Nazarkovsky, Pontifical Catholic University of Rio de Janeiro, Pontifical Catholic University of Rio de Janeiro

Felipe Lopes Oliveira, Mr., Federal University of Rio de Janeiro

Kevin Doran, Senior Staff Engineer, Intel Corporation

Isaac Himanga, Senior Analytics Engineer, ADM

Mason Chen, Mr., Stanford OHS

Scott Wise, Principal Analytical Training Consultant, SAS

Lori Rothenberg, Associate Professor, NC State University

Anuja Khairnar, Quality Control Systems Manager, Surry Chemicals Inc

Helmut Hergeth, Associate Professor, NC State University

Peter Hersh, JMP Senior Systems Engineer, SAS

Mary Loveless, Manager, Pre-Sales Support, SAS

Matheus Sguissardi, Lean Six Sigma Consultant, Plana Consultoria e Treinamentos

Jordan Hiller, JMP Senior Systems Engineer, SAS