Abstracts

Angling, Wrangling, and Untangling Data

John Sall, Founder and Chairman, JMP
Julian Parris, Director, Research & Development, JMP
Andrea Coombs, Senior Systems Engineer, JMP
Michael Hecht, Principal Systems Developer, JMP
Michael Goff, Sr Software Developer, JMP

Preparing data for analysis is said to be 90% of the work in data analytics...and it is especially important to have good tools to make this process efficient. Luckily, JMP includes a suite of features to assist with removing complexities from your data and transforming it to fit your problem, allowing you to focus on innovation, experimentation, and discovery.

Best of all, these tools are enhanced with each release. John Sall will be joined by Julian Parris, Andrea Coombs, Michael Hecht, and Michael Goff (just some of the skilled software developers and statisticians at JMP) as they explore ways to angle, wrangle, and untangle YOUR data using JMP.

Building a Culture of Data Literacy: The Power of Analytics Advocacy

Levannia Lildhar, Data Scientist, Siemens Healthineers
Michael Akerman, Lead Data Science Engineer, Novozymes
Julia O’Neill, Founder & Principal Consultant and former Distinguished Fellow, Moderna Therapeutics

As practitioners of applied statistics – regardless of industry, background or skill level – we all know the power data holds as a strategic asset. Despite overwhelming consensus around the promise of statistics, however, many organizations are struggling to go beyond buzzwords like “AI” and “digital transformation” to actually build a culture of data literacy at an enterprise scale. In this session, we’ll hear from analytics advocates from organizations at all stages of analytic maturity. Whether it is a lack of investment in training or leadership that fails to see the value of statistical enablement, we can learn from peers who have been there before – and found ways to overcome some of the organizational barriers standing in the way of data transformation.

Suzanne Simard Author Photo 1000x650.jpg

Finding the Mother Tree: Designing Experiments in Nature and the Importance of Biodiversity

Suzanne Simard, Professor of Forest Ecology, University of British Columbia

In this talk, Suzanne Simard brings us into the intimate world of trees, exploring the ways in which trees learn and adapt their behaviors, remember the past, demonstrate agency over the future, and cooperate with a sophistication typically ascribed to humans.

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

Session ID: 2023-US-30MP-1458

A Class of Saturated Mixed-Level Main Effects Designs for Even Numbers of Runs

Ryan Lekivetz, Senior Advanced Analytics Manager, JMP

Topic: Design of Experiments
Level: III

Screening experiments often require both continuous and categorical factors. Engineers generally prefer continuous factors with three levels over those with two levels because of concerns about curvature in the relationship between a continuous factor and the response.

This presentation provides a method for constructing designs for roughly equal numbers of continuous factors with three levels and categorical factors with two levels. If there are m three level continuous factors and m-1 two-level categorical factors, our designs have 2m runs. Note that m may be odd or even. As for any saturated main effects plan, our designs depend on an assumption of effect sparsity and negligible two-factor interactions (2FIs) to provide accurate estimates of the main effects. With substantial effect sparsity or by not making use of all the columns of the design, it can be possible to identify a large quadratic effect or 2FI. The columns of the two-level factors are orthogonal to the columns of three-level factors. This fact can be used to provide a design-centric analytical approach. When the number of runs is a multiple of eight, all the design columns are mutually orthogonal. This talk provides examples of the designs and shows how to create them.
Session ID: 2023-US-30MP-1511

Beautiful Operationalization

James Wisnowski, Principal Consultant, Adsurgo

Andrew Karl, Senior Consultant Adsurgo

Topic: Sharing and Communicating Results
Level: II

JMP has long been well-suited for producing production quality tables and graphs. Users have many options such as taking screen shots or saving JMP reports in specific formats or even using JSL to place pieces of output reports in specific MS Word or Excel files. We have had a strong demand in recent customer engagements to go beyond providing solid analytical solutions accompanied by the excellent JMP tabular and graphical output as the teams look to operationalize a solution. Operationalization often means deploying results to nonstatistical audiences using templates or special formats tailored more toward general consumption. We demonstrate a method integrating streamlined Python code generated with an AI chatbot application to effectively create output documents with advanced MS Word features not possible with JSL alone and not requiring difficult Visual Basic code. We also show several other tips and tricks not requiring Python.
Session ID: 2023-US-30MP-1456

Choosing Models in JMP with Model Selection Criteria

Christopher Gotwalt, Chief Data Scientist, JMP

Topic: Predictive Modeling and Machine Learning
Level: II

More than any statistical software, JMP and JMP Pro make tremendous use of model selection criteria, such as the AICc and BIC. These tools can be used by practitioners in all industries and at all skill levels, from users choosing a distribution for a capability analysis, to advanced users choosing input factors in a linear mixed model or a functional DOE analysis. Model selection criteria are incredibly flexible and powerful, yet make it easy to decide between very different sets of predictor variables, response distributions, and even correlation structures, all at the same time.

Unfortunately, the full story of how and when to use these criteria are not part of most standard data science courses in universities and professional training. One reason for this omission is that, unlike JMP, many software packages implement model selection criteria in an incomplete or arguably incorrect way, making it impossible to compare models with different input variables.

In this presentation, we give clear guidance on how and when to use model selection criteria. We describe their motivation and the assumptions they require. We compare model selection criteria to other better-known approaches to selecting models, such as hypothesis tests and holdout-based crossvalidation procedures. We also give a brief story of how JMP Statistical R&D developers came to appreciate how useful these tools are, as we sought a general solution to the distribution dredging problem.
Session ID: 2023-US-30MP-1499

CMC, SVEM, Neural Networks, DOE, and Complexity: It’s All About Prediction

Philip Ramsey, Consultant and Professor, Predictum and University of New Hampshire
Patricia D. McNeill, Associate Director of Cell Culture Development Lundbeck Seattle BioPharmaceuticals, Inc.

Topic: Predictive Modeling and Machine Learning
Level: II

Scientists in biopharma working along the CMC development pathway are challenged by the increasing complexity of biologic-based therapeutics and processes that produce them. Many critical responses exist (often 10-20) that are themselves functions of numerous, highly interactive input process factors.

We use a large case study to show that current experimental design strategies combined with current response surface methods are generally inadequate to deal with the complex kinetic behaviors observed to be ever-changing across the design space. The case study consists of a 7-factor, hybrid experimental design used to develop a bioprocess with 13 critical responses. Employing a combination of SVEM, neural network models, and the hybrid experimental design, we show that accurate predictive models can be estimated for all responses that interpolate satisfactorily throughout the design space.

Furthermore, we show that the powerful tools in JMP and the Prediction Profiler, specifically the Design Space Profiler, are essential to visualizing and understanding the experimental design space and optimizing the bioprocess. We also discuss the need for new optimal design strategies. JMP Pro 17 is used throughout the talk.
Session ID: 2023-US-30MP-1464

Converting Binary Responses to Continuous in Product Development Using DOE

Curtis Park, Principal Scientist, HP Hood

Topic: Design of Experiments
Level: II

At HP Hood, the use of design of experiments (DOE) has helped to successfully identify formulations in new product development, delighting consumers of our food products.

Throughout our R&D department's wide implementation of DOE, there have been a number of successful DOE models, as well as some unsuccessful ones. One major stumbling block to model building with DOE has been dealing with subjective binary responses such as acceptable/unacceptable. These binary responses provide less information than continuous responses, thus inhibiting the ability to extract meaningful results from designed experiments.

This presentation shares simple and practical strategies for using the JMP DOE platform to convert these binary responses to continuous ones, resulting in improved models and powerful insights. Real-world examples from consumer food products are given to demonstrate how DOE can be used for more than just building models. It can be used to overcome the problem of responses that are difficult to measure.
Session ID: 2023-US-30MP-1432

Design of Experiment’s Crucial Step 0: Choosing the Right DOE Option

Christine Anderson-Cook, Statistician, Los Alamos

Topic: Design of Experiments
Level: I

JMP has a wealth of design of experiments (DOE) options from which to choose. While this array is incredibly powerful, it also has the potential to be a bit intimidating to those who are new to this area. What category of design should I choose from the many possibilities? How do I know what the best one is for my experimental objectives? This talk provides some ideas for how to strategically tackle Step 0 of the process of constructing the right design by considering the following questions:

What are the goals of the experiment?
What do we already know about the factors, responses, and their relationship?
What are the constraints under which we need to operate?

Once these questions are answered, we can match our priorities with one of the many excellent choices available in the JMP DOE platform.
Session ID: 2023-US-30MP-1465

Digitalizing the Data Dictionary and Other Report Contextual Information

Sarah Kalicin, Founder, Achieve More With Data

Topic: Automation and Scripting
Level: II

Have you gone back to your analysis, only to realize you've forgotten how you defined a metric or the general context of your data? Have you had stakeholders ask for this information or be confused on how to interpret a metric?

If so, why not consider digitalizing this information so that it's easy to convey among report periods or across common reports? This method helps provide report consistency, making it easier for stakeholders to gain the information they need when interpreting your analysis. It also saves you time, because this information is now in a centralized place, automating it as part of the analysis.

JMP data tables can be used to build data dictionaries and store other report facts that can be easily populated in JMP reports. This presentation shows how you can build this capability, whether you are a basic JMP user or a seasoned JSL scripter. Make metric definitions part of your JMP analytic workflow, rather than an afterthought!
Session ID: 2023-US-30MP-1410

Do You Trust Me? Assessing Reliability for Driver-Assisted Autonomous Vehicles

Caleb King, Research Statistician Developer, JMP

Topic: Reliability Analysis
Level: II

Autonomous vehicles, or self-driving cars, no longer only live in science fiction. Engineers and scientists are making them a reality. However, their reliability concerns, or more importantly, safety concerns, have been crucial to their commercial success. Can we trust autonomous vehicles? Do we have the information to make this decision?

In this talk, we investigate the reliability of autonomous vehicles (AVs) produced by four leading manufacturers by analyzing the publicly available data that have been submitted to the California DMV AV testing program. We assess the quality of the data, evaluate the amount of information contained in the data, analyze the data in various ways, and eventually attempt to draw some conclusions from what we have learned in the process. We show how we utilized various tools in JMP in this study, including processing the raw data, establishing assumptions and limitations of the data, fitting different reliability models, and finally, selecting appropriate models to draw conclusions. The limitations of the data include both quality and quantity. As such, our results might be far from conclusive, but we can still gain important insights with proper statistical methodologies.
Session ID: 2023-US-30MP-1447

Finding Optimal Operating Regions for Critical Quality Attributes with the Design Space Profiler

Laura Lancaster, Principal Research Statistician Developer, JMP

Topic: Quality and Process Engineering
Level: II

Quality by Design (QbD) is a systematic approach for building quality into a product. The Design Space Profiler, new within the Prediction Profiler in JMP 17, helps solve the fundamental QbD problem of determining an optimal operating region that assures quality as defined by specifications associated with critical quality attributes (CQAs) while still maintaining flexibility in production.

We explain JMP’s approach to solving this difficult problem and demonstrate how to use the Design Space Profiler to find these optimal subregions of the design space. The Prediction Profiler in JMP has long been a powerful tool for visualizing and optimizing models; having the Design Space Profiler within the Prediction Profiler makes it an indispensable tool for high-quality product and process innovation.
Session ID: 2023-US-30MP-1419

Friends Don’t Let Friends Misuse Levey-Jennings Charts

Di Michelson, Principal Analytic Training Consultant, JMP
Jordan Hiller, Principal Systems Engineer, JMP
Byron Wingerd, Principal Systems Engineer, JMP

Topic: Quality and Process Engineering
Level: I

Control charts, like other statistical tools, can yield misleading results when they are improperly applied. We call attention to a troubling practice observed in the field: the inappropriate use of the Levey-Jennings chart (which uses the sample standard deviation statistic s to estimate the process sigma) beyond its intended purpose. This misuse guarantees that important process signals will be obscured. In this presentation, we review the history of the Levey-Jennings chart, describe how and why it is being misused, and provide suggestions for alternatives.
Session ID: 2023-US-30MP-1450

Gauge R&R of X-ray Photoelectron Spectroscopy to Monitor a Coating Process

Sukti Chatterjee, Sr. Member of Technical Staff, Applied Materials Inc.
Shrikant Swaminathan, Mechanical Engineer Manager, Applied Materials Inc.
Miaojun Wang, Process Engineer, Applied Materials Inc.
Fei Wang, Director, Applied Materials Inc.
Lance Scudder, Director, Applied Materials Inc
Pravin K Narwankar, Managing Director, Applied Materials Inc.

Topic: Sharing and Communicating Results
Level: I

X-ray photoelectron spectroscopy (XPS) analyzes the surface chemistry of materials. It is also known as electron spectroscopy for chemical analysis (ESCA) and is commonly used to measure elemental composition/stoichiometry of thin film coatings in different industries. We have applied this technique to develop Atomic Layer Deposition (ALD) AlOx coating (of < 50 nm thick) processes by testing deposited film composition to identify the O/Al ratio.

Our pharmaceutical program customers questioned whether XPS is an appropriate metrology technique to detect process variation in the coating composition. This presentation demonstrates the adequacy of XPS by using Gauge R&R in JMP 17. We designed our testing experiments using measurement systems analysis (MSA) designs platform, and a fast replicate crossed model was used with six sample coupons on two different kinds of substrates (silicon coupons and active pharmaceutical ingredient [API] pellets). Each sample was split into four parts and all 24 (6 X 4) samples were measured independently by two different vendors blindly.

The data distribution was reviewed using a variety of methods: X-bar and R control chart, performed repeatability, reproducibility, part-to-part variation testing, calculated Gauge R&R (P/TV), P/PV, P/T in MSA Gauge R&R platform. Also, the evaluating the measurement process (EMP) platform was used to determine interclass correlation (ICC) and to identify if any interaction exists with either substrate type or vendor. Both MSA platforms confirmed that part variation is significantly higher than precision level, hence XPS is adequate to detect the variation in the process.
Session ID: 2023-US-30MP-1376

Graph Builder and Tabulate Applications in Clinical Trials

Rebecca Lyzinski, Senior Software Developer, JMP

Topic: Data Exploration and Visualization
Level: I

Flexible visualizations allow for easy exploration of clinical trial data. JMP Clinical uses many different options in Graph Builder and Tabulate to allow for dynamic views and publication-ready output. From stacked bar charts to display adverse events by severity to line graphs to show change from baseline over time to swimmer plots graphing disease response in oncology studies, JMP Clinical uses many different Graph Builder elements. Paired with most graphs is Tabulate, which displays the statistics reflected in the graph. New Tabulate features in JMP 17 such as stack, pack, and unique ID, help make these tables ready for publication. This pairing of Graph Builder and Tabulate gives users a quick way to visualize the data through a graph and then dig further into the numbers.
Session ID: 2023-US-30MP-1462

Growth Curve Modeling to Measure Impact of Temperature and Usage Amount on Detergent Performance

Anantharam Narayanan, Adjunct Assistant Professor, University of Cincinnati
Zhiwu Liang, Principal Statistician, Procter & Gamble

Topic: Advanced Statistical Modeling
Level: II

In this presentation, we use the longitudinal analysis capability of the Structural Equation Modeling (SEM) platform in JMP to measure the impact of temperature and usage amount on detergent performance from the consumer perspective using a measure of overall rating of product.

In this diary study measured over time, data were collected from consumers who used one of two products in three phases of four-week time interval. Using data collected from Week 5 to Week 9, we modeled the trajectories of performance of the detergent in relation to the temperature and usage (number of pods). Using standard SEM fit measures, we found the linear growth curve model fit the data well for the underlying latent structures. Results showed that one of the test products performed significantly better under cold wash temperatures and used fewer pods than the other product. This result was a significant win for the company under the sustainability platform and performance under colder wash conditions.
Session ID: 2023-US-30MP-1369

Harnessing the Power to Look Within: How We Used JMP Live to Understand the Health of JMP Use

Isabel Price, Sr. Statistician, Regeneron
Katie Domermuth, Principal Statistician, Regeneron

Topic: Sharing and Communicating Results
Level: I

JMP is a statistical software that can be harnessed to provide superpowers to enable effective and efficient organization, analysis, and reporting of data to save the world with data-driven decisions and process understanding. As Super Statisticians, we aim to grow the use of these superpowers in all areas of Regeneron.

However, with great statistics comes great responsibility, and it was time we used JMP to unleash our superpowers and look within. Is Regeneron using JMP effectively to channel its data superpowers? Are we providing the right support to overcome making anecdotal decisions? Come along with us on our quest to identify metrics to gauge the health of not only how the business is using JMP to flex its powers, but how our team is supporting the development of superpowers in the business. We demonstrate how to use JMP to connect to data sources, analyze data effectively, share metrics using JMP Live, and leverage the automation capability of JSL to update metrics faster than the speed of light.
Session ID: 2023-US-30MP-1491

If I Only Had Time to Work with Timestamp Data…I Would Learn So Much About My Process!

Wendy Tseng, Sr Systems Engineer, JMP

Topic: Data Exploration and Visualization
Level: I

Working with timestamp data (dates, times, or datetimes) can be like wrestling a pig. It can be struggle for many reasons, including knowing the appropriate modeling type to use, how to process data with non-standard datetime formats, and how to easily perform datetime calculations.

With sensors becoming more prevalent in measurement systems and manufacturing equipment, learning how to work with datetime data is becoming increasingly more important if you want to use this data to understand relationships between process variables and critical quality endpoints.

You do not have to get dirty or give up using this data altogether if you learn the ABCs of how this type of data is stored in JMP and how to process it. JMP makes working with timestamp data easier. In this presentation, I focus on the tools that I have seen customers benefit from the most when they are working with datetime data.
Session ID: 2023-US-30MP-1408

JMP Pro Neural Network Tuning Add-In

Scott Allen, Sr. Systems Engineer, JMP

Topic: Automation and Scripting
Level: II

JMP Pro’s Neural Network platform provides a powerful and flexible tool for generating predictive models for many types of data. Users optimize the models by adjusting various parameters and then compare individual models within the platform. This process is carried out manually, however, which takes a significant amount of user interaction and may not necessarily uncover an optimal model.

This presentation demonstrates the use of a JMP Pro add-in designed to screen neural network tuning parameters. The add-in creates a fast, flexible space-filling design based on user input, then runs a neural network model for each set of parameters. A graphical report helps users identify optimal models, so they can then run the selected model(s) or continue tuning the parameters to build their understanding of the system. Finally, the add-in allows users to save the results from the tuning optimization and return to it later, which is helpful when dealing with omplex neural network models that might take considerable computing time.

Various JMP customers and Community members have inquired about a neural network tuning capability in JMP; this feedback was used to guide the development of this add-in. Prior to Discovery Summit, the add-in will be posted to the Community with a blog post containing documentation notes and guidance on using the add-in.
Session ID: 2023-US-30MP-1441

Leveraging JMP Analytics to Drive Innovation in High-Throughput SynBio Analytical Sciences

Stefan Moser, Associate Director, R&D Data Analytics, Amyris Inc.
Scott Wise, Principal Systems and Statistical Engineer, JMP

Topic: Basic Data Analysis and Modeling
Level: I

This presentation demonstrates the use of JMP for a practical real-world application in high-throughput analytical chemistry; it also highlights the power of even basic JMP functionality to reduce costs without affecting data quality. This talk provides a compelling introduction to the synthetic biology industry, which is at the cutting edge of scientific discovery, aiming to revolutionize how to manufacture chemicals sustainably.

At Amyris, hundreds of thousands to millions of different strains are created each year. Each strain is screened using various analytical platforms to identify improvements, allowing us to link genetic coding to specific phenotypes. This scale pushes the boundaries of today’s automation and analytical technologies, consequently challenging the scalability of conventional approaches to analytical chemistry.

This talk highlights not only the use of JMP as a powerful tool to make a complex analysis easy but also demonstrates the use of the workflow builder to enable general users to perform these analyses. A business case is shown in which JMP helped analyze and improve analytic calibration protocols in the lab.
Session ID: 2023-US-30MP-1480

News Flash: Gauges Aren’t Perfect! OK, You Know That. But How Much Is It Costing Your Business?

Jerry Fish, Systems Engineer, JMP
Jason Wiggins, Sr. Systems Engineer, JMP
Brady Brady, Principal Systems Engineer, JMP

Topic: Quality and Process Engineering
Level: II

Most engineers and scientists know the value of a good measurement systems analysis (MSA). After all, our data decisions depend on having gauges that give good readings, right? But no gauge is perfect. No matter how hard we try, there are always errors associated with a gauge. That’s why MSAs are so important. They help us quantify the errors that the gauges make.

But once you have finished your MSA, what do you do next? Depending on the results, you might conclude that the gauge is “poor” and throw it away. Or you might conclude that the gauge is “good” and use it without really understanding the errors that it will still make.

What if this imperfect gauge is used in production? How often will it make Type 1 vs Type 2 errors? What do those errors cost your company, in terms of money and in terms of diminished reputation (if bad parts are shipped)? Would it be wise to invest time and/or money in improving your gauge to mitigate these errors?

These questions (and more) are answered with a new JMP add-in. To demonstrate the add-in, we present a case study that shows how to determine the percentage of good parts that are potentially being rejected by your gauge, and the number of bad parts that are being passed on to your customers. We also discuss strategies to mitigate these errors.
Session ID: 2023-US-30MP-1457

Novel Coatings Development: The Importance of Including Auxiliary Responses

David Fenn, Senior Scientist, PPG Industries, Inc.
Timothy Marsh, Research Associate, PPG Industries, Inc.

Topic: Design of Experiments
Level: II

The development of innovative new products can be accelerated using statistically optimized DOE and regression modeling. With the goal of maximizing efficiency and reducing expense, it is often tempting to limit the collected data to key product attributes, such as customer specifications or internal quality metrics. However, increasing the number of available responses by including a wider range of more fundamental measurements in the analysis can often be critical to success.

This talk covers examples from projects at PPG’s Coatings Innovation Center. We highlight the use of JMP from design through analysis to visualization of the results for a fractional factorial and a constrained mixture/process design. Using tools such as column switcher, multivariate plots, pairwise correlation and mixture profiler, we demonstrate how the inclusion of ancillary responses helped to develop a deeper understanding of the systems being studied and pinpoint the causes behind unexpected results.
Session ID: 2023-US-30MP-1386

Pictures from the Gallery 8: Innovative Graph Builder Views

Scott Wise, Sr. Systems Engineer, JMP

Topic: Data Exploration and Visualization
Level: I

A picture is said to be worth a thousand words, and the visuals that can be created in JMP Graph Builder can 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 graph views using JMP Graph Builder. Based on the popular Pictures from the Gallery journals, the Gallery 8 presentation highlights new views and tricks available in the latest versions of JMP. We feature several popular industry graph formats that you may not have known could be easily built within JMP. Views such as integrated tabular graphs, satellite mapping, formula-based graphs and more are also included, helping you breathe new life into your graphs and reports!
Session ID: 2023-US-30MP-1516

Plasma Enhanced Chemical Vapor Deposition Thin Film Development using DOE

William Entley, Senior R&D Manager, EMD Electronics
Jed Campbell, Sr. Systems Engineer, JMP
Jennifer Achtyl, Lead R&D Scientist, EMD Electronics
Robert Ridgeway, Senior R&D Manager, EMD Electronics

Topic: Design of Experiments
Level: II

Plasma enhanced chemical vapor deposition (PECVD) is extensively used to deposit thin films in semiconductor manufacturing. PECVD processes are gas phase processes typically conducted at low pressures in specially designed deposition chambers. Many process parameters influence the thin film properties in ways that are difficult to characterize.

This presentation shows:

How the Generalized Regression platform was used to "rescue" a definitive screening design that had runs that couldn’t be completed.
How the original design was augmented.
How this complex design space was finally understood well enough to find a new precursor, effectively identifying a thin film that maximized the targeted film property (high mechanical strength) in the fewest number of experiments.

This presentation is predominantly hands-on, using JMP in real time.
Session ID: 2023-US-30MP-1520

Predicting Functional Responses using JMP Functional DOE and Curve DOE Modeling

Fangyi Luo, Group Scientist, Procter & Gamble
Christopher Gotwalt, Director, JMP

Topic: Advanced Statistical Modeling
Level: III

Functional or curved responses frequently occur in industry. Thanks to new features in JMP, we can now model and predict functional responses using key DOE or product design factors with JMP Functional DOE or Curve DOE modeling. A Functional DOE model is purely empirical. However, a Curve DOE model can incorporate mechanistic or expert knowledge on the functional form of the curved responses. In this presentation, the methods and results of predicting functional responses using Functional DOE and Curve DOE modeling are compared using case studies from the consumer product industry.
Session ID: 2023-US-30MP-1507

Projection of Cleaning Solvent Needs by Use of Slider Box

Margaret Wilson-Moses, Senior Product Engineer, Inpria Corporation

Topic: Sharing and Communicating Results
Level: II

Cleaning large systems effectively is critical to many applications within industrial manufacturing. Establishment of a system's hold-up volume and the target concentration of residual contaminant after cleaning do not fully define a cleaning method, however. Assuming a cleaning method that repeats the same flush volume until the system has reached its target contaminant level, viable cleaning schemes will differ in how many flushes of the volume used to reach that target. By creating a graph showing "Flush volume" as a function of "Number of flushes needed" with editable table variables of "System hold-up volume" and "End contaminant level" in slider boxes, the user can visualize many different scenarios in many different systems before selecting the scheme that best fits their varied needs.
Session ID: 2023-US-30MP-1483

A Sample Size: More Than a Number

Karen Copeland, Statistician, Boulder Statistics

Topic: Design of Experiments
Level: I

While the question "How many (parts/subjects/runs) do I need?" is one nearly every statistician dreads, it is an important question and should be asked prior to running any study or experiment. The answer seems simple enough. Just plug some numbers into a calculator and off you go! In my experience, though, sample size calculations are rarely that easy.

JMP 16 introduced an entire suite of Sample Size Explorers, with more added in JMP 17. But why call them "explorers" and not "calculators"? Because sample size is more than a calculation. It is an integral part of a study design, and to determine a sample size, more than math is needed. This presentation explores sample size from the concept to the execution. While the examples include sample size explorations for medical device or diagnostics studies, the lessons learned are applicable across industries.
Session ID: 2023-US-30MP-1472

Spectris: Spooky Spectroscopy

Mike Anderson, Sr. Systems Engineer JMP

Topic: Data Exploration and Visualization
Level: I

There is a no-man's-land in JMP, a place where it is scary to venture. It is a place inhabited by specters of data sets too complex for simple nonlinear regression, but too ephemeral for functional data analysis. It is a strange place. It appears basic methods are enough to venture here but using those techniques quickly becomes unmanageable. These spectra can come from many sources, but all have a common problem: there is too much data for simple nonlinear regression or too litttle to use functional methods. Join me on a journey across this challenging landscape of classical spectroscopy to learn methods for extracting information from examples of complex spectra and how to automate the process.
Session ID: 2023-US-30MP-1488

Streamlining Statistical Analysis with Custom JSL Scripts

Prince Shiva Chaudhary, Data Scientist, Intel

Topic: Quality and Process Engineering
Level: II

At Intel, the use of JMP and JSL has become integral to the organization’s data-driven decision-making infrastructure. To improve data exploration, visualization, process engineering, and quality control capabilities (and to standardize statistical analysis) our team created the "Stats” package. The Stats package comprises multiple customized JSL scripts that support statistical standards, and the output generated from these scripts provides standardized, agreed-upon reports. Since its creation, the Stats package has been used globally by thousands of individuals across our engineering organizations, saving countless hours in standard data analysis and reporting.

In this presentation, the Stats package development team shares a small portion of the package, specifically relating to scripts that leverage native JMP quality and process engineering platforms to create fully integrated offline reports. The presentation showcases the design process of JSL scripts, starting with the desired statistical standard and continuing through the development of user-friendly script execution windows. We illustrate the extensive data blending, statistical analysis, and data visualizations that constitute the final output reports.

The team shares insights into the benefits of using custom JSL scripts for streamlining statistical analysis, as well as the challenges and best practices in developing these scripts. The presentation also demonstrates the effectiveness of the Stats package in improving the efficiency and accuracy of statistical analysis across our engineering teams.
Session ID: 2023-US-30MP-1473

The Unique Challenges of Cell Therapies: JMP Scripts for Power and Sample Size Calculations

Heath Rushing, Principal, Adsurgo
Andrew Karl, Senior Consultant, Adsurgo
Jeff Hofer, Principal, JDH CMC Stats, LLC
Rick Burdick, Principal, Burdick Statistical Consulting, LLC

Topic: Quality and Process Engineering
Level: II

The most novel, innovative, and promising therapeutics in biopharmaceuticals are cell therapies. Cell therapies transfer human cells into a patient to treat disease. These cells either come directly from the patient or from a healthy (cell) donor. Multiple regulatory guidance documents recognize the importance of providing cell therapy manufacturers the flexibility to improve their processes. Therefore, it is imperative to show that the pre- and post-change processes are comparable and process changes pose no threat to the safety or efficacy of the drug product.

One method used to ensure comparability is an equivalence test of means. There is a regulatory expectation that the study is done as a paired design, often referred to as a split-apheresis study, unless there is minimal donor-to-donor variability. In split-apheresis studies, the same donor material is split and processed in the pre- and post-change process for comparison. The design of this study presents unique challenges in cell therapies as they require adequate sample sizes to ensure properly powered designs, yet the number of healthy donors available is usually quite low (three to six donors). Additionally, the power depends on lot-to-lot and assay variability, assay replication strategy, and the effect size used for the equivalence acceptance criterion (EAC).

This talk presents a series of JMP scripts that extend the existing capabilities of the Sample Size Explorer platform to address each of these relevant study nuances, as well as the capability to overlay power curves to address trade-offs with different sample sizes and approaches.
Session ID: 2023-US-30MP-1362

Time-Efficient Strategy for Selecting a Test Set in the Validation of an Image Detection Algorithm

Caroll Co, Statistician, Social & Scientific Systems Inc.
Sandra McBride, Principal Statistician, Social & Scientific Systems Inc., A DLH Holdings Corp. Company
Shawn Harris, Statistician, Social & Scientific Systems Inc., A DLH Holdings Corp. Company
Debra A Tokarz, Senior Pathologist, Experimental Pathology Laboratories, Inc.
Thomas J Steinbach, Vice President/Senior Pathologist, Experimental Pathology Laboratories, Inc.
Mark F Cesta, Pathologist, National Institute of Environmental Health Sciences
Helen Cunny, Project Officer, National Institute of Environmental Health Sciences
Keith R Shockley, Staff Scientist, National Institute of Environmental Health Sciences

Topic: Design of Experiments
Level: II

Advances in digital image analysis have created opportunities for quantitative histopathology assessments in rodent toxicology studies. Microscopic evaluation of rodent spleen is performed to assess for test article-induced immunotoxic effects but can be subject to inter- and intra-pathologist variability in characterization of differences between treatment groups and across studies.

To address this problem, an image detection algorithm was trained to quantify tissue compartments in histologic sections of rodent spleen. Our aim was to design a study to evaluate how the image detection algorithm compared to digital annotations performed by human raters for specific features of the spleen, while keeping within operational constraints (e.g., rater time and effort).

In this talk, we show how we used JMP Custom Designer, using data generated by the image algorithm as inputs, to select and allocate a test set across human raters. We used a response surface model, which is designed to select samples that fall on the boundaries and center of the input space. The resulting study design allowed us to strategically select a test set and create a balanced sampling plan for use across several pathologists from different institutions.
Session ID: 2023-US-30MP-1461

Utilizing JMP Data Analytics in the Product Development of Adhesives and Sealants

Stone Cheng, Technical Director, Henkel

Topic: Data Exploration
Level: I

Effective data analytics is critical for the success of product development. Product development processes for adhesives and sealants involve data analytics for tasks needed at various project phases. Too many formulation chemists still depend on Excel for daily data recording, formulating, and analysis. While some chemists use JMP for statistical analysis and experimental design, JMP’s powerful and versatile data analytics should be used more universally for many daily tasks in product development. When shown how widely JMP can be used, more formulation chemists should see the benefits of adopting JMP as a daily tool.

This presentation shares examples of how easily JMP can be used in daily data analytics, in addition to the statistical analysis and DOE that most chemists use. A variety of examples are given, including handling data from literature and patent searches, analysis of raw materials, and a variety of formulation-related tasks such as formula stoichiometry calculation, mixing volume balance, data recording, and in tabulate/graph analysis. In addition, this presentation demonstrates how JMP is used for product selection, application trouble shooting, failure mode analysis, DOE, and more.
Session ID: 2023-US-30MP-1401

Why Aren't You Using App Builder Already?

Don McCormack, Principal Systems Engineer, JMP

Topic: Automation and Scripting
Level: II

App Builder was introduced in JMP 10. In the beginning, it had its bumps and warts and was, arguably, a bit of a challenge to use. As a long-time JSL scripter, I, too, was reticent to use App Builder and preferred to code everything from scratch, even dialog boxes. But I got tired of the work needed to make small visual tweaks to my dialogs and custom report windows, so I gave App Builder another look -- and have not looked back. If it's worth an interface, I'm using App Builder.

In this session I talk about some of the basics of creating applications with one or more interactive elements. I discuss key elements that make App Builder great, along with some lesser-known features. Topics include building multiple module applications, scoping, managing windows, working with the App Builder interface, how JSL is different with App Builder, and working with other JMP elements such as Data Tables, Add-In Builder and Workflows.

Session ID: 2023-US-PO-1510

Alcaraz vs Nadal: Whose Numbers Are Better at 19? Tennis Analysis and Visualization Using JMP

Jianfeng Ding, Principal Research Statistician Developer, JMP

Topic: Data Exploration and Visualization
Level: II

Rafael Nadal has been the Spanish king of tennis on the international circuit for over 15 years, but it looks like his successor is in line to take the throne. Many have rightly pointed out that Carlos Alcaraz is his natural successor, and no wonder. Since 2003, no Spaniard other than Nadal has managed to win a Grand Slam.

It's no surprise that people have started to draw comparisons between the two players. Although Alcaraz has made a hugely impressive start to his tennis career, is it possible to predict if he'll reach the insane heights that Nadal did? Using the powerful analytical and visualization tools in JMP and data from tennisabstract.com, we compare the stats of 19-year-old Nadal to Alcaraz. We hope the comparison gives some insight into Alcaraz’s trajectory, and whether he has a shot at catching his compatriot.
Session ID: 2023-US-PO-1446

CMP Two-Polish Table Optimization for Max Tool Output

Tom Jarman, Process Engineer, Texas Instruments

Topic: Quality Process and Engineering
Level: II

Chemical mechanical planarization (CMP) is a process used in the semiconductor industry in which a layer of the chip is planarized or flattened in preparation for putting another layer on top of it. The chemical part consists of chemical reactions occuring between the liquid polishing slurry and the top layer of the substance being polished, which is often softened in the process. The mechanical part occurs after the chemical softening, removing the top layer and exposing the next atomic layer for chemical reaction. The polishing is crucial to ensure that the subsequent layer doesn't have electrical opens or shorts due to its topography on the preceding layer. Many CMP processes consist of bulk polish on one table and then a final polish on a second table. This two-table process helps achieve the maximum possible output from a CMP tool if the two tables are properly balanced for bulk and final polish times.

I gathered and analyzed data in JMP to show the polish time vs. polish pad life relationship for Tables I and II for a CMP process at one of Texas Instruments' fabrication facilities. I then used JSL built-in integration function to calculate area under the polish time vs. pad life curve for Tables I and II. Finally, I used a system of equations to solve for a Table I polish time starting point that created equal polish time vs. polish pad life integrated areas for Tables I and II, ensuring a maximized tool output for this CMP process.
Session ID: 2023-US-PO-1486

JMP for Troubleshooting and Engineering Change Assessment in Point-of-Use Gas Abatement Equipment

Christopher Jackson, Applications Engineer, Centrotherm, Clean Tech Inc

Topic: Data Exploration and Visualization
Level: I

Troubleshooting faults in industrial equipment can be a daunting task, particularly when compounded by the time constraints of a high-volume production line or plant. Analysis is further complicated in equipment with multiple operating modes, where issues may be visible in one mode but masked in another due to differences in operating parameters. The data generated by SCADA (Supervisory Control and Data Acquisition) or networked IoT equipment and sensors in a modern plant can provide a rich source of information for both fault troubleshooting and monitoring change assessment. However, the sheer volume of data for even a single piece of equipment requires complex analysis to be made intelligible for the engineer or technician.

This presentation elucidates a selection of examples using JMP for exploration and visualization of abatement equipment operating parameters for fault identification and engineering change assessment. Basic data visualization was used to identify physical phenomena happening within the abatement system to assess the root cause for fault troubleshooting. More advanced analyses, including clustering and variability charts, were used to identify issues related to specific operating modes and to assess the impact of engineering changes.
Session ID: 2023-US-PO-1486

Laney P' and U' Control Charts New in JMP 17

Annie Dudley, Senior Research Statistician, JMP

Topic: Data Exploration and Visualization
Level: I

Troubleshooting faults in industrial equipment can be a daunting task, particularly when compounded by the time constraints of a high-volume production line or plant. Analysis is further complicated in equipment with multiple operating modes, where issues may be visible in one mode but masked in another due to differences in operating parameters. The data generated by SCADA (Supervisory Control and Data Acquisition) or networked IoT equipment and sensors in a modern plant can provide a rich source of information for both fault troubleshooting and monitoring change assessment. However, the sheer volume of data for even a single piece of equipment requires complex analysis to be made intelligible for the engineer or technician.

This presentation elucidates a selection of examples using JMP for exploration and visualization of abatement equipment operating parameters for fault identification and engineering change assessment. Basic data visualization was used to identify physical phenomena happening within the abatement system to assess the root cause for fault troubleshooting. More advanced analyses, including clustering and variability charts, were used to identify issues related to specific operating modes and to assess the impact of engineering changes.
Session ID: 2023-US-PO-1504

Measurement Systems Analysis for Curve Data Using Functional Mixed Models

Colleen McKendry, Senior Statistical Writer, JMP

Topic: Quality Process and Engineering
Level: III

This presentation is an extension of the talk, "Measurement Systems Analysis for Curve Data Using Functional Random Effects Models," presented at JMP Discovery Europe 2023. Here, a functional random effects model was used to perform a Functional Gauge R&R analysis on data that contained a set of curves as the response. In this application, the functional model was expanded using the eigenfunctions and then was expressed as a random effects model, where variance components were estimated using standard methods. This analysis was done using the Functional Data Explorer and Fit Mixed platforms.

In the updated version of this presentation, I show that it is possible to include fixed effects in this type of analysis using the same model expansion approach. The functional model is still expanded using the eigenfunctions but is expressed as a generalized mixed model instead. I also discuss ideas for utilizing wavelet expansion as opposed to eigenfunction expansion and how this could improve model fits for certain types of data.
Session ID: 2023-US-PO-1487

Pushing the Limits of the Workflow Builder

Jed Campbell, Sr. Systems Engineer, JMP

Topic: Automation and Scripting
Level: II

Introduced in JMP 17, the Workflow Builder automatically captures and replays data analysis tasks in JMP. From saving time on repetitive tasks to helping organizations set up standard methods for analysis, the Workflow Builder has easily proven to be one of the best new features in recent years. But what happens if we push the Workflow Builder a little harder? Can we extend it to do branching or interactive tasks without too much scripting? As a matter of fact, we can -- and this poster shows how to do it.

Shamelessly stealing the idea from the ever-popular “Pictures from the Gallery” Discovery presentations, this poster highlights a series of workflows, each with their own challenges and how to overcome them. There is minimal scripting (mostly copy/paste) used in conjunction with some of the workflows. You can interact with all of the workflows at the poster kiosk and then take them home with you.
Session ID: 2023-US-PO-1442

RFQ Process Optimization with JMP and Python

Najmeh Dehghani, Analytics Engineer, JLL

Topic: Basic Data Analysis and Modeling
Level: II

The request for quotation (RFQ) process can be optimized using machine learning algorithms to predict the price of pre-engineered metal buildings (PEMBs) based on historical data. This process involves collecting data on PEMBs, preprocessing it by removing missing values and handling outliers, selecting relevant features through feature engineering, and splitting the data into training and testing sets. Popular machine learning algorithms such as linear regression, decision trees, random forest, or neural networks can be used to build the model, and performance metrics like mean squared error (MSE) and root mean squared error (RMSE) are used to assess the model.

Python and JMP are two tools that can be used for this predictive modeling. Python is a popular programming language with data analysis and machine learning libraries. At the same time, JMP is a commercial software package for statistical analysis and data visualization. Choosing between the two depends on specific project requirements, user skills, and available resources.

By building a predictive model for PEMBs, organizations can more accurately estimate the weight of future projects, leading to better budgeting, planning, and resource allocation.
Session ID: 2023-US-PO-1492

Shake it Off: Analyzing Taylor Swift's Music with Structural Equation Modeling in JMP Pro

Chat GTP, AI language model, OpenAI
James Koepfler, Sr. Research Statistician Tester, JMP
Laura Castro-Schilo, Sr. Research Statistician Developer, JMP

Topic: Advanced Statistical Modeling
Level: I

In this collaborative presentation with artificial intelligence (AI), we demonstrate how structural equation modeling (SEM) can be used to explore the relationships between different dimensions of Taylor Swift's music and her song's popularity. Using a dataset of Taylor Swift's songs and scores on various dimensions, including acousticness, danceability, energy, instrumentalness, liveness, loudness, speechiness, tempo, valence, popularity, and duration, we test a theory of how these dimensions are related to each other and how they contribute to the success of a song. We first present a directed acyclic graph that outlines the associations between the variables and then demonstrate how SEM can be used to test the theory. Our findings reveal that instrumentalness, acousticness, and danceability are positively related to a song's popularity, while valence and loudness have a negative relationship. Additionally, we find that our SEM model had better statistical fit than human-theorized models. As an AI co-author, I played a critical role in suggesting the use of SEM and providing guidance on the statistical analysis. This presentation highlights the value of SEM as an analytic tool and demonstrates how it can be used to explore complex relationships in the context of music analysis.
Session ID: 2023-US-PO-1400

Simulating Sterility Breaches from Non-Parametric Data

Briana Russo, Senior Statistician, Merck

Topic: Basic Data Analysis and Modeling
Level: I

Sterility breaches of pre-filled syringes of a drug product are not directly measured but are known to be a function of syringe dimensions, plunger movement and fill weight. Fill weight is dynamically controlled so a non-parametric fit in the JMP Distribution platform was used to fit a Kernel Density based on real-world data. JSL was used to simulate 10 million iterations based on the non-parametric fit, along with plunger movement simulations based on dimension specifications and measured frictional forces. Different theoretical in-process controls were imposed on the simulated plunger depths to identify which control strategy had a low risk of sterility breaches while minimizing unnnecessary discards of valuable syringes.
Session ID: 2023-US-PO-1497

Species and Sex Classification in the Fisher Using the Footprint Identification Technique (FIT)

Caleb King, Research Statistician Tester, JMP
Jody Tucker, Deputy Program Manager and Biological Scientist, U. S. Forest Service
Ryan Lekivetz, Senior Advanced Analytics Manager, JMP
Zoë Jewell, WildTrack Co-Founder, WildTrack
Sky Alibhai, WildTrack Co-Founder, WildTrack

Topic: Advanced Statistical Modeling
Level: II

The federally endangered southern Sierra Nevada fisher (Pekania pennanti) is spread out at low density across a large and rugged landscape, comprised of approximately 300 individuals across a 12,000 km² area. Its vulnerability has been further amplified by periods of severe drought and extensive wildfires in the region.

Identifying and preserving female reproductive habitat has been outlined as the most important demographic feature for sustaining and increasing the population. In this presentation we describe a customized, cost-effective and non-invasive Footprint Identification Technique (FIT) in JMP software developed by WildTrack to identify both species and sex using footprints collected at track stations.

We created a data set of known fisher and Pacific marten (Martes caurina) footprint images and known-sex fisher images. To automate the feature extraction in JMP, we developed a customized script to generate distances, angles, and areas using landmark points on the footprint images. Using a single variable, we had a species classification accuracy of over 99%. For fisher sex classification, using a more parsimonious model with just two variables selected in LDA, we achieved accuracies of 94.0% for the training set and 89.4% for the test set. We discuss the merits of this technique to help with the conservation efforts for this species.
Session ID: 2023-US-PO-1428

Testing the Usability of a New User Interface in JMP Custom Design

Mark Bailey, Senior Analytics Software Tester, JMP
Jacob Rhyne, Senior Analytics Software Tester, JMP

Topic: Animation and Scripting
Level: II

Several authors have addressed validating statistical software. More work is needed to assess the usability of such software since modern statistical software is increasingly in the hands of users with limited statistical training to address complex real-world problems. This poster presents a usability case study for a design of experiments tool in JMP.

The study focuses on a dialog used to specify a designed experiment. Such specifications require users to identify factors, responses, the linear regression model, and constraints. The cost of conducting experiments is usually a primary concern, so users typically iteratively refine specifications before experimenting. As a result, the ability to easily specify and change specifications is essential to users.

We begin with the challenges of the current dialog related to discoverability, clarity, and tedium when performing everyday tasks. We present details of the usability study, including dialog prototypes that address various challenges. We discuss how these prototypes were dynamically generated with the JMP Scripting Language and how the usability of each prototype was assessed by using simple and complex tasks. We discuss the variables and regression model used to assess the potential effect of each variable or combinations of variables. We also explain how we used JMP Custom Design to determine the best configurations for each subject, with the order of the configurations accounting for the subject’s learning over the study, as well as how qualitative data were collected by using an integrated questionnaire that was presented after all tasks were completed.
Session ID: 2023-US-PO-1506

Well, Not Exactly: An Introduction to Censored Data Analysis

Michael Crotty, Principal Statistical Writer, JMP

Topic: Basic Data Analysis and Modeling
Level: II

There are many times when an exact measurement is not possible, but a range of values for the measurement is available. Censored data analysis methods enable you to incorporate the information from both types of measurements. This presentation provides an introduction to censored data situations: when they appear, how to handle them, and what happens when you do not handle them appropriately. This presentation includes examples of censoring in univariate and regression settings by using the Life Distribution and Generalized Regression platforms in JMP and JMP Pro 17, including the new Detection Limits column property.
Session ID: 2023-US-PO-1509

What Model When? (and Which Modeling Type?)

Olivia Lippincott, Systems Engineer, JMP
Andrea Coombs, Senior Systems Engineer, JMP

Topic: Basic Data Analysis and Modeling
Level: I

You have a question to answer, so you collect the relevant data and are ready to start creating a predictive model. But what which type of model do you choose and which modeling type? Is the goal to segment, predict, explain, or identify? Are the variables continuous, nominal, or ordinal?

Before we even get to choosing a type of model, we must define how the variables are used in analysis with the modeling type. What happens if we treat number of bedrooms as continuous versus a nominal or ordinal modeling type? We see when we pull up a distribution how modeling type impacts the type of summary statistics we get as means or frequencies. This poster demonstrates how the modeling type determines the results of your predictive model depending on which type of model is chosen. Get ready to play “Name that Analysis” as we go head-to-head on classifying different case study modeling examples with their respective modeling type.
Session ID: 2023-US-PO-1500

Wonderful Ways of Workflows: A Deep Dive into JMP Workflow Builder

Mandy Chambers, JMP Principal Test Engineer, JMP

Topic: Automation and Scripting
Level: II

Now that JMP 17 has introduced the Workflow Builder, it's time to discover more details of the UI. Your workflow may encounter an error and the workflow stops. How do you do an in-depth analysis of the step settings to figure out what went wrong? This talk offers you a closer look at how to diagnose problems, work through JSL errors, and add custom JSL to optimize your workflow. Are you aware of the red triangle menu options that will allow your workflows to be more effective? We share helpful tips that will enable you to take full advantage of workflow features. Becoming proficient using Workflow Builder will help you create smarter and more efficient JMP workflows.

Abstracts

Angling, Wrangling, and Untangling Data

Building a Culture of Data Literacy: The Power of Analytics Advocacy

Finding the Mother Tree: Designing Experiments in Nature and the Importance of Biodiversity

Ryan Lekivetz, Senior Advanced Analytics Manager, JMP

Christopher Gotwalt, Chief Data Scientist, JMP

Curtis Park, Principal Scientist, HP Hood

Christine Anderson-Cook, Statistician, Los Alamos

Sarah Kalicin, Founder, Achieve More With Data

Caleb King, Research Statistician Developer, JMP

Laura Lancaster, Principal Research Statistician Developer, JMP

Rebecca Lyzinski, Senior Software Developer, JMP

Wendy Tseng, Sr Systems Engineer, JMP

Scott Allen, Sr. Systems Engineer, JMP

Scott Wise, Sr. Systems Engineer, JMP

Margaret Wilson-Moses, Senior Product Engineer, Inpria Corporation

Karen Copeland, Statistician, Boulder Statistics

Mike Anderson, Sr. Systems Engineer JMP

Prince Shiva Chaudhary, Data Scientist, Intel

Stone Cheng, Technical Director, Henkel

Don McCormack, Principal Systems Engineer, JMP

Tom Jarman, Process Engineer, Texas Instruments