Abstracts

The Flow of Discovery: Automating the standard paths and enabling the non-standard paths.

JMP Presenters: John Sall, Julian Parris, Mandy Chambers, Ryan Lekivetz, Rebecca Lyzinski and Aurora Tiffany-Davis

JMP is built for discovery, but discovery is not just happening to see something in a graph; it usually involves a lot of work and many process steps. The problem is that the steps are not always the same in each situation or in each set of data – the analysis has to be interactive. You must be able to pursue clues, identify things, drill down to details, and adapt to new conditions.

The analytic process is fundamentally interactive, and JMP supports interactivity. But there can be “easy buttons” along the way, and you can create your own “easy buttons” for the steps that you plan to take again.

Whether your work can be planned or is a wild path of exploration and serendipity, JMP has the modes to make your work speedy, efficient and productive, while keeping you in an environment of discovery.

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

Session ID: 2023-EU-30MP-1272

BEST Contributed PAPER

A Family of Orthogonal Main Effects Screening Designs for Mixed Level Factors

Bradley Jones, JMP Distinguished Research Fellow, JMP

Topic: Design of Experiments
Level: III

There is scant literature on screening when some factors are at three levels and others are at two levels. Two well-known and well-worn examples are Taguchi's L18 and L36 designs. However, these designs are limited in two ways. First, they only allow for either 18 or 36 runs, which is restrictive. Second, they provide no protection against bias of the main effects due to active two-factor interactions (2FIs). In this talk, I will introduce a family of orthogonal, mixed-level screening designs in multiples of eight runs. The 16-run design can accommodate up to four continuous three-level factors and up to eight two-level factors. The two-level factors can be either continuous or categorical. All of the designs supply substantial bias protection of the estimates of the main effects due to active 2FIs. I will show a direct construction of these designs (no optimization algorithm necessary!) using the JSL commands Hadamard product and direct product.
Session ID: 2023-EU-30MP-1339

Advanced Workflow Builder: Using the Reference Manager To Create Custom Tools

Michael Hecht, Principal Systems Developer, JMP

Topic: JSL Application Development
Level: II

The new Workflow Builder introduced in JMP 17 is a great time saver for automating a fixed set of tasks. But wouldn’t it be great to create a workflow you could use on any data table? With Workflow Builder’s Reference Manager, you can! In this presentation, we will show how to record a workflow using a specific data table, then modify it so it can be used on any other table. Using this technique, you can build a custom tool with minimal JSL coding and share it with your colleagues.
Session ID: 2022-US-45MP-1103

Coding with Continuous and Mixture Variables to Explore More of the Input Space

Micol Federica Tresoldi, Senior Research Statistician, Dow Chemical
Xinjie Tong, Senior Research Statistician, Dow Chemical

Topic: Statistics and Modeling
Level: II

This case study investigates chemical mixtures to achieve optimal properties using design of experiment (DOE) data. The formulation space consists of four input variables: Chemical A Type, Chemical B Type, Chemical C Type, and Chemical D Content. The first three variables represent different compositions for making Chemical A, B and C, respectively, and as such, can be coded both as categorical factors, as well as continuous mixture variables.

We created the DOE treating them as categorical due to the experimental constraints. However, at the data analysis stage, even after considering thousands of simulated hypothetical formulations, none of them was predicted to meet the desired properties. At that point, to be able to identify promising subregions, we needed to overcome the discreteness of the space. So, we recoded those two factors as continuous and mixture variables, derived the equivalent regression model, and reran the simulations. Indeed, under certain assumptions, this coding strategy enables one to interpolate and consider missing compositions not present in the original DOE.

In this presentation, we demonstrate how to use JMP Pro 16 Profiler Simulation feature with Graph Builder to achieve an extensive and insightful exploration of the formulation space applicable to diverse fields.
Session ID: 2023-EU-30MP-1206

DoE-Assisted Chromatography Method Development for the Analysis Biopharmaceuticals

Giulia Lambiase, Senior Scientist, AstraZeneca

Topic: Design of Experiments
Level: II

Cation-exchange chromatography (CEX) is the industry gold standard for the analysis of biopharmaceutical charge variants. However, the development of CEX methods in a time and resource-efficient manner constitutes a bottleneck in product characterization. CEX separations are complex and governed by multiple factors. Several scientific publications have proven the successful application of design-of-experiment (DoE) in chromatography method development. Nevertheless, performing DoEs with a large number of factors may be challenging, time-consuming, and expensive. This work illustrates the use of a split-DoE approach to aid the development of a CEX method for the analysis of the charge variants profile of a mAb candidate. Analytical method development was intended to provide a high-throughput (HT) CEX method to support charge variants analysis with minimal sample and time requirements. The split-DoE approach is based on fundamental knowledge of the CEX separation mechanism and aims to reduce the number of experimental runs whilst exploring a wide experimental space. Regression modeling was used to study the effect of both individual process parameters and their interactions on the separation efficiency to ultimately identify the optimal method conditions. This study provides an efficient workflow for leveraging the development of CEX methods.
Session ID: 2023-EU-30MP-1306

Enrich Databases With Useful Information

Mauro Gerber, Data Scientist, Huber+Suhner AG

Topic: JSL Application Development
Level: II

JSL can be used to make GUIs to access data in measurement systems with pre-programmed scripts, such as for SPC. For this to work, JMP® must remember the control limits from the status quo when new data arrives. A second JSL script is needed to connect to a DB and load existing limits for variables if they are present. Control limits can be altered/added manually and updated in the data table or automated with default values and a factor for multiple variables. When others pull the data again from the DB, the control limits are automatically added to the column parameter and go into the SPC. This presentation will provide a live demo and JSL example of how to insert and update data in a DB.
Session ID: 2023-EU-30MP-1296

Exploring Group Differences and Other New Features in Structural Equation Models

Laura Castro-Schilo, Senior Research Statistician Developer, JMP

Topic: Data Exploration
Level: II

The structural equation models (SEM) platform continues to grow and evolve into a more complete and powerful platform. An important feature added to SEM in JMP® Pro 17 is multiple-group analysis (MGA). MGA allows users to test for differences in parameters across populations by enabling the specification of models that can have group-specific estimates or equality constraints on parameters across groups. In this presentation, we will demonstrate the use of MGA and other new features in SEM using real data examples. We start with a simple regression example and then turn to a longitudinal analysis example that showcases the flexibility of MGA. Lastly, we show how survey development can be expedited by a new feature that links Exploratory Factor Analysis to the SEM platform.
Session ID: 2023-EU-30MP-1326

Growing Your Internal Talent: Resources From JMP® Education

Di Michelson. Principal Analytical Training Consultant, JMP

Topic: Data Exploration
Level: I

Has your world changed in the past couple of years? Ours has too! We hope our changes will make your job easier. This talk will present our newly-free and low-cost options to educate your engineers and scientists. We offer instructor-led courses on our public schedule and will add courses to the public schedule on request. If you have instructors in-house, we can provide them with our course materials, and they can adapt our demonstrations to use data relevant to your students. We also have free self-paced eLearning available. We are looking for ways to help you even more! What topics do you want to see for half-hour Mastering JMP sessions, one-hour Deeper Dive sessions, or multi-day analytics education? What times should we offer these sessions? In-person or remote? This session will include time to gather your feedback.
Session ID: 2023-EU-30MP-1283

JMP® in Qualification and Validation of Biological Assays

Alexander Gill, Laboratory Expert, VelaLabs

Topic: Quality and Reliability
Level: II

Statistical evaluation for biological assays is critical because a lot of data needs to be summarized for reporting to customers and authorities for drug registration. JMP® is a helpful tool not only for calculating the required parameters but also for automating evaluation. For example, it can be used to graph and automate calculations for Repeatability, Intermediate Precision, Linearity, and Robustness of a Relative Potency Assay. Because these calculations are often required, a single JMP file for calculating the parameters saves a lot of time and can be used by novice users. Furthermore, the evaluation remains consistent throughout various assays, even when different technologies are used, such as SPR, ELISA, or Cell-Based Assays.
Session ID: 2023-EU-30MP-1350

Looking at the Big Picture: Equivalence Between Two Sets of Batch Data Over Time

Gwenola Ninon, Staff Scientist, Statistics and Data Analytics, Fujifilm Diosynth Biotechnologies

Topic: Quality and Reliability
Level: II

Working as a manufacturer in the biopharmaceutical industry means we often need to show that we obtain similar results on different sites when transferring a manufacturing process and, notably, when scaling processes up or down. Comparison techniques such as t-tests and ANOVAs are widespread, but equivalence testing has become a standard way to show two processes behave similarly. When we look at a few parameters, those techniques are easy to apply, but when we have large numbers of variables, it becomes difficult to see the bigger picture. The challenge with equivalence testing is that it requires the scientists to provide a value for what they deem an acceptable difference between the groups of data. In addition, many processes change over time, and we are interested in capturing whether they behave similarly across the duration of the process. JMP scripting is a great way to automate the data prep, visualisations, and production of all the plots and comparison tests for those data sets. The multivariate platform in JMP helps create a holistic picture of the process for each time point. We can now use equivalence testing and relate it to the individual variable contributions.
Session ID: 2023-EU-30MP-1277

My Gauge Isn’t as Good as It Could Be—Will Its Errors Cost Money? And How Much?

Jerry Fish, Systems Engineer, JMP
Jason Wiggins, Senior Systems Engineer, JMP

Topic: Quality and Reliability
Level: I

All gauges have errors. They might be minuscule, or they might be large, but they always exist. Large or small, the errors lead to gauges having some likelihood of making Type 1 and Type 2 errors (passing a bad part or failing a good part). The mistake likelihood is higher for parts that lie near the specification limits. These errors cost real money! But how do we quantify those costs? This paper builds on the results shown in a 2022 JMP Americas Discovery paper (2022-US-30MP-1123) that discussed how to quantify the gauge performance and how to set “informative manufacturing specs” (or guardbands) to improve the gauge’s performance in segregating good vs. bad parts. In this paper, we extend the learning and script functionality as we discuss how to combine gauge characteristics with the costs of individually passing a failed part, rejecting a good part, and projecting production volumes. This gives insight into risk analyses, e.g., how much I should budget to account for gauge errors, whether (or how much) to spend on improving our gauge, etc.
Session ID: 2023-EU-30MP-1256

Pictures From the Gallery 8: Innovative Graph Builder Views

Scott Wise, Senior Systems Engineer, JMP

Topic: Data 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 advanced 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 will 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 will be included that can help breathe new life into your graphs and reports!
Session ID: 2023-EU-30MP-1319

Revisiting Pearl’s Influenza Studies Using JMP®

Roselinde Kessels, Assistant Professor, Maastricht University
Chris Gotwalt, Chief Data Scientist, JMP
Guido Erreygers, Professor of Economics, University of Antwerp

Topic: Data Exploration
Level: II

In 1919 and 1921, Raymond Pearl published four empirical studies on the Spanish flu epidemic. He explored the factors that might explain the epidemic’s explosiveness and destructiveness in America’s largest cities. Using partial correlation coefficients, he tried to isolate the net effects of the possible explanatory factors, such as general demographic characteristics of the cities and death rates for various diseases, on the variables measuring the severity of the epidemic. In this presentation, we revisit Pearl’s data and apply variable selection with a pseudo-variable in JMP®'s Generalized Regression platform instead of Pearl’s correlation analysis. We use Poisson forward selection on the variables in the platform with AICc validation. We find that our results largely correspond with Pearl’s conclusions but contain some additional nuances that are substantive. This paper contributes to the literature showing that Poisson regression proves useful for historical epidemiology. JMP’s Generalized Regression platform offers researchers much flexibility in choosing the most appropriate model.
Session ID: 2023-EU-30MP-1201

Speeding Up Assay Development with Automated Workflow for Assessing Plate Bias of Microtiter Assays

Tomasz Witkos, Senior Scientist, AstraZeneca
Hadley Myers, Senior Systems Engineer, JMP

Topic: JSL Application Development
Level: II

Understanding positional effects in microtiter plate-based assays is an important step in developing robust potency assays for testing biologics. Here, you'll see a bespoke JMP application that automates the analysis of multiple-plate experiments aimed at detecting plate bias. The application, using the raw output plate reader file, creates plate map visualizations, calculates IC 50 /EC 50 of drug dose-response curves, and analyses row and column variabilities across plates. What typically took scientists half a day of analysis, error-prone manual formatting of datasets, and the need for external software for curve fitting, can now be achieved in JMP in under one minute.
Session ID: 2023-EU-30MP-1327

Uncovering Gene Patterns in 3D Matrices of Single Cell Data: An Application of Two New JSL Add-ins

Paul Fogel, Consultant
Orson Jay, Intern, Sanofi
Galina Boldina, Principal Data Scientist, Sanofi
Franck Augé, Senior Director, Omics Data Sciences, Sanofi
George Luta, Professor, Department of Biostatistics, Bioinformatics and Biomathematics at Georgetown University
Alexey Fedorov, Senior Python Engineer, Advestis

Topic: Data Exploration
Level: II

Matrices are second-order tensors. In some applications, the input data are third- or higher-order tensors. For example, gene expression data from different cell types found in a single donor form a second-order tensor of the form {cell type × gene}, while the collection of similar cell types on different donors results in a third-order tensor of the form {cell type × gene × donor}. We present two JMP Add-ins, the first of which performs multi-way data analysis of a third-order tensor using non-negative tensor factorization. The second Add-in allows visualization of the major expression patterns found in the tensor in the form of parallel heatmaps. Using a public transcriptome dataset, we show how these tools enable the discovery and visualization of tri-clusters of cell types, genes, and donors that share similar transcriptomic patterns, resulting in cell type signatures that can be used to deconvolute bulk mixtures of different cell types. We also show that the Python package used to perform the core computations can be integrated seamlessly into JSL scripts.
Session ID: 2023-EU-30MP-1329

Using Graph Builder’s Splines To Align Measurement Curves

Bernd Heinen, Owner, Stabero

Topic: Data Access & Manipulation
Level: II

A frequent task in data analysis is aligning curves before a descriptive or root cause analysis. Often an additional complication occurs when the measurement intervals are not equidistant in the series to be compared. There is not one single value that quantifies the shift for a whole curve. Interpolation is the solution in cases like this. Simple linear interpolation may lead to numerous random errors; a spline interpolation is more robust. Since the Graph Builder exports the formula for the spline in its current shape, it became an easy, accessible tool for the alignment of curves. And as all steps can be programmed in JSL, it provides a framework for automating curve alignment. This presentation will describe the background, concept, and case study application for the alignment of curves.

Session ID: 2023-EU-PO-1345

Applications of MSA Platform Tools in JMP® 17

Vasco Cachaco, Business Improvement and Analytics Engineer, Xaar pic

Topic: Quality and Reliability
Level: II

Measurement System Analysis is a methodical approach to identifying and managing the sources of variation that can influence the measurement system. At the top level, this type of analysis enables the quantification of the measurement system variation (MV) present in the Total Observed Variation (TV) of a process or system. It separates it from the Process (part-to-part) Variation (PV). The measurement variation can be broken down further into Precision and Accuracy. For the Precision component, we follow a sequential method for Continuous Data to determine the adequacy of a measurement system. A Type 1 Gauge Study examines the accuracy and consistency of the measurement device, and a Full Gauge R&R Study explores the Repeatability and Reproducibility of the entire measurement system. This poster will demonstrate the practical application of these tools in JMP 17.
Session ID: 2023-EU-30MP-1260

Data Driven Selection as a First Step for a Fast and Future Proof Process Development

Egon Gross, Research & Technology Manager, Symrise AG
Peter Hersh, Senior Systems Engineer, JMP
Bill Worley, Senior Global Enablement Engineer, JMP

Topic: Data Exploration
Level: II

This talk will focus on how JMP® helped drastically reduce the cultivation experimentation workload and improved response from four up to 30-fold, depending on the target. This was accomplished by screening potential media components, generally the first, and sometimes tedious, step in fermentation optimizations. Taking characteristic properties such as the chemical composition of complex components like yeast extracts enables flexibility in the case of future changes. We describe the procedure for reducing the workload using FT-MIR spectral data based on a DSD setup of 27 media components. After several standard chemometric manipulations, enabled by different Add-ins in JMP® 16, the workload for cultivation experiments could be drastically reduced. In the end, important targets could be improved up to approximately 30-fold as a starting point for subsequent process optimizations. As JMP® 17 was released in the fall of 2022, the elaborate procedure in version 16 will be compared to the integrated features. It might give space for more inspiration – for developers and users alike.
Session ID: 2023-EU-PO-1231

Easy DOE: Easy Enough for a 7-Year-old?

Ryan Lekivetz, Advanced Analytics Manager, DOE & Reliability, JMP
Lorelai Lekivetz, 7-year-old, Northwoods Elementary

Topic: Design of Experiments
Level: I

JMP 17 introduces the Easy DOE platform, providing both flexible and guided modes to users, aiding their design choices. In addition, Easy DOE allows for the DOE workflow from design through data collection and modeling. This presentation offers a preview of the new Easy DOE platform, including insights from a 7-year-old using the new platform on a DOE problem of her choosing.
Session ID: 2023-EU-PO-1210

Laney P’ and U’ Control Charts New in JMP® 17

Annie Zangi, Senior Research Statistical Developer, JMP

Topic: Quality and Reliability
Level: II

Traditionally, control charts for attribute data (p-charts and u-charts) assume the data is either binomial or Poisson and that the mean is constant over time. Rarely is this assumption true in practice. David Laney developed a technique that solves the problem so that control charts work well whether the mean parameter is stable or not. This talk will both walk through the evolution of the Laney P’ and U’ charts and show examples of how best to apply the charts.
Session ID: 2023-EU-PO-1229

New Tools for Modeling and Visualizing Moderation and Moderated Mediation in JMP® Pro

Haley Yaremych, Statistical Testing Intern, JMP
Laura Castro-Schilo, Senior Research Statistician Developer, JMP

Topic: JSL Application Development
Level: I

Often as we are trying to gain insights from our data, understanding that two variables are related is not enough. We need to dig deeper and ask questions like: under what circumstances are they related? For whom are they related, why are they related, and how ? Moderation (i.e., interactions), mediation, and moderated mediation models allow us to answer these types of questions. These models are popular and important but cumbersome to fit. Furthermore, visualizations essential for understanding interactions are difficult to create from scratch. This presentation will describe the Moderation and Mediation Add-In for JMP Pro, which enables easy specification, fitting, and visual probing of interactions in three popular models: moderation, first-stage moderated mediation, and second-stage moderated mediation. With minimal user input, the add-in automatically specifies and estimates the appropriate model. Then, the results are processed and packaged into ready-to-publish output. An interactive Johnson-Neyman plot, as well as a simple slopes plot, is created. We will provide an in-depth demonstration of these features using an example from psychology. Academics and data analysts across the social, behavioral, educational, and life sciences will benefit from this novel functionality.
Session ID: 2023-EU-PO-1342

Predictive Models for (BCO) Lameness Using Health, Stress, and Leg Health Parameters in Broilers

Andy Mauromoustakos, Professor, University of Arkansas
Shawna L. Weimer, Assistant Professor, University of Arkansas

Topic: Design of Experiments
Level: II

Stress and lameness negatively affect animals' health, production, and welfare. The following physiological and non-invasive measures of stress and lameness were measured: relative bursa weight, core body temperature, corticosterone (CORT) concentrations in serum and feathers, surface temperatures of the head (eye and beak) and legs (hock, shank, and foot) regions by infrared thermography (IRT), leg blood and oxygen saturation. The JMP Pro 17 Model Screening platform was used to fit several parametric and machine learning models to the binary response variable (Lame=1) of the 258 study birds on the nine health and stress indicators mentioned above. We selected K Fold Cross-validation with K=5 and repeated the process twice (N Trials Folds=10). The best two models were the Neural Boosted (Mean AUC=.985 and misclassification rate of zero in 50 validation birds) and the Generalized Regression Lasso (Mean AUC=.975 and misclassification rate of 1 in 50 validation birds). Both models agreed on predictor effects when using Model Comparison of JMP Pro to compare them further and are found nonsignificant in comparing their AUC.
Session ID: 2023-EU-PO-1343

Yield and Quality Issue Solving by Correlating Optical Inspection Step Results With Electrical Tests

Corinne Berges, Six Sigma Master Black Belt for NXP Structured Problem Solving, NXP Semiconductors
Shilu Zhang, Data Scientist for Data Engineering & Analytics Team, Manufacturing IT, NXP Semiconductors
James Bird, Technical Director, Six Sigma Black Belt, Data Scientist, NXP Semiconductors
Chris Smith, Senior Quality Director, Six Sigma Master Black Belt, NXP Semiconductors

Topic: Data Exploration
Level: II

In the semiconductor manufacturing industry for automotive, parts are tested at each manufacturing step to screen likely-to-fail parts. The further upstream the weak parts are scrapped, the lower the scrapping cost will be. But testing has a cost as well. A recent project at NXP sought to avoid a manual defect classification of the defects observed at the wafer inspection level. Defects are now classified as killer or not-killer from a training image dataset, and a failure probability is assessed for each die. JMP® allows a further step in correlating this failure probability to electrical tests with three types of analysis. The first analysis assessed a failure probability threshold to limit the number of parts tested to limit test cost. The second analysis highlighted the tests most correlated with failure probability. The final analysis used the list of highlighted tests to adjust test limits to screen the parts with failure probability outliers. The analyses limit test costs while increasing quality.

Abstracts

The Flow of Discovery: Automating the standard paths and enabling the non-standard paths.

BEST Contributed PAPER