Discovery Summit US Dots Background

Americas 2020

October 12-16

Register

Agenda

On-Demand Sessions

Papers and Posters

Join in the discussion!

  • Twenty paper presentations will be streamed live during Discovery Summit. Check the dates and times below. Presenters will be available for questions while these recordings are streaming.
  • Poster presenters will be on-hand live on Friday, Oct. 16 at 1:30 p.m. to answer questions.

Live Sessions

Monday, Oct. 12

12:30 - 3:30 p.m. ET

Attendee-driven "Unsessions"

According to Wikipedia, an unconference is a participant-driven meeting designed to encourage attendee involvement in topic selection and knowledge sharing. Join us for these lightly structured discussions to share your ideas and learn more about JSL scripting or predictive modeling. If you’ve got suggestions for topics you’d like to see discussed, or that you’d like to share, contribute your ideas using the buttons below.

Tuesday, Oct. 13

11:30 a.m. - 12:15 p.m. ET

Yoga: East Coast Midday Stretch or Sunrise in the West

What’s the best way to prepare your mind and body for being open to a week of learning and growing? That would be yoga! Whether you’re already warmed up or just getting started for the day, you’ll want to join us for this JMP-led yoga session.

12:30 - 12:45 p.m. ET

In the Flow

We talk about being “in the flow” when we’re analyzing data without interruption, when we’re writing the coolest code, or just really, really focused on a task. This is your chance to get in the Discovery Summit flow with hosts Jeff Perkinson and Jessica Marquardt. This is how you’ll be in the know about papers, posters, plenaries and all of the interactive goodness that comes with an online Discovery Summit.

12:45 - 1:45 p.m. ET

Plenary Session – Anabolic, Aphrodisiac or Analgesic?

John Sall, Co-Founder and Executive Vice President, SAS

 

As we develop analytical tools in JMP, we inevitably must make decisions about how to prioritize:

  1. Should we make the product more powerful by adding more muscle?
  2. Should we make it sexier, more exciting?
  3. Should we focus on pain relief, making it less frustrating, less burdensome?

In the language of long A-words, should we go for anabolic, aphrodisiac or analgesic? John Sall thinks the answer is analgesic. Pain relief should be the central motivating force for development.

Of course, the three aren’t mutually exclusive. Adding an exciting power feature could also relieve pain. But pain relief is central, because pain is the condition that can really freeze us, demotivate us, make us stop at a less-than-full perspective of what our data can tell us.

1:50 - 4:45 p.m. ET

Paper Presentations

What’s the next best thing to watching a Discovery paper together in person? It’s watching it live online, where you can comment in real time, ask the presenters questions, and applaud the parts that make you go “wow.”

  • Session ID: 2020-US-45MP-549

    1:50 p.m.

     

    Let's Talk Tables

    Mandy Chambers, JMP Principal Test Engineer, SAS

    Kelci Miclaus, Senior Manager Advanced Analytics R&D, JMP Life Sciences, SAS

    • Topic: Data Access and Manipulation
    • Level: 2

    JMP has many ways to join data tables. Using traditional Join you can easily join two tables together. JMP Query Builder enhances the ability to join, providing a rich interface allowing additional options, including inner and outer joins, combining more than two tables and adding new columns, customizations and filtering. In JMP 13, virtual joins for data tables were developed that enable you to use common keys to link multiple tables without using the time and memory necessary to create a joined (denormalized) copy of your data. Virtually joining tables gives a table access to columns from the linked tables for easy data exploration. In JMP 14 and JMP 15, new capabilities were added to allow linked tables to communicate with row state synchronization. Column options allow you to set up a link reference table to listen and/or dispatch row state changes among virtually joined tables. This feature provides an incredibly powerful data exploration interface that avoids unnecessary table manipulations or data duplications. Additionally, there are now selections to use shorter column names, auto-open your tables and a way to go a step further, using a Link & ID and Link & Reference on the same column to virtually “pass through” tables. This presentation will highlight the new features in JMP with examples using human resources data followed by a practical application of these features as implemented in JMP Clinical. We will create a review of multiple metrics on patients in a clinical trial that are virtually linked to a subject demographic table and show how a data filter on the Link ID table enables global filtering throughout all the linked clinical metric (adverse events, labs, etc.) tables.

  • Session ID: 2020-US-45MP-538

    2:25 p.m.

     

    21st Century Screening Designs

    Bradley Jones, JMP Distinguished Research Fellow, SAS

    • Topic: Design of Experiments
    • Level: 2

    JMP has been at the forefront of innovation in screening experiment design and analysis. Developments in the last decade include Definitive Screening Designs, A-optimal designs for minimizing the average variance of the coefficient estimates, and Group-orthogonal Supersaturated Designs. This tutorial will give examples of each of these approaches to screening many factors and provide rules of thumb for choosing which to apply for any specific problem type.

  • Session ID: 2020-US-30MP-621

    3:00 p.m.

     

    Statistical Process Control for Process Variables that have a Functional Form

    Steve Hampton, Process Control Manager, PCC Structurals

    Jordan Hiller, JMP Senior Systems Engineer, SAS

    • Topic: Quality and Reliability
    • Level: 2

    Many manufacturing processes produce streams of sensor data that reflect the health of the process. In our business case, thermocouple and vacuum curves are key process variables in a manufacturing plant. The process produces a series of sensor measurements over time, forming a functional curve for each manufacturing run. These curves have complex shapes, and blunt univariate summary statistics do not capture key shifts in the process. Traditional SPC methods can only use point measures, missing much of the richness and nuance present in the sensor streams. Forcing functional sensor streams into traditional SPC methods leaves valuable data on the table, reducing the business value of collecting this data in the first place. This discrepancy was the motivator for us to explore new techniques for SPC with sensor stream data. In this presentation, we discuss two tools in JMP — the Functional Data Explorer and the Model Driven Multivariate Control Chart — and how together they can be used to apply SPC methods to the complex functional curves that are produced by sensors over time. Using the business case data, we explore different approaches and suggest best practices, areas for future work and software development. In this presentation, we discuss two tools in JMP -- the Functional Data Explorer and the Model Driven Multivariate Control Chart -- and how together they can be used to apply SPC methods to the complex functional curves that are produced by sensors over time. Using the business case data, we explore different approaches and suggest best practices, areas for future work and software development.

  • Session ID: 2020-US-45MP-614

    3:35 p.m.

     

    Creating a JSL Script Ecosystem: GIT, Unit Test, VBA for PPT, Crash Log Collection and More

    Serkay Olmez, Sr. Staff Data Scientist, Seagate Technology

    Fred Zellinger, Sr. Staff Engineer, Seagate

    • Topic: JSL Application Development
    • Level: 3

    With many users and multiple developers, it becomes crucial to manage and source control JSL scripts. This talk outlines how to set up an open source system that integrates JSL scripts with GIT for source control and remote access. The system can also monitor the usage of scripts as well as crash log collection for debugging. Other features such as VBA scripting for PPT generation, unit testing, and user customization are also integrated to create high quality JSL scripts for a wide range of user base.

  • Session ID: 2020-US-45MP-548

    4:10 p.m.

     

    Characterizing Bio-processes With Augmented Full Quadratic Models and FWB+AV

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

    Tiffany D. Rau, Ph.D., Owner and Chief Consultant, Rau Consulting, LLC

    • Topic: Predictive Modeling
    • Level: 2

    Quality by Design (QbD) is a design and development strategy where one designs quality into the product from the beginning instead of attempting to test-in quality after the fact. QbD initiatives are primarily associated with bio-pharmaceuticals, but contain concepts that are universal and applicable to many industries. A key element of QbD for bio-process development is that processes must be fully characterized and optimized to ensure consistent high quality manufacturing and products for patients. Characterization is typically accomplished by using response surface type experimental designs combined with the full quadratic model (FQM) as a basis for building predictive models. Since its publication by Box (1950) the FQM is commonly used for process characterization and optimization. As a second order approximation to an unknown response surface, the FQM is adequate for optimization. Cornell and Montgomery (1996) showed that the FQM is generally inadequate for characterization of the entire design space, as QbD requires, given the inherent nonlinear behavior of biological systems. They proposed augmenting the FQM with higher order interaction terms to better approximate the full design regions. Unfortunately, the number of additional terms is large and often not estimable by traditional regression methods. We show that the fractionally weighted bootstrapping method of Gotwalt and Ramsey (2017) allows the estimation of these fully augmented FQMs. Using two bio-process development case studies we demonstrate that the augmented FQM models substantially outperform the traditional FQM in characterizing the full design space. The use of augmented FQMs and FWB will be thoroughly demonstrated using JMP Pro 15.

2:00 - 5:00 p.m. ET

Discovery Expo

Who says online in real time can’t be as fun as in-person events? That’s a silly question. We know it’s not as fun. But we’re making the most of the online platform to create a very special Discovery Summit experience for you. Come to the Discovery Expo for Meet-the-Developer opportunities, Birds-of-a Feather sessions, opportunities to vote for your favorite papers and posters, talk to Tech Support, buy JMP gear, browse books, and more.

5:00 - 6:00 p.m. ET

Social: Welcome One and All

JMP employees are known for a lot of things. One of them is our hospitality. In fact, first-time Discovery attendees have been heard to exclaim wonderful things about how welcome they felt from the moment they entered the venue. Join us for a social hour for new registrants, JMP employees and Discovery veterans alike. If you like, we’ll introduce you around so that everyone knows your name.

Wednesday, Oct. 14

11:00 a.m. - 12:00 p.m. ET

Fun Run

Just like during an in-person Discovery Summit, staff and attendees will get the blood pumping with a fun run. Someone from JMP will organize your run and explain the technology.

12:30 - 12:45 p.m. ET

In the Flow

We talk about being “in the flow” when we’re analyzing data without interruption, when we’re writing the coolest code, or just really, really focused on a task. This is your chance to get in the Discovery Summit flow with hosts Jeff Perkinson and Jessica Marquardt. This is how you’ll be in the know about papers, posters, plenaries and all of the interactive goodness that comes with an online Discovery Summit.

12:45 - 1:45 p.m. ET

Plenary Session – The Gender Data Gap

Caroline Criado Perez, Author, Invisible Women: Exposing Data Bias in a World Designed for Men

 

Ever wonder what happens when entire segments of a population are ignored, and gathering data about them isn’t even considered? Simply look around. Author of Invisible Women, Caroline Criado Perez joins us to explore the gender data gap and invisible bias that has a profound effect on women’s lives. After sharing case studies and research results, she will answer your questions about the hidden ways in which women are excluded from the very building blocks of the world we live in.

1:50 - 4:45 p.m. ET

Paper Presentations

What’s the next best thing to watching a Discovery paper together in person? It’s watching it streamed live online, where you can comment in real time, ask the presenters questions, and applaud the parts that make you go “wow.”

  • Session ID: 2020-US-45MP-590

    1:50 p.m.

     

    ABCs of Structural Equations Models

    Laura Castro-Schilo, JMP Research Statistician Developer, SAS

    James Koepfler, JMP Research Statistician Tester, SAS

    • Topic: Predictive Modeling
    • Level: 2

    This presentation provides a detailed introduction to Structural Equation Modeling (SEM) by covering key foundational concepts that enable analysts, from all backgrounds, to use this statistical technique. We start with comparisons to regression analysis to facilitate understanding of the SEM framework. We show how to leverage observed variables to estimate latent variables, account for measurement error, improve future measurement and improve estimates of linear models. Moreover, we emphasize key questions analysts’ can tackle with SEM and show how to answer those questions with examples using real data. Attendees will learn how to perform path analysis and confirmatory factor analysis, assess model fit, compare alternative models and interpret all the results provided in the SEM platform of JMP Pro.

  • Session ID: 2020-US-45MP-532

    2:25 p.m.

     

    Finding the Source of Grandma’s Chili: Investigative Text Mining

    Scott Wise, Senior Manager, JMP Education Team, SAS

    • Topic: Predictive Modeling
    • Level: 1

    The power of using Text Mining is a great tool in investigating all kinds of unstructured text that commonly resides in our collected data. From notes captured on warranty issues, lab testing/experimental comments, to even looking at food recipes, this new method opens a lot of opportunity to better understand our world. In this presentation, we will show how to use the latest text analytic methods to help solve a family mystery as to the regional source of my Grandma’s delicious chili recipe. Along the way, we will see how to use text mining to create leading terms and phrase lists and word cloud reports. Then we will utilize the resulting document term matrix to perform topic analysis (via latent class analysis clustering) that will enable us to find a solution to our question. You will be left with an understanding of the powerful text mining approaches that you can add to your own toolbox and start solving your own text data challenges!

  • Session ID: 2020-US-30MP-562

    3:00 p.m.

     

    JMP BEAST Mode: Boundary Exploration through Adaptive Sampling Techniques

    James Wisnowski, Principal Consultant, Adsurgo

    Andrew Karl, Senior Statistical Consultant, Adsurgo

    • Topic: Design of Experiments
    • Level: 2

    Testing complex autonomous systems such as auto-navigation capabilities on cars typically involves a simulation-based test approach with a large number of factors and responses. Test designs for these models are often space-filling and require near real-time augmentation with new runs. The challenging responses may have rapid differences in performance with very minor input factor changes. These performance boundaries provide the most critical system information. This creates the need for a design generation process that can identify these boundaries and target them with additional runs. JMP has many options to augment DOEs conducted by sequential assembly where testers must balance experiment objectives, statistical principles, and resources in order to populate these additional runs. We propose a new augmentation method that disproportionately adds samples at the large gradient performance boundaries using a combination of platforms to include Predictor Screening, K Nearest Neighbors, Cluster Analysis, Neural Networks, Bootstrap Forests, and Fast Flexible Filling designs. We will demonstrate the Boundary Explorer add-in tool with an autonomous system use-case involving both continuous and categorical responses. We provide an improved “gap filling” design that builds on the concept behind the Augment “space filling” option to fill empty spaces in an existing design.

  • Session ID: 2020-US-45MP-580

    3:35 p.m.

     

    Lubricant Research Using JMP Non-Linear Regression

    Fred Girshick, Principal Technologist, Infineum USA, L.P.

    • Topic: Data Exploration
    • Level: 2

    Wherever there are moving parts, surfaces come into contact and need to be lubricated. Development of lubricants, particularly engine oils, relies on fundamental chemical knowledge, applied physics, bench-top experiments and small-scale fired engine tests; but the ultimate – and only certain – proof of performance are full-scale field tests in the engines under actual operating conditions. The results of these tests, for example, oxidation of the hydrocarbon oil, are inherently non-linear. After a brief introduction of engine oil characteristics and parameters, this paper will present several examples from passenger cars, heavy-duty trucks, railroad, stationary natural gas and marine engines where the JMP non-linear platform and graphing capabilities were used to differentiate performance of engines and engine oils. Both single-variable and categorical cross variable models are used.

  • Session ID: 2020-US-45MP-606

    4:10 p.m.

     

    Fault Detection and Diagnosis of the Tennessee Eastman Process Using Multivariate Control Charts

    Jeremy Ash, JMP Analytics Software Tester, SAS

    • Topic: Quality and Reliability
    • Level: 2

    The Model Driven Multivariate Control Chart (MDMVCC) platform enables users to build control charts based on PCA or PLS models. These can be used for fault detection and diagnosis of high dimensional data sets. We demonstrate MDMVCC monitoring of a PLS model using the simulation of a real world industrial chemical process — the Tennessee Eastman Process. During the simulation, quality and process variables are measured as a chemical reactor produces liquid products from gaseous reactants. We demonstrate fault diagnosis in an offline setting. This often involves switching between multivariate control charts, univariate control charts, and diagnostic plots. MDMVCC provides a user-friendly way to move between these plots. Next, we demonstrate how MDMVCC can perform online monitoring by connecting JMP to an external database. Measuring product quality variables often involves a time delay before measurements are available, which can delay fault detection substantially. When MDMVCC monitors a PLS model, the variation of product quality variables is monitored as a function of process variables. Since process variables are often more readily available, this can aide in the early detection of faults.

2:00 - 5:00 p.m. ET

Discovery Expo

Who says online in real time can’t be as fun as in-person events? That’s a silly question. We know it’s not as fun. But we’re making the most of the online platform to create a very special Discovery Summit experience for you. Come to the Discovery Expo for Meet-the-Developer opportunities, Birds-of-a Feather sessions, opportunities to vote for your favorite papers and posters, talk to Tech Support, buy JMP gear, browse books, and more.

5:00 - 6:00 p.m. ET

Social: Industry & Academic Connections

We all start somewhere, and for many of us it was in a university studying to become a scientist, engineer or other type of data explorer. This is a unique opportunity for those of us who have made it in industry to share our journeys with students who will follow in our footsteps, and with professors who offer guidance to those students. All are welcome to join in the conversations and make connections.

Thursday, Oct. 15

11:00 a.m. - 12:00 p.m. ET

Bike Ride

Put on your most colorful cycle gear and hop on your two-wheeler. If you’ve got a JMP cycling outfit, wear that! What a fun way to warm up for an awesome day of learning!

12:30 - 12:45 p.m. ET

In the Flow

We talk about being “in the flow” when we’re analyzing data without interruption, when we’re writing the coolest code, or just really, really focused on a task. This is your chance to get in the Discovery Summit flow with hosts Jeff Perkinson and Jessica Marquardt. This is how you’ll be in the know about papers, posters, plenaries and all of the interactive goodness that comes with an online Discovery Summit.

12:45 - 1:45 p.m. ET

Plenary Session – The Story Behind the Story

Aleszu Bajak, Graduate Programs Manager, Northeastern University School of Journalism

Anna Flagg, Senior Data Reporter, The Marshall Project

Andrew Ba Tran, Data Reporter, The Washington Post

Moderator: Julian Parris, JMP Strategic Initiatives and Analytics, SAS

 

While journalists have long made use of data in breaking news stories and investigative reporting, media outlets are increasingly using data visualization as a tool to convey information to the public. Compelling graphics not only make the fact-finding aspects of journalism more transparent, they are also an essential part of the investigative process. Data journalists in particular rely heavily on exploratory data analysis. Three successful journalists share the stories behind some of their most significant work.

1:50 - 4:45 p.m. ET

Paper Presentations

What’s the next best thing to watching a Discovery paper together in person? It’s watching it streamed live online, where you can comment in real time, ask the presenters questions, and applaud the parts that make you go “wow.”

  • Session ID: 2020-US-30MP-541

    1:50 p.m.

     

    Using Auto-Validation to Analyze Screening DOEs

    Peter Hersh, JMP Senior Systems Engineer, SAS

    Phil Kay, JMP Learning Manager Global Enablement, SAS

    • Topic: Design of Experiments
    • Level: 2

    In the process of designing experiments often many potential critical factors are identified. Investigating as many of these critical factors as possible is ideal. There are many different types of screening designs that can be used to minimize the number of runs required to investigate the large number of factors. The primary goal of screening designs is to find the active factors that should be investigated further. Picking a method to analyze these designs is critical, as it can be challenging to separate the signal from the noise. This talk will explore using the auto-validation technique developed by Chris Gotwalt and Phil Ramsey to analyze different screening designs. The focus will be on group orthogonal supersaturated designs (GO-SSDs) and definitive screening designs (DSDs). The presentation will show the results of auto-validation techniques compared to other techniques to analyze these screening designs.

  • Session ID: 2020-US-45MP-615

    2:25 p.m.

     

    Towards Predicting the Fate of Reef Corals

    Anderson Mayfield, Assistant Scientist, University of Miami

    • Topic: Data Exploration
    • Level: 2

    Coral reefs around the globe are threatened by the changing climate, particularly the ever-rising temperature of the oceans. As marine biologists, we normally document death, carrying out surveys on degraded reefs and quantifying the percentage of corals that have succumbed to "bleaching" (the breakdown of the anthozoan-dinoflagellate endosymbiosis upon which reefs are based) or disease. Although these data are critical for managing coral reefs, they come too late to benefit the resident corals. Ideally, we should instead seek to assess the health of corals before they display more visible, late-stage manifestations of severe health decline. Through a series of laboratory and field studies carried out over the past 20 years, we have now developed a better understanding of the cellular cascades involved in the coral stress response; this has resulted in a series of putative molecular biomarkers that could be used to assess reef corals health on a proactive, pre-death timescale. In this presentation, I will review progress in reef coral diagnostics and show how I have used JMP Pro to develop models with the predictive capacity to forecast which corals are most susceptible to environmental change. A similar approach for instead identifying resilient corals will also be presented.

  • Session ID: 2020-US-30MP-584

    3:00 p.m.

     

    Simulation, the Good, the Bad and the Ugly or Independence? Dependence, Synergistic, Antagonistic

    Ned Jones, Statistician, 1-alpha Solutions

    • Topic: Data Exploration
    • Level: 2

    Simulation has become a popular tool used to understand processes. In most cases the processes are assumed to be independent; however, many times this is not the case. A process can be viewed as physically independent, but this does not necessarily equate to stochastic independence. This is especially true when the processes are in series such that the output of a process is the input for the next process and so forth. Using the JMP simulator a simple series of processes are set up represented by JMP random functions. The process parameters are assumed to have a multivariate normal distribution. By modifying the correlation matrix, the effect of independence versus dependence is examined. These differences are shown by examining the tails of the resulting distributions. When the processes are dependent the effect of synergistic versus antagonistic process relationships are also investigated.

  • Session ID: 2020-US-45MP-607

    3:35 p.m.

     

    From Details on Demand to Wandering Workflows: Getting to Know JMP Hover Label Extensions

    Nascif Abousalh-Neto, JMP Principal Software Developer, SAS

    Lisa Grossman, JMP Associate Test Engineer, SAS

    • Topic: Data Visualization
    • Level: 2

    The JMP Hover Label extensions introduced in JMP 15 go beyond traditional details-on-demand functionality to enable exciting new possibilities. Until now, hover labels exposed a limited set of information derived from the current graph and the underlying visual element, with limited customization available through the use of label column properties. This presentation shows how the new extensions let users implement not only full hover label content customization but also new exploratory patterns and integration workflows. We will explore the high-level commands that support the effortless visual augmentation of hover labels by means of dynamic data visualization thumbnails, providing the starting point for exploratory workflows known as data drilling or drill down. We will then look into the underlying low-level infrastructure that allows power users to control and refine these new workflows using JMP Scripting Language extension points. We will see examples of "drill out" integrations with external systems as well as how to build an add-in that displays multiple images in a single hover label.

  • Session ID: 2020-US-30MP-585

    4:10 p.m.

     

    Images in Data Tables and related JSL Tools

    Heidi Hardner, Senior Staff Engineer, Seagate Technology

    Serkay Olmez, Senior Staff Data Scientist, Seagate Technology

    • Topic: JSL Application Development
    • Level: 2

    A Keras /TensorFlow deep learning project motivated a set of JSL Add-Ins for working with images inside JMP data tables. In turn, the new tools revealed widespread applicability of embedded images for our more conventional JMP analyses. JMP has had the ability to incorporate images in tables for some time but we had not fully utilized it. We will share the value we have found in embracing this feature as well as a suite of scripted tools to help unlock that value. The JSL tools include pulling images into a table, reviewing, marking up and measuring images as well as exporting them to PowerPoint or to a file structure with organization and properties based on other data columns.

2:00 - 5:00 p.m. ET

Discovery Expo

Who says online in real time can’t be as fun as in-person events? That’s a silly question. We know it’s not as fun. But we’re making the most of the online platform to create a very special Discovery Summit experience for you. Come to the Discovery Expo for Meet-the-Developer opportunities, Birds-of-a Feather sessions, opportunities to vote for your favorite papers and posters, talk to Tech Support, buy JMP gear, browse books, and more.

5:00 - 6:00 p.m. ET

Social: Users Group Members 

If you’re a member of a JMP Users Group, a leader of a Users Group, or someone who just wants to see what all the buzz is about, this social is for you. Pull up a chair, pour a glass of your favorite beverage, and move from group to group, or simply stay in one.

Friday, Oct. 16

11:50 a.m. - 2:55 p.m. ET

Paper Presentations

What’s the next best thing to watching a Discovery paper together in person? It’s watching it live online, where you can comment in real time, ask the presenters questions, and applaud the parts that make you go “wow.”

  • Session ID: 2020-US-45MP-593

    11:50 a.m.

     

    Using JMP to Compare Models from Various Environments

    Lucas Beverlin, Statistician, Intel Corp.

    • Topic: Predictive Modeling
    • Level: 2

    The Model Comparison platform is an excellent tool for comparing various models fit within JMP. However, it also has the ability to compare models fit from other software as well. In this presentation, we will use the Model Comparison platform to compare various models fit to the well-known Boston housing data set in JMP 15, Python, MATLAB, and R. Although JMP can interact with those environments, the Model Comparison platform can be used to analyze models fit from any software that can output its predictions.

  • Session ID: 2020-US-45MP-534

    12:25 p.m.

     

    Missing Random Effects in Machine Learning

    Fabio D'Ottaviano, R&D Statistician, Dow Inc.

    Wenzhao Yang, R&D Statistician, Dow Inc.

    • Topic: Predictive Modeling
    • Level: 3

    The large availability of undesigned data, a by-product of chemical industrial research and manufacturing, makes it attractive the venturesome use of machine learning for its plug-and-play appeal in attempt to extract value out of this data. Often this type of data does not only reflect the response to controlled variation but also to that caused by random effects. Thus, machine learning based models in this industry may easily miss active random effects out. This study shows by simulation the effect of missing a random effect via machine learning — versus including it properly via mixed models — for a set of experimental variables commonly encountered in the chemical industry and as a function of relative cluster size, total variance, proportion of variance attributed to the random effect and data size. Simulation was employed for it can provide visual explanation for non-statisticians/chemists. Besides the long-established fact that machine learning performs better the larger the data size, it was also observed that data lacking due specificity (i.e., clustring information) causes critical prediction biases regardless the data size — a point that can easily pass unseen to the data modeler.

  • Session ID: 2020-US-30MP-573

    1:00 p.m.

     

    Measurement Systems Analysis for Curve Data

    Astrid Ruck, Senior Specialist in Statistics, Autoliv

    Chris Gotwalt, JMP Director of Statistical Research and Development, SAS

    Laura Lancaster, JMP Principal Research Statistician Developer, SAS

    • Topic: Quality and Reliability
    • Level: 2

    Measurement Systems Analysis (MSA) is a measurement process consisting not only of the measurement system, equipment and parts, but also the operators, methods and techniques involved in the entire procedure of conducting the measurements. Automotive industry guidelines such as AIAG [1] or VDA [4], investigate a one-dimensional output per test, but they do not describe how to deal with data curves as output. In this presentation, we take a first step by showing how to perform a gauge repeatability and reproducibility (GRR) study using force versus distance output curves. The Functional Data Explorer (FDE) in JMP Pro is designed to analyze data that are functions such as measurement curves, as those which were used to perform this GRR study.

  • Session ID: 2020-US-30MP-591

    1:35 p.m.

     

    Creating a Reliability Modeling and Report Generation App using JSL + JMP Reliability Capabilities

    Shamgar McDowell, Senior Analytics and Reliability Engineer, GE Gas Power Engineering

    • Topic: Quality and Reliability
    • Level: 2

    Faced with the business need to reduce project cycle time and to standardize the process and outputs, the GE Gas Turbine Reliability Team turned to JMP for a solution. Using the JMP Scripting Language and JMP’s built-in Reliability and Survival platform, GE and a trusted third party created a tool to ingest previous model information and new empirical data which allows the user to interactively create updated reliability models and generate reports using standardized formats. The tool takes a task that would have previously taken days or weeks of manual data manipulation (in addition to tedious copying and pasting of images into PowerPoint) and allows a user to perform it in minutes. In addition to the time savings, the tool enables new team members to learn the modeling process faster and to focus less on data manipulation. The GE Gas Turbine Reliability Team continues to update and expand the capabilities of the tool based on business needs.

  • Session ID: 2020-US-30MP-567

    2:10 p.m.

     

    Heuristic Perspectives on Parametric Survival Analysis

    Thor Osborn, Principal Systems Research Analyst, Sandia National Laboratories

    • Topic: Predictive Modeling
    • Level: 2

    Parametric survival analysis is often used to characterize the probabilistic transitions of entities — people, plants, products, etc. — between clearly defined categorical states of being. Such analyses model duration-dependent processes as compact, continuous distributions, with corresponding transition probabilities for individual entities as functions of duration and effect variables. The most appropriate survival distribution for a data set is often unclear, however, because the underlying physical processes are poorly understood. In such cases a collection of common parametric survival distributions may be tried (e.g., the Lognormal, Weibull, Frechét and Loglogistic distributions) to identify the one that best fits the data. Applying a diverse set of options improves the likelihood of finding a model of adequate quality for many practical purposes, but this approach offers little insight into the processes governing the transition of interest. Each of the commonly used survival distributions is founded on a differentiating structural theme that may offer valuable perspective in framing appropriate questions and hypotheses for deeper investigation. This paper clarifies the fundamental mechanisms behind each of the more commonly used survival distributions, considering the heuristic value of each mechanism in relation to process inquiry and comprehension.

12:00 - 12:15 p.m. ET

Mindfulness Moment

Whether you’re just starting your day, breaking for lunch, or maybe even finishing your workday, a meditation session will get you ready for whatever comes next.

12:30 - 2:55 p.m. ET

Discovery Expo

It’s not every day that you can meet the JMP development team, hang out with like-minded data explorers from other industries, or buy discounted JMP gear and books. In fact, this is the last day for a long time that you can do all those things and more. 

1:30 - 2:55 p.m. ET

Poster Presentations

Discovery Summit Posters have always been about sharing research, starting conversations and discussing conclusions. This session is no different. Come ready to interact with our presenters.

3:00 - 4:00 p.m. ET

Plenary Session – Fireside Chat with Shankar Vedantam

Shankar Vedantam, Social Science Correspondent, NPR and host of Hidden Brain

Moderator: Jeff Perkinson, JMP Customer Care Manager, SAS

 

Discovery Summits aren’t just about visualizing data and talking statistics. Our Summits are also about exploring ideas, making connections and inspiring innovation. They’re about curiosity and engagement. Which is why we’ve asked the host of “Hidden Brain” podcast, Shankar Vedantam, to close the conference with a conversation about how we understand numbers, how we make decisions and how we persuade – and are persuaded by – others. Vedantam will tell real-world stories, share his research and answer your questions.

4:00 - 4:20 p.m. ET

Close the Flow

All good things must come to an end. Lucky for you, your adventures in data exploration can continue long after this conference closes. Join us for a quick wrap-up as we announce the most popular papers and posters, and let you know where to find lots of follow-up resources.

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