Agenda

Monday, Oct. 14

9:00 a.m. - 5:00 p.m.

Day 1 of 2

Pre-Conference Training

  • Murphey II

    Date/Time: Monday, Oct. 14, 9:00 a.m. - 5:00 p.m. – Tuesday, Oct. 15, 9:00 a.m. - 5:00 p.m.
    Location: Murphey II
    Instructor: Monica Beals, Analytical Training Consultant, SAS
    Conference Attendee Price: $975 | 975 PTS | 2.4 EPTO (25% off standard price)

    Course Description: This course offers a fresh perspective about designing experiments through state-of-the-art features in JMP. This course specifically focuses on the principles of designing an experiment and how to use all of them to achieve an optimal design.

    Prerequisites:

    • JMP Software: A Case Study Approach to Data Exploration
    • JMP Software: Statistical Decisions Using ANOVA and Regression

    Laptops will be provided.

  • Murphey III

    Date/Time: Monday, Oct. 14, 9:00 a.m. - 5:00 p.m. – Tuesday, Oct. 15, 9:00 a.m. - 5:00 p.m.
    Location: Murphey III
    Instructors: Mark Bailey and Di Michelson, Principal Analytical Training Consultants, SAS
    Conference Attendee Price: $975 | 975 PTS | 2.4 EPTO  (25% off standard price)

    Course Description: This course will help you recognize and model functional data. It will teach you to use the data as a response, such as the outcome for a designed experiment, or as new covariates or features, such as in a multivariate analysis. Functional data is defined as a function, profile, or curve. It is a series of observations over time or any other continuous variable. These data can be modeled so that changes in the shape can be associated with changes in other variables.

    Prerequisites:

    • JMP Software: A Case Study Approach to Data Exploration
    • JMP Software: Statistical Decisions Using ANOVA and Regression

    Laptops will be provided.

12:00 - 5:00 p.m.

Lobby Foyer

Early Registration

Tuesday, Oct. 15

8:00 a.m. - 5:00 p.m.

Lobby Foyer

Early Registration

8:30 - 10:00 a.m.

Pre-Conference Tutorials

  • Session ID: 2019-US-TUT-290

    Finger Rock I

    Essential Scripting for Efficiency and Reproducibility: Do Less to Do More

    Drew Foglia, JMP Distinguished Software Developer, SAS

    Evan McCorkle, JMP Software Developer, SAS

    • Topic: JSL Application Development
    • Level: 2

    From reproducing simple tasks to automating daily processes to sharing scripts with colleagues to deploying full applications across your organization, challenges exist at every level that can limit the efficiency and reliability of your JSL scripts. In this tutorial, we will travel along the arc from small scripts to large applications presenting best-practice techniques for mitigating many of the common, yet subtle, pitfalls that often hinder JSL novices and veterans alike. We will discuss strategies for combining multiple steps into a cohesive script, and conversely splitting overly large scripts into more manageable files. We will also present tips for handling errors, wrangling windows, isolating variables from unexpected changes, protecting the integrity of your scripts without compromising usefulness and more.

  • Session ID: 2019-US-TUT-291

    Finger Rock II

    Advanced Data Preparation: 10 Essential Tools in JMP® to Get From “Messy” to “Analysis Ready”

    Julian Parris, JMP Learning Strategy Manager, SAS

    • Topic: Data Access and Manipulation
    • Level: 2

    Rarely, if ever, do data come to us in an “analysis ready” format. Luckily, JMP has a rich and expansive set of tools that enable you to efficiently prepare your data for analysis. In this tutorial we explore 10 of the essential tools in JMP that help us get our data from “messy” to “analysis ready,” including methods for handling table restructuring and joining, computed and derived variables, outliers and influential points, recoding of variables, missing values and more. After we explore each of the 10 essential tools in depth and discuss best practices (and even some “off-label” uses for certain tools), we’ll work through three case studies where we will apply these tools in various ways to efficiently import, recode, restructure and reorganize complex and challenging data sets. Previous experience using JMP is highly recommended, though not strictly necessary.

  • Session ID: 2019-US-TUT-270

    Finger Rock III

    Which Model When?

    Ruth Hummel, JMP Academic Ambassador, SAS

    Mary Loveless, JMP Systems Engineer Manager, SAS

    • Topic: Data Exploration
    • Level: 2

    You have a business or research question, you’ve collected or found appropriate data, and you are ready to analyze. But which analytical methods should you try? And how will you choose a final model? In this talk, we will look at several data scenarios and present modeling options and a framework for comparison. We will look at how different questions or goals affect the modeling choices we make. (Predict? Explain? Find associations?) Models covered will include traditional regression, penalized regression, partial least squares and a few others. Comparison techniques will include residuals analysis, comparing fit statistics, and cross-validation or validation on new data.

9:00 a.m. - 5:00 p.m.

Day 2 of 2

Pre-Conference Training

  • Murphey II

    Date/Time: Monday, Oct. 14, 9:00 a.m. - 5:00 p.m. – Tuesday, Oct. 15, 9:00 a.m. - 5:00 p.m.
    Location: Murphey II
    Instructor: Monica Beals, Analytical Training Consultant, SAS
    Conference Attendee Price: $975 | 975 PTS | 2.4 EPTO (25% off standard price)

    Course Description: This course offers a fresh perspective about designing experiments through state-of-the-art features in JMP. This course specifically focuses on the principles of designing an experiment and how to use all of them to achieve an optimal design.

    Prerequisites:

    • JMP Software: A Case Study Approach to Data Exploration
    • JMP Software: Statistical Decisions Using ANOVA and Regression

    Laptops will be provided.

  • Murphey III

    Date/Time: Monday, Oct. 14, 9:00 a.m. - 5:00 p.m. – Tuesday, Oct. 15, 9:00 a.m. - 5:00 p.m.
    Location: Murphey III
    Instructors: Mark Bailey and Di Michelson, Principal Analytical Training Consultants, SAS
    Conference Attendee Price: $975 | 975 PTS | 2.4 EPTO  (25% off standard price)

    Course Description: This course will help you recognize and model functional data. It will teach you to use the data as a response, such as the outcome for a designed experiment, or as new covariates or features, such as in a multivariate analysis. Functional data is defined as a function, profile, or curve. It is a series of observations over time or any other continuous variable. These data can be modeled so that changes in the shape can be associated with changes in other variables.

    Prerequisites:

    • JMP Software: A Case Study Approach to Data Exploration
    • JMP Software: Statistical Decisions Using ANOVA and Regression

    Laptops will be provided.

9:00 a.m. - 12:00 p.m.

Choose one exam

Pre-Conference Certification

  • Murphey I

    Conference Attendee Price: $90 | 90 PTS | 0.3 EPTO (50% off standard price)

    Candidates will apply the skills and knowledge necessary to design and analyze industrial experiments.

    Exam Prep:

    • JMP Software: Custom Design of Experiments
    • JMP Software: Classic Design of Experiments
  • Murphey I

    Conference Attendee Price: $90 | 90 PTS | 0.3 EPTO (50% off standard price)

    Candidates will apply the skills and knowledge necessary to perform a variety of scripting and programming tasks in the JMP environment using the JMP Scripting Language.

    Exam Prep:

    • JMP Software: Introduction to the JMP Scripting Language
    • JMP Software: Designing and Building a Complete JMP Script
  • Murphey I

    Conference Attendee Price: $90 | 90 PTS | 0.3 EPTO (50% off standard price)

    Candidates will apply the skills and knowledge necessary to apply statistical thinking and fundamental statistical methods to solve industrial problems.

    Exam Prep:

    • Statistical Thinking for Industrial Problem Solving

10:30 a.m. - 12:00 p.m.

Pre-Conference Tutorials

  • Session ID: 2019-US-TUT-264

    Finger Rock I

    Before the Modeling: Feature Engineering in JMP®

    Jordan Hiller, JMP Senior Systems Engineer, SAS

    Mike Muhlada, JMP Senior Test Engineer, SAS

    • Topic: Data Access and Manipulation
    • Level: 2

    Successful predictive modeling projects require careful feature engineering. Both a science and an art, feature engineering involves gathering, summarizing, reshaping and transforming data into a form usable by modeling algorithms. This tutorial will describe some of the common activities in feature engineering and demonstrate JMP tools that can be used to perform them. There will be in-depth discussion and examples showing the use of formula columns for binning and transformation, the Recode platform for collapsing categories and Query Builder for summarization.

  • Session ID: 2019-US-TUT-292

    Finger Rock II

    Graph Builder: From Discovery to Presentation

    Bill Worley, JMP Senior Systems Engineer, SAS

    • Topic: Data Visualization
    • Level: 1

    Graph Builder is a jack-of-all-trades when it comes to visualizing your data. It is useful throughout the analysis process, from exploring your data to understand what you've got, to discovering relationships, to crafting the perfect graph to present your results. This session will show you how to use Graph Builder effectively with an overview of the graph elements available and the options for each one. It will also show you some tricks to help you become a master graph builder.

  • Session ID: 2019-US-TUT-293

    Finger Rock III

    The Most Flexible Modeling Platform That You're Not Using

    Clay Barker, JMP Principal Research Statistician Developer, SAS

    • Topic: Predictive Modeling
    • Level: 2

    Generalized Regression — it sounds scary, but it's not. This platform in JMP Pro can handle most common linear modeling exercises. Need to find the best set of predictors in a sea of possibilities (i.e., variable selection)? This platform can do that. Need to model a categorical response with more than two levels? Generalized Regression can do that. Have problems with non-normal responses like count data or yield percentage? Where other modeling methods fall down with these responses, Generalized Regression handles them with aplomb. If you're not using this platform, you're missing out. We will look at using Genreg to build models in a variety of settings: starting with an orthogonal designed experiment and moving to large observational data sets.

1:00 - 2:30 p.m.

Pre-Conference Tutorials

  • Session ID: 2019-US-TUT-246

    Finger Rock I

    Building Dashboards in JMP®

    Dan Schikore, JMP Principal Software Developer, SAS

    • Topic: JSL Application Development
    • Level: 2

    JMP platforms are often used in combination with one another to help guide a workflow or to provide complementary analysis and visualization of the data. The JMP Dashboard Builder can help you organize multiple reports in a single window and reproduce the set of reports using the same data table or a new table. Dashboard layout can be done automatically or through interactive drag-and-drop operations, and one report can optionally be used to filter others in the dashboard. Dashboards can be saved to a JMP data table to reproduce the reports with the same table, or they can be saved to an add-in to share the dashboard with colleagues and reproduce the same report on different data tables. When dashboards depend on the results of database queries, you have the choice to re-run the query each time, or to use a saved copy of the query. Dashboard results can also be saved to JMP Public or a private JMP Live server to share interactive reports with others via the web.

  • Session ID: 2019-US-TUT-287

    Finger Rock II

    The Fundamentals of Modern Experimentation Using JMP®

    Bradley Jones, JMP Distinguished Research Fellow, SAS

    • Topic: Design of Experiments
    • Level: 1

    Designed experimentation is the best way to learn about industrial processes. But, in the long history of the design of experiments, most experimenters learned methods that focused on conforming the experiment to classical designs. The need to understand these constraints limited the number of effective experimenters. JMP’s approach to DOE takes the burden of understanding the constraints of textbook designs off the practitioner, instead putting the experimenter in control. See how to use the Custom Designer to design an experiment for any process. The Custom Designer creates an experiment tailor-made for your situation. Do you have constraints on the input factors? No problem. Mixture factors along with process inputs? No problem. What about a run budget that limits the number of experiments you can perform? The Custom Designer will give you a design that will allow you to learn as much as possible within your budget. A few years ago Stu Hunter said that, "[the technology behind the DOE platforms in JMP] would change the way I teach DOE.” Come see how.

  • Session ID: 2019-US-TUT-193

    Finger Rock III

    Large-Scale Process Monitoring Using JMP®

    Laura Lancaster, JMP Principal Research Statistician Developer, SAS

    Jianfeng Ding, JMP Senior Research Statistician Developer, SAS

    • Topic: Quality and Reliability
    • Level: 2

    In this age of big data and complex manufacturing there is often an enormous amount of process data that needs to be monitored and analyzed to maintain or improve quality. JMP has several tools to help the analyst quickly and efficiently increase the scale of process monitoring. The Process Screening platform allows users to easily scan processes for stability and capability, enabling them to focus attention on processes needing improvement. The platform initially computes a summary report based on control chart, capability and stability calculations, and creates several graphs for quick visual assessment of process health. Based on these initial results, it is easy to select the processes needing attention and explore them more in depth with access to Control Chart Builder and Process Capability. The Model Driven Multivariate Control Chart (MDMCC) platform, new in JMP 15, allows users to monitor large amounts of highly correlated processes. This platform can be used in conjunction with the PCA and PLS platforms to monitor multivariate process variation over time, give advanced warnings of process shifts and suggest probable causes of process changes. We will use case studies to demonstrate how to use JMP to monitor and analyze many processes for fast and efficient improvement.

1:00 - 4:00 p.m.

Choose one exam

Pre-Conference Certification

  • Murphey I

    Conference Attendee Price: $90 | 90 PTS | 0.3 EPTO (50% off standard price)

    Candidates will apply the skills and knowledge necessary to design and analyze industrial experiments.

    Exam Prep:

    • JMP Software: Custom Design of Experiments
    • JMP Software: Classic Design of Experiments
  • Murphey I

    Conference Attendee Price: $90 | 90 PTS | 0.3 EPTO (50% off standard price)

    Candidates will apply the skills and knowledge necessary to perform a variety of scripting and programming tasks in the JMP environment using the JMP Scripting Language.

    Exam Prep:

    • JMP Software: Introduction to the JMP Scripting Language
    • JMP Software: Designing and Building a Complete JMP Script
  • Murphey I

    Conference Attendee Price: $90 | 90 PTS | 0.3 EPTO (50% off standard price)

    Candidates will apply the skills and knowledge necessary to apply statistical thinking and fundamental statistical methods to solve industrial problems.

    Exam Prep:

    • Statistical Thinking for Industrial Problem Solving

3:00 - 4:30 p.m.

Pre-Conference Tutorials

  • Session ID: 2019-US-TUT-295

    Finger Rock II

    Let's Talk Tables

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

    Mandy Chambers, JMP Principal Test Engineer, 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/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: 2019-US-TUT-289

    Finger Rock III

    Introduction to Functional Data Analysis

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

    Ryan Parker, JMP Senior Research Statistician, SAS

    • Topic: Predictive Modeling
    • Level: 3

    JMP Pro is now able to model and visualize functional data in a new way that is direct, straightforward and leads to highly accurate models. We will introduce the Functional Data Explorer platform and go through a set of examples that illustrate two particularly useful families of problems that it facilitates solving, function response DOE analysis (FR-DOE) and functional regressor machine learning (FR-ML). FR-DOE analysis allows one to see directly how the shape of a response curve changes as a result in changes in the value of the DOE factors. FR-ML is an approach to feature extraction for sensor data from manufacturing processes that faciliates accurate early prediction of final batch yield or faulty products. Our examples will be primarily from pharmaceutical, chemical and semiconductor manufacturing, but our approach can be applied to practically any industry.

5:00 - 5:30 p.m.

Sonoran

Steering Committee Meeting

6:00 - 9:00 p.m.

Main Pool

JMP Birthday Fiesta

Wednesday, Oct. 16

7:30 - 8:45 a.m.

AZuL, Arizona Deck and Arizona Foyer

Breakfast

7:30 - 9:00 a.m.

Lobby Foyer

Registration

8:00 - 8:30 a.m.

Lantana

Breakout Presenter Meeting

8:00 - 8:45 a.m.

Arizona Foyer

Discovery Expo

9:00 - 11:00 a.m.

Plenary Session

  • Grand Canyon

    JMP 15 and JMP Pro 15 give you more options for exploring and analyzing your data interactively. From enhanced data preparation and modeling to instant graphical gratification, you’ll discover new ways to access and understand your data. You’ll also see new ways to publish, share and communicate those findings.

11:00 - 11:30 a.m.

Arizona and Terrace Foyers

Break and Discovery Expo

11:30 a.m. - 12:00 p.m.

Breakout Sessions

  • Session ID: 2019-US-30MP-222

    Aster

    Using Text Explorer to Inform and Enhance Risk and Issue Application Development and Functionality

    Scarlett Marklin, Computer Scientist, Sandia National Laboratories

    Yvonne Petrova, Governance, Risk and Compliance Analyst, Sandia National Laboratories

    • Topic: Data Exploration
    • Level: 2

    The leadership of Sandia National Laboratories requested the development of a new “risks and issues” tool that would help users record their risk management activities with more consistency and accuracy than was possible with the existing corporate tool. To accomplish this goal, developers needed details about what each text field within the existing tool had captured from users during the previous seven years. JMP Text Explorer was used to classify and identify categories of issues, risks, actions and results using latent semantic analysis with SVD. Heavy emphasis on the use of regular expressions increased the efficiency of the data preprocessing stage, which enhanced the quality of the overall results and reduced preprocessing time. In the absence of a baseline for comparison, the accuracy of the text-derived categories was gauged line by line with input from subject matter experts. The analysis highlighted gaps and inconsistencies in the historical data and clarified misunderstandings about what should be captured by the tool, resulting in a clear path forward to configure the new risks and issues tool for higher-quality and more accurate data capture.

  • Session ID: 2019-US-30MP-216

    Indigo

    Using FDE and DOE to Help Build Predictive Models for Spectral Data

    Bill Worley, JMP Senior Global Enablement Engineer, SAS

    • Topic: Predictive Modeling
    • Level: 2

    Recently, partial least squares (PLS) has been used to build predictive models for spectral data. This session will show a newer approach using Functional Data Explorer (FDE) and covariate design of experiments (DOE) that will allow for fewer spectra to be used in the development of a good predictive model. This method uses one-fourth to one-third of the data that would otherwise be used to build a predictive model based on spectral data.

  • Session ID: 2019-US-30MP-248

    Verbena

    Enhancing and Extending the JMP® Process Screening Platform

    Vincent Faller, Senior Software Engineer, Predictum Inc.

    Karen Biagini, Statistician, KLA Corporation

    • Topic: JSL Application Development
    • Level: 2

    JMP add-ins are extremely practical for users who are required to perform a complex set of tasks in a routine manner. To help these types of users in a client project, we were challenged with extending and customizing the Process Screening platform in JMP to make it more comprehensive for KLA’s new product development application. The Process Screening platform was customized, allowing it to be both simplified in some respects and extended in others. The user is provided a fast, one-button launch to plot their selected analysis options, including drilling down to see the Levey-Jennings Control Chart, histograms with Cpk, and variability charts with custom P/T calculations. In addition to this custom drilldown view, KLA also has new visualizations to look for visual patterns across parameters in novel ways. A major implementation challenge in this project was to ensure that customized features were supported, while still maintaining JMP native functions such as automatic recalc for row exclusions.  This talk will explore the JMP add-in, its use and advantages from KLA’s viewpoint, as well as its design and implementation insights from the development viewpoint. 

  • Session ID: 2019-US-30MP-200

    Lantana

    The Design and Analysis of Experiments With 'Order' Factors

    Kevin Gallagher, Scientist, PPG Industries

    • Topic: Design of Experiments
    • Level: 2

    A quick look at the JMP Custom Design platform reveals that there are several types of factors (continuous, discrete numeric, categorical, blocking, covariate and mixture). However, little attention has been paid to a very important class of experimental factors: order. There are many situations in which the order that events happen has an influence on the outcome or response. This is especially true for formulated products such as paints, adhesives, foods, cosmetics, etc., for which the order-of-addition of the ingredients can often be the difference between a successful or unsuccessful product. There are many other situations in which one may want to study the influence of order – for example, the order in which glasses of wine are presented to judges in wine taste tests or the order in which straps are tightened when putting on a knee brace. This presentation will describe the concept of a “pairwise ordering factor” along with case study examples (development of an automotive paint formulation with premium appearance) that illustrate how to use JMP statistical software to both design and analyze experiments with order factors.

12:00 - 1:30 p.m.

Murphey II

JMP Users Group Luncheon

Limited seating. Please contact Robin Moran for details.

12:00 - 1:30 p.m.

AZuL, Arizona Deck and Arizona Foyer

Lunch and Discovery Expo

1:30 - 2:15 p.m.

Breakout Sessions

  • Session ID: 2019-US-45MP-273

    Aster

    An Introduction to Structural Equation Models in JMP® Pro 15

    Laura Castro-Schilo, JMP Research Statistician Developer, SAS

    • Topic: Data Exploration
    • Level: 1

    Structural Equation Models (SEM) is a new platform in JMP Pro 15 that offers numerous modeling tools. Confirmatory factor analysis, path analysis, measurement error models and latent growth curve models are just some of the possibilities. In this presentation, we provide a general introduction to SEM by describing what it is, the unique features it offers to analysts and researchers, how it is implemented in JMP Pro 15, and how it is applied in a variety of fields, including market and consumer research, engineering, education, health and others. We will use an empirical example – that everyone can relate to – to show how the SEM platform is used to explore relations across variables and test competing theories.

  • Session ID: 2019-US-45MP-211

    Indigo

    Using JMP® to Create a Self-Guided and Completely Contained Training Curriculum

    Jed Campbell, Quality Director, US Synthetic

    • Topic: JSL Application Development
    • Level: 2

    I plan to present and share a self-contained add-in that is a 15-lesson statistical problem solving course. The add-in relies moderately on scripts to do tasks like demonstrate concepts visually, call up data tables from the add-in folder, ensure that the user has the most up-to-date version of the add-in, save extra files to the add-in, and email the results of each session's homework. I will walk the audience through a simplified version of this add-in, then show them how to build each of the concepts listed here. The entire presentation will use JMP. I'm not an expert scripter, so I'll approach this from the perspective of a learner, sharing lessons I've learned along the way.

  • Session ID: 2019-US-45MP-213

    Verbena

    What to Do When Your Data Is a Curve

    Sam Gardner, Principal Research Scientist, Elanco Animal Health

    • Topic: Predictive Modeling
    • Level: 3

    In many situations in pharmaceutical product development, the most relevant data is a value that is a function of time (a curve). Traditional approaches to handling curves often ignore the full time dependency of the result and only focus on one aspect of the curve. This often results in a loss of information. Three analysis approaches that utilize the entire curve will be discussed: 1) When data is aligned in time, multivariate methods can be used to characterize the data; 2) When data can be described by a parameterized model, fitting that model to each curve results in model parameters that become the new data; and 3) When the data in the curve is not time aligned, or if it is too complex to describe with a parameterized model, then the Functional Data Explorer can be used to fit a smoothing model to each curve, and again the parameters of that smooth curve fit become the new data. DOE can be combined with these methods to understand how the curves depend on the DOE factors. This talk will show how to use JMP to apply each of these approaches, combined with DOE, using real examples from pharmaceutical product development.

  • Session ID: 2019-US-45MP-284

    Lantana

    Use Cases of Repairable Systems Simulation Platform

    Peng Liu, JMP Principal Research Statistician Developer, SAS

    Leo Wright, JMP Principal Product Manager (Retired), SAS

    • Topic: Quality and Reliability
    • Level: 2

    Repairable Systems Simulation (RSS) is a new addition to JMP capability in reliability engineering. RSS is designed to solve simple to very sophisticated problems. The innovative graphical user interface of the platform offers engineers great freedom in expressing complex maintenance strategies. The presenter will first demonstrate the basics of using the platform to help the audience become familiar with the tool and its concept. The presentation will walk through several use cases to illustrate the capability of the platform. Use cases will demonstrate the usefulness in a variety of applications, including identifying critical components to the system reliability, computing reliability of large non-repairable systems, comparing costs of different maintenance strategies, and changing component state to mimic environment impact to system reliability, etc.

2:30 - 3:00 p.m.

Breakout Sessions

  • Session ID: 2019-US-30MP-182

    Aster

    A Visual Wafer to Mask Coordinate Converter Application

    John Como, Process Engineer, Intel

    • Topic: JSL Application Development
    • Level: 2

    In semiconductor manufacturing, masks are used to imprint patterns onto wafers in the lithography process. The pattern dimensions on the six-inch masks are four times larger than the pattern printed on the 12-inch wafers, so multiple dies are printed onto a single wafer. A single defect on the mask pattern can cause a repeating bad die and cost the manufacturing company millions, so it is imperative that mask defects be found and characterized. Often, the mask inspection machines use a different coordinate system than the wafer inspection machines, and the linear distances on the mask are four times longer than distances on the wafers. If a defect is found on the mask, then a tedious conversion from mask to wafer coordinates must be done in order to verify if there is an effect. This interactive application written in JSL makes it simple to convert coordinates from one system to another, and even creates an image of the mask, wafer and the location of the defect for easy comparison to real-world data. This presentation will cover the mathematics behind the conversions and showcase how JSL was used to create an application to simplify the calculation.

  • Session ID: 2019-US-30MP-185

    Indigo

    It All Started With a Simple Request

    Jim Grayson, Professor, Augusta University

    • Topic: Predictive Modeling
    • Level: 1

    It all started with this request: "Can you help us determine why there are so many no-shows at our doctor’s appointments?" In response, using JMP predictive modeling and after many iterations, we developed a beautiful predictive model to identify the characteristics of the no-shows. Although we were able to use our expertise to answer the initial question, once we “solved the problem” we realized that the true need was not to develop a wonderful model. Rather, we needed to use what we had learned to create interventions that would lead to addressing the true goal: getting patients to the clinic for their visits! We developed a simple designed experiment to study potential interventions to address the insights from the initial predictive model on the no-show population. Results show significant improvements for two of the three interventions. We are reminded that there is a beginning, a middle and an end. We get the most excited about the middle – that is the modeling. But what makes or breaks us are the beginning and end – the beginning in understanding the true goal, and the end when we deploy the results that meet the desired outcome. We will discuss this entire process, from starting with a primarily analytic goal to pivoting to a business-focused goal that went full cycle from problem statement to deployment.

  • Session ID: 2019-US-30MP-261

    Verbena

    See Fer Yer Sen: The Importance of Data Exploration

    Phil Kay, JMP Senior Systems Engineer, SAS

    • Topic: Data Exploration
    • Level: 1

    People and organizations make expensive mistakes when they fail to explore their data. Decision makers cause untold damage through ignorance of statistical effects when they limit their analysis to simple summary tables. In this presentation you will hear how one charity wasted billions of dollars in this way. You will learn how you can easily avoid these traps by looking at your data from many angles. An example from media reports on "best places to live" will show why you need to look beyond headline results. And how simple visual exploration – interactive maps, trends and bubble plots – gives a richer understanding. All this will be presented entirely through JMP Public.

  • Session ID: 2019-US-30MP-235

    Lantana

    Effectively Applying Team Foreknowledge With the Power of JMP® to Design More Effective DOEs

    Wayne Levin, President, Predictum

    Cy Wegman, President, SY64

    • Topic: Design of Experiments
    • Level: 1

    As Alexander Graham Bell said, “Before anything else, preparation is the key to success.” As such, well-planned DOEs are vital. Limited budgets and time often force us to reduce the number of factors we can test. Often it is difficult to identify the most important factors to consider from all the candidates. Planning should include the collection of assumptions, conflicting views and known unknowns from among the subject matter experts. Because of the labor involved, any planning effort should carry forward to support subsequent DOEs. We will introduce a component of our DOE training that focuses on the Plan stage of the Plan-Do-Study-Act cycle. This features a capability – assisted by JMP – for independently collecting views that are then considered within the group. The result is a summarization of available knowledge in a format that is easily understood by all team members. This will greatly streamline the decision process in identifying the most important factors and thereby lead to a more effective DOE.

3:00 - 3:30 p.m.

Arizona and Terrace Foyers

Break and Discovery Expo

3:30 - 4:00 p.m.

Breakout Sessions

  • Session ID: 2019-US-30MP-161

    Aster

    Getting More Out of Data Competition Results With Pareto Fronts

    Christine Anderson-Cook, Scientist, Los Alamos National Laboratory

    Sarah Burke, Statistician, The Perduco Group

    Lu Lu, Assistant Professor, University of South Florida

    • Topic: Data Exploration
    • Level: 1

    Data competitions have attracted considerable attention among the world’s community of data and analytics scientists, as well as discipline-specific subject matter experts. Their broad involvement provides a model of crowdsourcing for business and government to solve tough, high-impact problems in a cost-effective way. Typically winners are determined through a leaderboard formula that needs to be static throughout the competition, with fixed rewards and penalties for patterns of correct and incorrect responses for different aspects of the solution. However, for different uses of the solution, these aspects might be more or less important. By using the existing capability for constructing flexible high-dimensional Pareto fronts in JMP, it is possible to explore and identify various solutions with their strengths and weaknesses. Pareto fronts allow the user to identify all the objectively superior solutions across all possible weightings of the different elements of the solution, and discard non-competitive solutions. The approach to using multiple Pareto fronts to highlight different "best" solutions will be demonstrated through a recently completed data competition focused on detecting, identifying and locating radioactive sources in an urban environment (https://www.topcoder.com/lp/detect-radiation).

  • Session ID: 2019-US-30MP-256

    Indigo

    Determining Behavioral Patterns Associated With Success as a Dual-Purpose Police K9

    Jordan Gillespie, Graduate Student, Auburn University

    Steve Figard, Research Director and Professor in the Department of Biology, Bob Jones University

    Jennifer L. Essler, Postdoctoral Research Fellow, Penn Vet Working Dog Center

    Cynthia M. Otto, Professor of Working Dog Sciences and Sports Medicine, University of Pennsylvania

    • Topic: Predictive Modeling
    • Level: 2

    Many studies have evaluated differences and consistencies in the behaviors of various dog breeds throughout early development. These behavioral examinations carry significant importance in working dog training and research as they can be utilized to determine the future potential of young dogs in different careers. This study focuses on a retrospective analysis of a behavioral test adapted by the Penn Vet Working Dog Center that evaluates the environmental soundness and hunt and toy drive of future working dogs as puppies (n=45) throughout training to determine behavioral patterns indicative of dogs that successfully complete K9 police training. The test consisted of multiple evaluations performed at three, six, nine and 12 months of age. Stress, toy engagement and hunting (for toy) behaviors were analyzed. Analyses of video recordings of the test allowed for determination of behavioral trends indicative of success in K9 police work using nominal logistic regression. The Prediction Profiler in JMP was the primary tool employed in evaluating the test results and will be demonstrated in this presentation.

  • Session ID: 2019-US-30MP-231

    Verbena

    Below the Martian Surface: Using JMP® to Improve Rock Abrasion & Dust Removal on the Mars 2020 Rover

    Iona Brockie, Mechatronics Engineer, Jet Propulsion Laboratory

    Kristopher Kriechbaum, Mechatronics Engineer, Caltech/Jet Propulsion Laboratory

    • Topic: Design of Experiments
    • Level: 1

    The Mars 2020 Rover will use new cameras and spectrometers to continue the search for evidence of past life on Mars. However, these instruments will reveal the most information if they can gain access to what is below the weathered outer layer of Martian rock. To expose what is underneath, the rover will carry a rotary percussive drill and pressurized gas. JMP was used to determine what drill, gas and rock parameters were most critical for abrading and dust removal performance. Design of experiments provided a backbone for a test plan that appropriately spanned the design space within a manageable number of tests. The data analytics and visualization in JMP helped cut through the noise inherent to working with the inconsistencies of real rock to reveal important conclusions about the most effective use of the tool. The results from this testing and analysis will allow improved in-situ science to occur on this mission.

  • Session ID: 2019-US-30MP-267

    Lantana

    Integration of JMP® With PowerPoint Using Scripting and VBA

    Martin Kane, Managing Scientist, Exponent

    • Topic: JSL Application Development
    • Level: 2

    JMP has long been used as a powerful tool for data exploration and analysis. The JMP scripting language (JSL) can extend the capabilities of JMP through programming tools, which enable the user to write a series of sequential steps for JMP to run. Users often want or need to convey the output from these scripts to co-workers, customers or others. MS PowerPoint is a common tool for sharing information, and it too has a back-end scripting language called Visual Basic for Applications (VBA). Using JSL, analyses and graphics can be saved as pictures, which can then be brought into PowerPoint through some simple VBA scripting. This talk will explain and demonstrate how this has been accomplished.

4:15 - 4:45 p.m.

Breakout Sessions

  • Session ID: 2019-US-30MP-199

    Aster

    Improving Gas Chromatography/Vacuum Ultraviolet Spectroscopy (GC/VUV) for Forensic Science Using JMP®

    Ashur Rael, Graduate Assistant, Indiana University-Purdue University Indianapolis

    John V. Goodpaster, Associate Professor, Department of Chemistry and Chemical Biology; Associate Director, Forensic Sciences Program at IUPUI, Indiana University-Purdue University Indianapolis

    Magnus Rydberg, Undergraduate Assistant, Indiana University-Purdue University Indianapolis

    Courtney Cruse, Graduate Assistant, Indiana University-Purdue University Indianapolis

    Zackery Roberson, Graduate Assistant, Indiana University-Purdue University Indianapolis

    • Topic: Data Exploration
    • Level: 2

    In the simplest of terms, a forensic chemist seeks to accomplish two tasks: 1) the identification of an unknown sample, and 2) the comparison of an unknown sample to a known exemplar. Instrumental methods lie at the heart of the modern forensic examination of ignitable liquids, controlled substances, explosives and other forms of physical evidence. The search for instrumentation that offers the requisite sensitivity, selectivity and specificity for analytes does not end, even when established methods exist. Knowing the strengths and weaknesses of established methodologies is crucial for forensic chemists. To that end, we apply JMP to both method development and spectral analysis. GC/VUV is a recent technique with great promise that we are currently exploring. Developing methods one factor at time is time-consuming and difficult to manage when multiple responses are important. Using JMP for design of experiments methods such as factorials and response surfaces allows simultaneous optimization of multiple experimental conditions while considering multiple responses. Understanding of the spectral data is difficult or impossible using visual or direct analysis. Using JMP for clustering and multivariate analysis readily elucidates the underlying spectral characteristics that drive differentiation and provides quantification of the method's ability to positively identify a compound.

  • Session ID: 2019-US-30MP-243

    Indigo

    Leveraging the Array of Data Structure Tools in JMP® to Solve Survey Data File Problems

    Amy Phillips, Principal Scientist, Procter & Gamble

    Tracy Desch, Scientist, Procter & Gamble

    Scott Reese, Senior Scientist, Procter & Gamble

    • Topic: Data Access and Manipulation
    • Level: 1

    Survey data files can be very messy. As more and more of Procter & Gamble’s researchers turn to do-it-yourself online survey platforms like Qualtrics and AYTM, there is more of a need for individuals to clean and restructure their own data sets to get the most of their analyses. JMP provides a variety of tools that enable users to create new variables and tackle any data structure challenge. This talk will share real-world survey data files and how we used tools like recode, Tables, Stack, formulas and virtual join to help our users solve their data problem and get on with their analysis.

  • Session ID: 2019-US-30MP-177

    Verbena

    Custom Measurement System Design and Qualification: A Case Study

    Stephen Czupryna, Quality Process Engineer, Samson Rope Technologies

    Canh Khong, Certified Quality Technician II, Samson Rope Technologies

    • Topic: Quality and Reliability
    • Level: 2

    Like high-volume manufacturers, specialty manufacturers need to measure important quality characteristics of their products. However, they often discover that off-the-shelf measurement systems, many of them designed for high-volume purposes, do not meet their needs. When this happens, they have no choice but to design and qualify their own measurement system. This case study outlines the development of a custom measurement system by a diverse team of people at Samson Rope Technologies, a high-performance rope manufacturer. Samson needed the system to measure the tensile strength of twisted UHMWPE fiber bundles used as sub-units in demanding ship mooring, tug and other rope applications. Samson faced two fundamental measurement challenges: (1) The sub-units are extraordinarily strong with multi-ton break strengths, and (2) the sub-units are intrinsically slippery and difficult to grip. Unfortunately, readily available tensile testing grips sold by instrument manufacturers didn't provide acceptable results. This left Samson Rope with only one choice – in-house custom grip development. Attendees will learn about the process approach taken by the development team and how JMP dramatically improved their creative thinking process. The first step was to use fundamental engineering principles and the wisdom of colleagues to identify controllable factors and safe experimental ranges. The factors and ranges were used in a definitive screening experiment to identify key main effects and, with augmentation, to create a useful predictive model of the measurement process. Attendees will also learn how the team followed the grip design optimization with iterative MSA to fine-tune the testing procedure and improve the system's signal-to-noise ratio. In summary, the case study is yet another demonstration of the philosophical underpinnings of statistical thinking – to treat all work (including measurements!) as a process, that all processes vary and that the key to success is to understand and reduce variation.

  • Session ID: 2019-US-30MP-274

    Lantana

    The Role of Perception in Statistics-Based Decisions

    Bryan Fricke, JMP Principal Software Developer, SAS

    • Topic: Data Visualization
    • Level: 1

    JMP is a powerful tool for generating statistical reports for evaluation by decision makers. However, when it comes to preparing reports, accuracy and comprehensibility are only part of the story. For example, Amos Tversky and Daniel Kahneman have suggested that presenting results in terms of a potential loss can have about twice the psychological impact as an equivalent gain. In this session, we will explore the role perception plays in statistics-based decisions and how knowledge of that role should inform JMP users with respect to generating reports for decision makers.

6:00 - 9:00 p.m.

Catalina Basin

Tucson Food Truck Rodeo

Thursday, Oct. 17

7:30 - 8:45 a.m.

AZuL, Arizona Deck and Arizona Foyer

Breakfast

7:45 - 8:45 a.m.

Murphey II

Government Networking Breakfast

Limited seating. Please contact Robyn Godfrey for details.

8:00 - 8:45 a.m.

Arizona Foyer

Discovery Expo

9:00 - 10:30 a.m.

Plenary Session

  • Grand Canyon

    Never Stop Learning: Why We Don’t Learn and What We Can Do About It

    Bradley Staats, Kenan-Flagler Business School, University of North Carolina

    What do Frodo Baggins, Luke Skywalker and you have in common? A dark side. Our worst enemy often comes from within and must be confronted. Otherwise blind spots, weaknesses and biases will keep us from reaching success. How do we overcome this inner enemy in business? Though we don’t have magic rings or lightsabers, we can confront our natural biases to unleash the power of learning. In this world, the best learners will win because continuous improvement makes our companies nimble, responsive and strong in the face of lightning-fast change. But first, we have to get out of our own way. This discussion will reveal how a few deeply ingrained human tendencies can interfere with learning and how to overcome them.

    You will discover:

    • The hard-wired traps that prevent learning.
    • Ways to learn faster and more effectively so you can stay relevant, innovate and thrive.
    • How to unleash the power of learning throughout your organization.

10:30 - 11:00 a.m.

Arizona and Terrace Foyers

Break and Discovery Expo

11:00 - 11:45 a.m.

Breakout Sessions

  • Session ID: 2019-US-45MP-277

    Aster

    AI Image Analysis With Real-Time Process Reporting

    Byron Wingerd, JMP Senior Systems Engineer, SAS

    Brian Garrett, Principal Domain Specialist, Global Technology Practice, SAS

    • Topic: JSL Application Development
    • Level: 2

    Artificial intelligence (AI) is a fun buzz word to throw around, but really what can it do for you in practical terms? This talk is for those who want to use AI techniques in analysis to leverage JMP skills and make results available on the web. This presentation features a working inspection line with an AI image capture model that generates data and insights in real time, published to JMP Live. In this presentation we will demonstrate how to leverage new features in JMP 15 to integrate with SAS Viya, which provides analysts with a wide range of SAS analytic services at an enterprise scale. JMP integration with SAS Cloud Analytic Services (CAS) in SAS Viya provides a mechanism to explore, visualize and publish interactive reports to a JMP Live server. Combing the benefits of the SAS CAS, JMP and JMP Live platforms gives users access to a unique set of processing, modeling, scoring and reporting tools. You will learn about new tools in JMP 15 and how to: 1) integrate with SAS® Viya® and perform SAS CAS actions from JMP; 2) analyze and visualize process data in JMP; 3) build and publish reports to JMP Live.

  • Session ID: 2019-US-45MP-218

    Indigo

    Anomaly Detection and JMP® Pro

    Michael Crotty, JMP Senior Statistical Writer, SAS

    Colleen McKendry, JMP Technical Writer, SAS

    Marie Gaudard, Statistical Consultant

    • Topic: Predictive Modeling
    • Level: 3

    In situations where anomaly detection is the goal of a predictive model, the underlying data often exhibit an imbalanced class distribution. Namely, the anomalous class is significantly smaller than the nonanomalous class. The modeling goal is usually to identify members of the minority class. However, a straightforward application of predictive modeling techniques can result in a biased and inaccurate model. Many techniques have been proposed to address these issues. We seek to guide JMP Pro users in developing predictive models for imbalanced data. We address JMP Pro approaches to classification into an underrepresented class. We first describe general aspects of the imbalanced class problem: bias, performance measures and approaches to addressing the modeling issues. We then discuss the sampling methods we use in our study; these include weighting, undersampling, oversampling, the synthetic minority oversampling technique (SMOTE) and Tomek links. For several real data sets that exhibit varying class proportions, we compare the fits obtained using these sampling methods in combination with predictive models available in JMP Pro classification platforms. We perform a similar exploration of sampling techniques and predictive models for a limited range of simulated data sets. For the simulated data sets, we attempt to identify the degree of underrepresentation for which standard models begin to be affected by class imbalance. We also present conclusions about the relative performance of the sampling methods and predictive models.

  • Session ID: 2019-US-45MP-272

    Verbena

    Fitting Custom Distributions to Data Using JMP®

    Matthew Flynn, Senior Data Scientist, GM

    Mary Loveless, JMP Systems Engineer Manager, SAS

    • Topic: Data Exploration
    • Level: 3

    Not everything in the statistical world is normally distributed. For example, JMP includes the standard Poisson, Gamma Poisson (or Negative Binomial), Binomial and Bet Binomial distributions for fits for discrete data. However, the Nonlinear platform enables the fitting of your choice of distribution. The right “fit” is important for style, but it is also important in analytics to most accurately summarize one’s data as concisely and accurately as possible. The JMP Distribution platform includes both commonly used discrete fit and continuous distributions. On occasion, we have data with more unusual characteristics that require different distribution fits. How would you go about fitting and plotting new, custom distribution to this type of data? In this presentation, we will show how JMP makes it easy to work with alternative distributions, such as Simplex, L-Logistic and Kumaraswamy distributions for bounded responses; or alternative count data distributions, such as COM-Poisson, Consul, Double Poisson and a large number of discretized continuous distributions like the discrete Weibull or discrete Lindley distributions.

  • Session ID: 2019-US-45MP-198

    Lantana

    Extracting Valuable Practical Information From Experimental Models Created for Quality by Design

    Rob Lievense, JMP Senior Systems Engineer, SAS

    • Topic: Design of Experiments
    • Level: 2

    JMP DOE models can be used dynamically to provide stakeholders with reliable estimates of the quality performance for new products. The design and execution of structured, multivariate experiments allow scientists and engineers to efficiently define a robust design space for pharmaceutical and medical device manufacturing. Models created through the DOE platform in JMP are used to determine settings of the critical process parameters (CPPs) that ensure a robust process to make products that meet the requirements for Critical Quality Attributes (CQAs). Regulatory submissions that include such QbD elements demonstrate that risks have been mitigated; however, a great deal of practical information can be extracted with simulations of the model. This presentation utilizes the experimental model with historical information and subject matter expertise to project the likely operational performance of a product. The DOE model prediction profiler is used with the simulator to dynamically predict a population of future results with patterns of real-world variation included in the inputs. This dynamic modeling is an excellent tool for setting manufacturing card limits determining a manufacturing control space defined with estimates of the percentage defects. The analyses allow for the inclusion of the measurement uncertainty as an added noise factor for the response.

11:45 a.m. - 1:00 p.m.

AZuL, Arizona Deck and Arizona Foyer

Lunch and Discovery Expo

11:45 a.m. - 1:00 p.m.

Arizona Foyer

Book Signing With Bradley Staats

11:45 a.m - 1:00 p.m.

Murphey II

Academic-Industry Network Lunch

Limited seating. Please contact Curt Hinrichs for details.

1:00 - 1:30 p.m.

Breakout Sessions

  • Session ID: 2019-US-30MP-244

    Aster

    Text Curation Example Using Genreg Platform

    Scott Reese, Senior Scientist, Procter & Gamble

    Amy Phillips, Principal Scientist, Procter & Gamble

    Tracy Desch, Scientist, Procter & Gamble

    A. Narayanan, Principal Scientist, Procter & Gamble

    • Topic: Data Exploration
    • Level: 2

    Higher quality text curation will result in higher-quality insights from unstructured text (the same as with all other analysis). This talk will focus on a few examples of how to be more efficient with your text cleanup. Examples will include: a recoding demonstration, using Genreg to identify stop words, finding the right number of topics and using Genreg approaches to focus on key topics.

  • Session ID: 2019-US-30MP-187

    Indigo

    Effective Communication and Visualization With Life Sciences Data

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

    • Topic: Data Visualization
    • Level: 1

    Conducting translational and clinical research comes with a big price tag. Given costs and time considerations in studies for understanding health outcomes and disease, researchers commonly collect as much data on as many endpoints as possible. This is further motivated by our limited understanding of the genomic underpinnings of disease and good clinical research practice protocols to assess not only efficacy of new therapeutics, but safety and operational integrity. In the life science data “life cycle,” the data volume poses challenges to analyze and communicate results; traditional practices that produce hundreds of tables are neither efficient nor effective. This presentation will focus on visualization to communicate results in several case study analyses typical of life science research. We highlight both data summary visualization techniques and graphs to communicate the results of complex statistical analyses. Examples include volcano plots and Manhattan plots when performing thousands to millions of statistical models with genomic data, distributional summaries of safety and efficacy in clinical trials, laboratory findings trends (waterfall plots, shift plots, spaghetti plots, lasagna plots, swimmer plots) and clinical operational integrity anomaly detection. All examples will be presented with Graph Builder, showcasing the strength and flexibility of this key JMP platform.

  • Session ID: 2019-US-30MP-278

    Verbena

    Establishing Equivalence for Practical Applications

    James Wisnowski, Principal Consultant, Adsurgo

    Andrew Karl, Senior Management Consultant, Adsurgo

    • Topic: Predictive Modeling
    • Level: 2

    Standard hypothesis tests are set up to prove differences from a standard or between populations. The goal in many applications is to demonstrate with statistical confidence that, in fact, there is no practical difference. Common examples include performance between model and simulation results versus live events, generic versus brand drug safety and efficacy, material engineering properties between different treatments, and many other problems the JMP community faces daily. While several platforms have the very well-named option Test Equivalence that uses the Two One-Sided Tests (TOST), in practice we often require more than equal means to properly characterize the similarity. We will demonstrate the capabilities in JMP to help establish equivalence in means, variances and distributions as an introduction. The focus of the talk will be on the equality between models; that is, not only the output responses being approximately equal, but also the consistency in the parameters characterizing the process. We will extend the custom estimation in JMP for regression models with Hotelling's T2 test statistic and show how the Functional Data Explorer can be used to decompose complex curves, followed by implementing standard equivalence methods for comparison.

  • Session ID: 2019-US-30MP-178

    Lantana

    New Features in JMP® 15 Control Chart Builder

    Annie Dudley Zangi, JMP Senior Research Statistician Developer, SAS

    • Topic: Quality and Reliability
    • Level: 2

    In this session, we will explore the new features in Control Chart Builder for JMP 15. As Control Chart Builder continues to grow and improve, JMP 15 brings many of the less common features in the older Control Chart platform into the flexible Control Chart Builder. With box plots, you can now see the shape of your data on an XBar or three-way chart. Along with the new default dispersion chart preference, we will also explore when the new Median Moving Range chart is warranted. We will investigate the alarm script feature, which enables remote real-time monitoring and is new to Control Chart Builder in JMP 15, along with new limits options, both importing and exporting. These new features and many others can serve to make your control charts more dynamic, illuminating and effective.

1:45 - 2:30 p.m.

Breakout Sessions

  • Session ID: 2019-US-45MP-210

    Aster

    JMP® Interactive HTML and JMP® Live: What's New and Different in JMP® 15

    Heman Robinson, JMP Principal Software Developer, SAS

    • Topic: Data Visualization
    • Level: 1

    Interactive HTML in JMP can be used either by itself or as part of JMP Live. By itself, Interactive HTML enables JMP users to share reports with dynamic graphs – but JMP Live enables more. This talk explains the differences between Interactive HTML when used alone and as part of JMP Live by demonstrating the new features of JMP 15.

  • Session ID: 2019-US-45MP-183

    Indigo

    Not Quite Normal: Choosing the Best Distribution for Modeling Your Response

    Clay Barker, JMP Principal Research Statistician Developer, SAS

    • Topic: Predictive Modeling
    • Level: 2

    The Generalized Regression platform in JMP Pro is a flexible and powerful tool for building regression models. While we often talk about the variable selection methods built into the platform, we cannot forget about the ability to specify the distribution of the response. Do we have count data or a skewed response? Or is the normal distribution sufficient? In this talk, we will give a brief overview of generalized linear models and then provide some strategies for choosing the best distribution for your response in the Generalized Regression platform.

  • Session ID: 2019-US-45MP-160

    Verbena

    A Practical Road Map for Implementing Industry 4.0 Using Custom SQL Databases and JMP® 15

    Joseph Beauchemin Jr., Director of Quality (Non-Ferrous), Hitchiner Manufacturing

    Philip Ramsey, Principal Lecturer, University of New Hampshire; and Owner, North Haven Group

    • Topic: Data Access and Manipulation
    • Level: 2

    Industry 4.0 (I4.0) is an initiative to transition organizations to the digital revolution and guide the creation of smart factories. Despite much hype, there is confusion as to what I4.0 is. Actually, I4.0 encompasses a number of parallel initiatives and is broader in scope than manufacturing. Despite confusion about I4.0, one consequence is the proliferation of large amounts of data within organizations. Few organizations have a coherent strategy to deal with the data deluge and lack the skills to analyze the data. We present a framework to manage the many data streams resulting from I4.0 and propose standardized workflows for analytics on the data to drive process improvement utilizing Six Sigma tools. We propose JMP statistical software as a solution for data analyses and intranet reports. Standard Work and JMP are used to: identify data requirements and collection strategies; create custom SQL databases; perform standardized analyses in JMP via custom scripts; and create dashboards with JMP for management reviews. The range of analytical capabilities and scriptability of JMP make it key to Standard Work. The JMP Query Builder is essential in accessing SQL databases that link all of the relevant data sources while automatically integrating various JMP data analyses. Using a case study we show how an advanced manufacturing company reduced the time spent on formatting, importing and cleaning up data from 90% to less than 10% of project time. With Standard Work, the company went from near 0% to greater than 95% yields on many products and became a preferred supplier for key customers. In this talk, we will discuss Standard Work and demonstrate live how JMP is central to its successful implementation using a case study. Standard Work requires an educated workforce, and the JMP online course Statistical Thinking for Industrial Problem Solving provides a free solution.

  • Session ID: 2019-US-45MP-195

    Lantana

    Recent Developments in JMP® Quality and SPC

    Laura Lancaster, JMP Principal Research Statistician Developer, SAS

    Jianfeng Ding, JMP Senior Research Statistician Developer, SAS

    Annie Dudley Zangi, JMP Senior Research Statistician Developer, SAS

    • Topic: Quality and Reliability
    • Level: 2

    JMP has several new quality platforms and features – modernized process capability in Distribution, CUSUM Control Chart and Model Driven Multivariate Control Chart – that make quality analysis easier and more effective than ever. The long-standing Distribution platform has been updated for JMP 15 with a more modern and feature-rich process capability report that now matches the capability reports in Process Capability and Control Chart Builder. We will demonstrate how the new process capability features in Distribution make capability analysis easier with an integrated process improvement approach. The CUSUM Control Chart platform was designed to help users detect small shifts in their process over time, such as gradual drift, where Shewhart charts can be less effective. We will demonstrate how to use the CUSUM Control Chart platform and use average run length to assess the chart performance. The Model Driven Multivariate Control Chart (MDMCC) platform, new in JMP 15, was designed for users who monitor large amounts of highly correlated process variables. We will demonstrate how MDMCC can be used in conjunction with the PCA and PLS platforms to monitor multivariate process variation over time, give advanced warnings of process shifts and suggest probable causes of process changes.

2:45 - 3:30 p.m.

Breakout Sessions

  • Session ID: 2019-US-45MP-184

    Aster

    Automated Report Creation: From Data Import to Publication

    Brian Corcoran, JMP Director of Research and Development, Host Group, SAS

    • Topic: Data Access and Manipulation
    • Level: 2

    JMP is a powerful analysis and visualization tool, but it is often only part of the workflow. Data can reside in a database or on the web. The consumers of your report may not have JMP, and may not want to know the details of producing the end result. Further, you may need to have this task done every day, automatically. This talk looks at an end-to-end automating of the creation of a dashboard using JMP Query Builder, JSL and Graph Builder. This dashboard will then be published to JMP Public with help from the operating system task scheduling mechanism.

  • Session ID: 2019-US-45MP-212

    Indigo

    Using JMP® Functional Data Explorer in Predicting the Performance of LigaSure™ Vessel Sealing Devices

    Jim Pappas, Senior Principal Statistician, Medtronic

    • Topic: Predictive Modeling
    • Level: 2

    Medtronic’s LigaSure™ vessel sealing devices are used to fuse blood vessels during surgery to reduce blood loss, ultimately reducing procedure times and patient recovery times. LigaSure™ devices, powered by the Valleylab™ FT10 energy platform, typically complete a vessel seal in two to four seconds. With each seal, continuous electrical data, including voltage (V) and impedance (Z) signals, are recorded by the FT10 generator. Understanding key features within this time series data that are critical to device performance is important for developing safe and effective vessel sealing devices. This presentation will demonstrate the use of the Functional Data Explorer (FDE) platform in JMP Pro to characterize time series data associated with vessel sealing. The use of dynamic time warping (DTW) for data preprocessing has proven to be a valuable tool for exploration and interpreting data features. Additionally, FDE has been used to obtain functional principal component (FPC) scores for use in subsequent analyses and predictive models. Graph Builder has been powerful for understanding time-warped data, data features and their relationships with FPC scores. A real-world example will demonstrate interactive JMP platforms, including Graph Builder, FDE, and the Multivariate and Predictive Modeling platforms.

  • Session ID: 2019-US-45MP-281

    Verbena

    Strangelove: How I Stopped Worrying and Learned to Love A-Optimal Designs

    Heath Rushing, Principal, Adsurgo

    Andrew Karl, Senior Management Consultant, Adsurgo

    • Topic: Design of Experiments
    • Level: 2

    Many years ago, a mentor convinced us to approach teaching DOE not as an instructor, but as a motivator to gain disciples, students who would use DOE so effectively and efficiently that others would follow. For years we have used that approach to spread the use of both D- and I-optimal designs in JMP. Then something happened on the way to dinner; A-optimal designs were introduced in JMP 14. While developing examples for these new (in JMP) designs to augment our current teaching material, we found A-optimal designs to be the most adaptable custom design in JMP. In this talk, we will motivate the use of A-optimal designs. We will start by demonstrating their flexibility: the ability to emphasize some effects (say main effects) over other effects (say interactions) in design selection. Using multiple design metrics, we will then compare an unweighted A-optimal design to other more popular design choices (D- and I-optimal) to show they are not only flexible, but also can perform decidedly better. Augmenting a design with an A-optimal design can produce a superior hybrid design. Lastly, we will demonstrate the use of space-filling designs to compare the utility of weighting schemes for A-optimal designs.

3:30 - 4:00 p.m.

Arizona and Terrace Foyers

Break and Discovery Expo

4:00 - 7:00 p.m.

Canyon I and Arizona Foyer

E-Poster Presentations and Networking Reception

View Group A from 4:00 - 5:00 p.m. and Group B from 5:00 - 6:00 p.m.

Friday, Oct. 18

7:30 - 8:45 a.m.

AZuL, Arizona Deck and Arizona Foyer

Breakfast

8:00 - 8:45 a.m.

Arizona and Terrace Foyers

Discovery Expo

9:00 - 9:45 a.m.

Breakout Sessions

  • Session ID: 2019-US-45MP-250

    Aster

    To Drink or Not to Drink? That Is the Question. Analyzing Alcohol Data With JMP® 15

    Mandy Chambers, JMP Principal Test Engineer, SAS

    Melanie Drake, JMP Principal Systems Developer, SAS

    • Topic: Data Access and Manipulation
    • Level: 2

    Did you know that 30% of Americans are teetotalers, while another 24 million consume on average about 10 drinks per day? In 2017 alone, alcohol sales in the United States amounted to approximately $234.4 billion. For this presentation, we narrowed our scope to explore alcohol sales data in North Carolina. Our objective was to help plan marketing strategies for increasing vodka sales for a local distillery by analyzing sales patterns in different counties. We began with using the new PDF import in JMP 15 to pull data from the web, as well as taking advantage of the data table features to enhance visualizations, clean up the data and prepare it for our analysis. New features such as header graphs in the data table help determine which columns to use in evaluations. JMP allows predictive modeling, SEM and forecasting to gather possible future sales scenarios, and JMP Live publishes these reports for everyone to view. When we are finished, not only will you know interesting statistics about the amount North Carolinians drink or the types of alcohol they consume, but we will show how to use new JMP features. You will learn how painless JMP 15 makes it to analyze your data more effectively, create graphical visualizations attractive to the eye and share your results more easily.

  • Session ID: 2019-US-45MP-268

    Indigo

    Applications of Dynamic Regression Models in Business and Industry

    Bob Lucas, Principal, Robert M. Lucas Consulting

    • Topic: Predictive Modeling
    • Level: 2

    Dynamic regression models extend multiple regression models by allowing for independent variables to be incorporated as leading indicators of the dependent variable and to account for autocorrelation of the dependent variable. Dynamic regression models can be built in the JMP Time Series platform. Manufacturing processes data is often a time series with process input characteristics and the final output quality varying over time. A dynamic regression model may be used to ascertain how to adjust process parameters to control the variability of process output quality. In business, market mix models are used to evaluate the return on investment of different advertising strategies. A dynamic regression model can be used to evaluate the return on investment of advertising spending by different media, a market mix model. One can use the model to drive sales by allocating advertising budgets efficiently. The author will use the above examples to illustrate how to build dynamic regression models using JMP and to interpret report results.

  • Session ID: 2019-US-45MP-190

    Verbena

    Construction, Properties and Analysis of Group-Orthogonal Supersaturated Designs (GO-SSDs)

    Bradley Jones, JMP Distinguished Research Fellow, SAS

    • Topic: Design of Experiments
    • Level: 2

    This talk will introduce a new method for constructing supersaturated designs (SSDs). The method leads to a partitioning of the columns of the design so that the columns within a group are correlated to the others within the same group, but are orthogonal to any factor in any other group. These new designs are called group-orthogonal supersaturated designs (GO-SSDs). Using this group structure it is possible to find an unbiased estimate of the error variance and develop an effective, design-based model selection procedure. Simulation results show that the use of these designs, in conjunction with our model selection procedure, enables the identification of larger numbers of active main effects than have previously been reported for supersaturated designs. These designs and their automated analysis are new in JMP 15. This talk will provide an example of this new design approach applied to the time it takes the custom design tool in JMP to create a design as a function of 12 factors. With only 12 runs, the design correctly identified the effects of the five most important factors.

  • Session ID: 2019-US-45MP-189

    Lantana

    Analysis of Fly Ash Concrete Curing Curves From a Mixture Amount Experiment Using FDE in JMP® Pro

    Philip Ramsey, Principal Lecturer, University of New Hampshire; and Owner, North Haven Group

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

    • Topic: Predictive Modeling
    • Level: 2

    Fly ash is a common effluent of coal-fired power plants and is increasingly used as an admixture in concrete formulations. Although it takes 28 days for concrete to reach its full strength, the reaction temperature curves in the first 24 hours are indicative of long term strength. Using a mixture amount experiment with three admixture components and total amount of addition as a process variable, we analyze the 24-hour curing temperature data from each of 23 trials in the experiment. Historically only a feature of the temperature curves such as maximum temperature is analyzed directly; however, using the Functional Data Explorer, we can analyze the full set of thermal curves from the experimental trials and subsequently use the Generalized Regression platform to find an optimum admixture composition and amount of addition for fly ash concrete. As far as we know this is a unique approach to the analysis of concrete formulation experiments and the FDE approach has wide applicability to many types of formulation and process experiments where traditionally only a single feature of each curve has been analyzed.

10:00 - 10:30 a.m.

Breakout Sessions

  • Session ID: 2019-US-30MP-197

    Aster

    Application of JMP® Data Mining and Multivariate Analysis Tools in Coffee/Tea Health

    Patrick Giuliano, Senior Quality Engineer, Abbott

    Anna Wu, High School Student, Mission San Jose High School

    Mason Chen, Student and Six Sigma Black Belt, Stanford Online High School

    • Topic: Predictive Modeling
    • Level: 2

    The purpose of this project is to determine which Starbucks drinks among all coffee and tea options are best for cardiovascular disease (CVD) prevention and overall good health. A science-based health index is constructed to consider different coffee/tea nutritional constituents, including saturated fat, cholesterol, sodium, carbohydrates, dietary fiber, sugars, protein and caffeine. Antioxidant activity of flavonoids from caffeine can reduce free radical formation and scavenge free radicals. Principal components analysis (PCA) is used to explore all factors in the analysis and to inform on the utility of the health index in relation to its link to CVD prevention and net healthiness. Principal component 1 is more relevant to most unhealthy constituents such as sugars, carbohydrates, saturated fat and total fat. Principal component 2 is more related to beneficial health due to caffeine content. Additionally, dietary fiber and caffeine are most opposite versus the other unhealthy constituents along the direction of both the first and second principal components on the loading plot. PCA eigen analysis is a very powerful computational and visual diagnostic tool for discrimination and classification of coffee product types based on patterns in nutritional constituents. To avoid variance inflation due to the contribution of the many constituents in the analysis, the original data has been Z-transformed, and JMP loading plots are standardized. A novel PCA-based health index is derived based on the eigenvalues and eigenvectors of the first two principal components. The new PCA-based health index is also compared and correlated to a previously established science-based health index (~70-80% R-squared Curve Fitting). Due to the orthogonality of principal eigen analysis, the remaining eight principal components are neutral on the health index (~0% R-square). The PCA has also demonstrated the Pareto concept (the first 20% of principal components have addressed ~80% of the total variance).

  • Session ID: 2019-US-30MP-247

    Indigo

    Graph Builder Contour Plots in JMP® 15

    Dan Schikore, JMP Principal Software Developer, SAS

    • Topic: Data Visualization
    • Level: 1

    Contour plots are a common visualization technique for summarizing the shape of a dense collection of data. The JMP Graph Builder contour element now has six different types of supported contours, including triangulations for 2D value contours, three types of 2D density contours and two types of 1D density contours. This talk will go in-depth on each of the contour visualization techniques and their strengths and weaknesses, including the various options that are supported for the different types, including smoothing parameters, outliers and alpha-shapes for non-convex domains.

  • Session ID: 2019-US-30MP-239

    Verbena

    Automate the Testing of JSL Using Hamcrest

    Justin Chilton, JMP Senior Associate Test Engineer, SAS

    Evan McCorkle, JMP Software Developer, SAS

    • Topic: JSL Application Development
    • Level: 3

    Have you written some JSL and gotten tired of manually testing after every change? Have you inadvertently broken some piece of your application, or has the fear of doing so prevented you from making the changes you want to make? With automated testing, you can be more confident in your changes. Now available is a set of tools for automating the testing of JSL. This framework includes the creation of tests and test cases, as well as an implementation of the well-known Hamcrest assertion library. Hamcrest provides flexible ways to assert what you know to be true about the behavior of your JSL. These tools are full-featured and can be specialized for your needs. In fact, JMP development even uses them to test JMP itself. The presentation will cover this framework and its use in testing an example JSL application, from individual functions to the automation of GUI interactions.

  • Session ID: 2019-US-30MP-249

    Lantana

    Use of Functional Data Explorer in the Analysis of Photovoltaic Devices

    Donald McCormack, JMP Technical Enablement Engineer, SAS

    Hadley Myers, JMP Systems Engineer, SAS

    Christian Kaufmann, Principal Scientist, Competence Centre Thin-Film and Nanotechnology for Photovoltaics

    Muhammad Saif Ullah, Researcher, Pakistan Institute of Nuclear Science & Technology

    Tobias Bertram, Postdoctoral Researcher, PVcomB

    Rutger Schlatmann, Professor, Hochschule für Technik und Wirtschaft

    • Topic: Data Exploration
    • Level: 2

    Data taken across continuous measurement ranges are usually expressed as sets of summary statistics, surrogates for real-valued continuous relationships. Attempts made at understanding complex processes through standard modeling techniques linking predictors and responses are then complicated by the abstraction caused by these surrogates. This risks omitting critical features and leaves no ability to draw temporal- or spectral-dependent conclusions. Many such examples are found in semiconductor and photovoltaic manufacturing, where advancements are largely dependent on gained knowledge from measurement and metrology techniques involving a continuous sweep of any physical phenomenon, including voltage, wavelength, temperature, sputtering depth, magnetic field strength, diffraction angle, etc. The Functional Data Explorer in JMP Pro offers a solution in which scalars can be extracted from continuous data to be used in subsequent modeling steps that somehow retain the functional aspect of the data. This presentation explores a use case where FDE was used in the study of photovoltaic devices, thus revealing insights that could not have been uncovered using traditional analysis methods.

10:30 - 11:00 a.m.

Arizona and Terrace Foyers

Break and Discovery Expo

11:00 a.m. - 12:15 p.m.

Plenary Session

  • Grand Canyon

    Beyond Eureka! Moments

    Steven Johnson, Farsighted: How We Make the Decisions That Matter the Most

    It’s a common belief that the best ideas come in a flash of brilliance, a “eureka!” moment where an individual suddenly and spectacularly gains the clarity needed to move a good idea forward or solve a complex problem. Best-selling author Steven Johnson says these “lightbulb” moments are more the exception than the rule. Innovation, Johnson argues, is much more likely to come to individuals who seek collaboration and input to help shape their ideas. In his talk, Johnson will show us how many of the best ideas and advancements in human history happened when individuals gave their ideas time to develop, and allowed their ideas to be shared, challenged, shaped and merged with others. Host of the six-part PBS series How We Got to Now and best-selling author of 11 books, including the recently released Farsighted: How We Make the Decisions That Matter the Most, Johnson will share lessons from cognitive science, social psychology, military strategy and other areas to help you tackle life’s most consequential decisions – the ones that forever alter the course of our lives or careers, or move our organizations forward.

     

    You’ll also learn:

    • The benefits of sharing, not protecting, good ideas.
    • Ways to make your environment more creative and innovative; a place where good ideas can flourish.
    • How to use today’s tools, including a highly accessible network of people and ideas, to realize radical innovation for yourself or your organization.
    • Johnson’s exciting and optimistic vision of human progress and innovation.

12:30 - 1:30 p.m.

Arizona Foyer

Book Signing With Steven Johnson

12:30 - 2:00 p.m.

AZuL, Arizona Deck and Arizona Foyer

Last-Chance Networking Lunch

12:30 - 2:00 p.m.

Sonoran

Steering Committee Meeting

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  Design of Experiments
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