As our tables get more numerous, our data gets bigger and our models have more features to encapsulate, we are looking for improvements to organize, contain and model. With the release of JMP 14, there are new ways to do all this.
Abstracts
Projections and Encapsulations
John Sall, Co-Founder and Executive Vice President, SAS
Risk Literacy: The Cement of Society
Gerd Gigerenzer, Director, Harding Center for Risk Literacy, Max Planck Institute for Human Development
We invest billions in technological progress, but little in helping citizens understand the technology. The result is collective risk illiteracy: People fear what’s unlikely to kill them and can be maneuvered into willingly relinquishing part of their individual freedom. Can the general public learn to deal with risk and uncertainty, or should authorities steer people’s choices in the right direction? Some argue that because people are hardly educable, governmental experts who know what is best for us need to step in and steer our behavior with the help of “nudges.” Yet experts themselves often lack risk literacy: Many judges, doctors, journalists and politicians alike do not understand statistical evidence and can be fooled into wrong conclusions without noticing. I argue that in the 21st century, we need not less but more paternalism and to take serious efforts to make the general public risk literate.
The Cost of Certitude
Bradley Jones, JMP Distinguished Research Fellow, SAS
Resistance is a natural, and often immediate, response to a new idea. It is tempting then, to relabel new as bad. To embrace new thinking requires us to change, and change is hard. It is easier to remain in the comfort zone of what we think we "know.” Doing this imposes the cost of certitude. Stories of critics mistaking masterpieces for rubbish and instances of my own resistance to new ideas illustrate this cost.
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Reliability Analysis and Engineering Using JMP® (EU 2018 426)
Peng Liu, JMP Principal Research Statistician Developer, SAS
Leo Wright, JMP Principal Product Manager, SAS
Michael Crotty, JMP Senior Statistical Writer, SAS
- Topic: Quality and Reliability
- Level: 3
The state-of-the-art suite of tools in JMP for studying reliability is designed to enable organizations to provide superior products – a requirement for success in today’s arena of complex products and services. For many years, we have incorporated cutting-edge concepts that integrate robust statistical analysis with dynamic data visualization to help users immediately assess performance, spot trends and diagnose improvement opportunities, even in huge data sets. JMP has recognized that reliability, also known as “quality over time,” is crucial across the board, from consumer and industrial products to health care to defense systems. JMP reliability tools are designed to assist engineers, researchers and managers to understand reliability data and system reliabilities easier and faster, whether the system studied is relatively simple or thoroughly complex. JMP reliability tools, backed by the powerful JMP infrastructure, are also extensible and customizable. In this session, we will cover the following JMP platforms: Life Distribution, Reliability Forecasting, Degradation and Repairable Systems Simulation. The platforms are among the essential components of reliability analysis and engineering, addressing problems for both repairable and non-repairable systems. We will start with an introduction to the censored data and will outline different objectives of reliability analysis and engineering. We will address individual objectives by using examples included with JMP software.
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10+ Things You Don’t Know About JMP® (EU 2018 424)
Brady Brady, JMP Technical Enablement Engineer, SAS
- Topic: Data Exploration
- Level: 1
As you already know, JMP is an amazingly rich tool for data analysis and exploration. That also means that you know that there's more than one way to do almost anything in JMP. The 10 (or more!) tips and tricks shared here will make you more efficient and introduce you to techniques, keyboard shortcuts, formulas and more. Come in and level-up from novice to expert, or expert to guru.
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An Add-In to Analyze Field Trial Data: The Custom ANOVA (EU 2018 102)
Anne Saint-Amans, Statistician, Limagrain
Cédric Loi, Statistician, Limagrain
- Topic: JSL Application Development
- Level: 2
Limagrain, the fourth-largest seed company worldwide, creates new varieties for vegetable and field crops every year. These new varieties are tested and compared to commercial references based on several variables (e.g., yield, length of fruits). Analysis of variance and mixed models are run to take into account undesired environmental effects (e.g., border, ventilation, soil fertility) and extract the genetic potential of the varieties. JMP possesses a lot of features to conduct the required statistical studies. Limagrain developed an add-in called “Custom ANOVA” to gather them. The objective is to offer a complete and interactive report specifically adapted to the group’s research needs. A minimalist and intuitive interface enables the user to select a few options before launching the analyses. The final report contains different tabs, each of them associated with a particular step: descriptive statistics, statistics of the model, validation of the hypotheses, effects study, genetic potential calculation, field maps and multiple comparisons. In addition, the add-in exploits the “expression object” of JSL, providing the ability to save the script of the report in the data table (like any other JMP report). Thus, the user can keep track of its results and easily regenerate the whole report.
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Application of Data Analytics to Improve the Yield of a Chemical Production Process: Learnings and Best Practices (EU 2018 113)
Elie Maricau, Senior Data Scientist, BASF Antwerpen
- Topic: Data Exploration
- Level: 2
This presentation discusses a case study in a chemical production plant where the objective is to improve the product yield. As part of the project, five years of historical data (including lab data, sensor data and logbook text data) have been analyzed. The results of the analysis trigger dedicated experiments in the plant, both to validate data-driven findings and to further explore unknown relationships. During the presentation, I will highlight some critical success factors in the project approach (i.e., LSS DMAIC) and demonstrate how JMP was used to find relationships hidden in the data. I will demonstrate how JMP can be effective to quickly aggregate data sets, clean the data, separate noise from actual process variations, and visualize and quantify relationships. Techniques that will be discussed and demonstrated include table operations, data filtering, modeling and DOE.
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Building Dashboards and Applications With JMP® (EU 2018 419)
Dan Schikore, JMP Principal Software Developer, SAS
- Topic: Data Visualization
- Level: 2
Saving an analysis so that it can be repeated on the same or different data tables is an important step in creating an analysis workflow that is efficient and repeatable. The JMP Scripting Language (JSL) can be used along with the drag-and-drop tools available in the JMP Dashboard Builder and Application Builder to quickly build and deploy dashboards and applications. This talk will cover everything from simple dashboards created with Combine Windows through full applications with multiple windows and JSL scripts. Learn more about designing reports with data filters, parameterizing applications for run-time selection of columns to analyze and options for sharing of results, including interactive HTML. The new custom template interface will be demonstrated, which makes it easier and faster to create multiple dashboards or applications with a common look and feel.
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Definitive Augmentation of Definitive Screening Designs (EU 2018 210)
Paul Nelson, Technical Director, Prism Training & Consultancy
Phil Kay, JMP Systems Engineer, SAS
Andrew Macpherson, Managing Director, Prism Training & Consultancy
- Topic: Design of Experiments
- Level: 2
Definitive screening designs (DSDs) uniquely address the key needs of many experimenters. How else can we explain the rapid and enthusiastic adoption of DSDs since their discovery was published in 2011? For many experimenters, 13- or 17-run DSDs for five to seven factors are go-to designs when screening for the few driving factors. Along with ‘Fit Definitive Screening’ in JMP, you potentially have a simple, efficient and effective experimental workflow to find the important main effects, interactions and curvilinear behaviors of these factors. If only three of the factors are active, you can fit the full second-order RSM model and achieve screening and optimization in one step. But what if more than three factors are active? When ambiguity occurs is there a simple next step? Or does the complexity of this situation become a barrier to adoption of DSDs? In this presentation, you will first hear about real-life examples that demonstrate the value of DSDs. You will then see simple ways to augment these DSDs, ensuring that the structure and properties can be preserved to maintain the benefits of DSDs. Consequently, more people in more situations can benefit from the workflow of sequential DOE and DSDs.
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Design of Experiment in Numerical Simulation (EU 2018 208)
Olivier Brack, Consultant in Industrial Statistics, KSIC
- Topic: Predictive Modeling
- Level: 4
On the eve of the publication of a new AFNOR standard (FDX06 081) devoted to DOE on simulation and industrial tests, we show an example of a JMP application to solve an optimization problem performance of a tracked vehicle undercarriage based on numerical simulations. After processing the data from unplanned simulations to address the problem, new simulations were performed to obtain more relevant information. These new simulations were also carried out to obtain data for comparing designed experiments, with a pedagogical objective in the framework of training at École Polytechnique. This example shows the utility, too often neglected, of the DOEs in numerical simulation. From the design of the tests, to the recalibration of the theoretical model on real experimental data, passing, obviously by the statistical treatment (notably taking into account the experimental variability, too often and so unfairly put aside in simulation) and graphical of the results of the plans, this presentation highlights the performance of JMP in achieving the requirements of DOEs in numerical simulation.
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Exploring Functional Data (EU 2018 124)
Doug Marsh, Upstream Senior Scientist, GlaxoSmithKline
Gary Finka, Director, GlaxoSmithKline
Sonya Godbert, Statistics Leader, GlaxoSmithKline
Phil Kay, JMP Systems Engineer, SAS
- Topic: Data Exploration
- Level: 2
There are many ways you can explore how the X’s relate to the Y’s when you have a rectangle of data, with columns of variables for each row (samples, runs, batches). But lots of other data that you might want to use doesn’t easily fit into this form. A common example is where, for each sample, run or batch, you have some measure that has been recorded over time. What if you want to relate the shape of this “functional data” to other variables? The challenge is to extract a small number of “features” that capture the useful information from the complete functional data from each “function ID” (the samples, runs or batches). The features can then be used as X’s or Y’s in a standard data rectangle. Other challenges include the fact that the data may not be aligned across function IDs: The frequency of collection may be different, there may be an offset, or there might be missing data at some points for some of the function IDs. With examples from a biopharmaceutical setting, we will show how valuable insights can be extracted from this type of data using Functional Data Explorer in JMP 14.
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Formula Editor Deep Dive (EU 2018 411)
Mike Muhlada, JMP Development Tester, SAS
Audrey Shull, JMP Senior Development Testing Manager, SAS
- Topic: Data Access and Manipulation
- Level: 1
This introductory demo will use examples to explain Formula Editor mechanics, to investigate and fix common errors and to solidify best practices and useful ‘tricks’ for formula creation. A detailed walkthrough will cover various aspects of the formula builder, including how to use drag-and-drop, keyboard shortcuts and function buttons to create formulas. As we work the examples, we will discuss useful functions, uncover and correct common mistakes, work with subscripts and more. Best practice topics to be covered include custom formats, using formulas for data ‘cleanup,’ column transforms and using intermediate columns to build more complex formulas. Special emphasis will be placed on JMP 14 new features including user-defined functions, result preview, ‘live’ formula error/warning detection and customizable function list.
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Getting to the Fun Part: How to Prepare Your Data for Analysis (EU 2018 420)
Brian Corcoran, JMP Director of Research and Development, SAS
Mandy Chambers, JMP Development Tester, SAS
Kelci Miclaus, JMP Life Sciences R&D Manager, SAS
- Topic: Data Access and Manipulation
- Level: 1
JMP visualization and statistical analysis platforms allow you to discover meaningful signals, trends and insights to your data. When a project requires working with multiple complex data files, getting to that “aha” moment requires necessary data querying, joining, import, exploration and cleaning. It is not uncommon for up to 80 percent of your time to be spent solely on data preparation in complex or large data analysis tasks. This session highlights the use of several new features in JMP 13 and JMP 14 to get you to that analysis-ready state quickly and easily. We will discuss how data can be queried and imported from a database and opened and joined with the JMP Query Builder, as well as multiple file import options new in JMP 14. JMP 13 introduced virtual join to JMP data tables, which allows you to specify common keys to link multiple tables. New capabilities in JMP 14 direct a table to listen or dispatch row state changes among linked tables. We will demonstrate how these new features allow virtually joined tables to “talk” to each other by sharing row states for easy data exploration and immediate analysis/dashboard creation without table manipulation. Finally we will tackle the dirty job of cleaning your data, highlighting new features of Recode to demonstrate how we can now replace strings using regular expressions, remove unwanted punctuation, save Recode scripts, reuse and append to them and even use value labels if the column has them already assigned. These topics will streamline every JMP user's experience with import, table exploration and data cleaning, leaving more time for fun: data analysis.
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How JMP® 14 Can Predict Performance of Future Batches: A Game Changer in Validation of Pharmaceutical Processes (EU 2018 204)
Per Vase, Managing Partner, Applied Statistics Group, NNE
- Topic: Quality and Reliability
- Level: 2
Many companies within the pharmaceutical industry still pass a Process Performance Qualification (PPQ) simply on the fact that the estimated Ppk on three consecutive batches are better than the acceptance criteria. However, validation is about predicting the future – NOT the past. So even though the three batches may have a sufficient Ppk, this is not sufficient. The prediction for future Ppk values also needs to look promising. The International Society of Pharmaceutical Engineering (ISPE) has published a discussion paper, Evaluation of Impact of Statistical Tools on PPQ Outcomes, on how this prediction can be made from a variance component calculation of between- and within-batch variance converted to process capability indices. One of the reasons this method has not become widespread is the lack of statistical software packages with this calculation method as a standard. However, with the three-way chart in JMP 14, you can now calculate the between- and within-Sigma capability. Examples where this method has been used for PPQ evaluation will be shown in JMP 14, both with successful and unsuccessful evaluations. It will be compared with the traditional method mentioned previously.
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Industry Training Session Based on a Case Study: Using JMP® to Learn How to Design Split-Plot Experiments (EU 2018 121)
Jacqueline Asscher, Senior Lecturer and Consultant in Industrial Statistics, Kinneret College on the Sea of Galilee
- Topic: Design of Experiments
- Level: 2
When experiments have a split-plot structure, this should be taken into consideration in both design and analysis. A common example is an experiment conducted to improve a process that runs in batches, where some factors are changed between batches and other factors are changed within batches, resulting in runs that are not fully independent. The split-plot structure may be inherent in the process being investigated or in the experimental setup, or may be adopted by choice either to save time or money, or to improve the design. In the case study, some factors are related to material preparation, and other factors are related to the processing of the material into the final product. In the training session for practitioners, the JMP tools for designing, evaluating and comparing experiments are used to explore alternative strategies for choosing a design. The candidate designs considered include the default and user-specified designs built by JMP and designs built from scratch by the user. For example, it is usually cheaper to include minimal whole plot replication, and we explore the consequences of adopting this strategy. The pedagogical aim is to simultaneously teach the topic and the use of the JMP tools.
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Integrated Process Improvement Using the Second-Generation Quality Tools in JMP® (EU 2018 422)
Laura Lancaster, JMP Principal Research Statistician Developer, SAS
- Topic: Quality and Reliability
- Level: 2
The second-generation quality tools in JMP – Control Chart Builder, Process Capability, Measurement Systems Analysis and Process Screening – were designed with an integrative philosophy to make quality analysis easier and more effective. For example, the new Process Capability platform was designed to reflect the type of control chart used in the statistical process control program, and a capability report was added inside of the Control Chart Builder. Similarly, the Shift Detection Profiler in the Measurement Systems Analysis platform allows quality engineers to make informed decisions about how to design their control chart methodology, taking into account their measurement system so they are alerted to process changes as quickly as possible. Understanding how your measurement system, statistical process control program and process capability assessments fit together is key to improving and maintaining quality. The software’s unique design philosophy makes this simple and straightforward. Additionally, the ability to quickly screen large numbers of processes for stability with the new Process Screening platform will save time, reduce workload and improve quality. This session will use a case study to show how to use each of these platforms with an integrated process improvement approach.
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JMP® Projects: A New Way to Organize Your Work in JMP® 14 (EU 2018 416)
Daniel Valente, JMP Senior Product Manager, SAS
Aaron Andersen, JMP Principal Software Developer, SAS
- Topic: Data Access and Manipulation
- Level: 1
Many JMP users have experienced an interactive JMP session that left them with many windows and analyses reports opened all at once. While using JMP in this "floating window mode" may work for many, there are situations where having a tabbed interface is preferred. Users also may want to associate certain files with an analysis project and not have the full list afforded by the Home Window. For these reasons, we created the JMP project in JMP 14. Projects provide a single document interface to JMP, a tabbed, re-configurable work space, a place to bookmark files and a window list that lets the user easily navigate various open windows, data tables, scripts and more as well as launch supporting files like PDFs and PPT documents. Projects can also be used to quickly open and close many files associated with one or more individual analysis activities, and can be used to keep parallel projects separate where in the past you may have needed to run multiple JMP sessions. Finally, projects in JMP 14 can also give you an easy way to share and collaborate on work with their archiving functionality.
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Model Validation Strategies for Designed Experiments Using Bootstrapping Techniques With Applications to Biopharmaceuticals (EU 2018 201)
Philip Ramsey, Principal Lecturer, University of New Hampshire; and Owner, North Haven Group
Chris Gotwalt, JMP Director of Statistical Research and Development, SAS
- Topic: Predictive Modeling
- Level: 2
There are two different goals to statistical modeling: explanation and prediction. Explanatory models often predict poorly (Shmueli, 2010). Often analyses of designed experiments (DOE) are explanatory, yet the experimental goals are prediction. DOE is a best practice for product and process development where one predicts future performance. Predictive modeling requires partitioning the data into training and validation sets where the validation set is used to assess predictive models. Most DOEs have insufficient observations to form a validation set precluding direct assessment of prediction performance. We demonstrate a “balanced auto-validation” technique using the original data to create two copies of that data, one a training set and the other a validation set. The sets differ in row weightings. The weights are Dirichlet distributed and “balanced;” observations contributing more to the training data contribute less to the validation set (and vice versa). The approach only requires copying the data and creating a formula column for weights. The technique is general, allowing one to apply predictive modeling techniques to smaller data sets common to laboratory and manufacturing studies. Two biopharma process development case studies are used for demonstration. Both cases have large validation sets combined with definitive screening designs. JMP is used to demonstrate analyses.
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Multivariate Testing of the Similarity of Dissolution Curves (EU 2018 104)
Piet Hoogkamer, Principal Analytical Scientist, Abbott Healthcare Products
Sven Daniel Schmitz, Data to Knowledge Expert, Abbott Healthcare Products
- Topic: Quality and Reliability
- Level: 1
Dissolution testing of pharmaceutical products is important, as it is a surrogate measure of in-vivo dissolution. In-vivo dissolution affects the bio-availability, which may affect pharmacokinetics (blood levels) and, as a result, safety and efficacy. In case a change is proposed with respect to the manufacturing process, manufacturing site or test method, “equivalence” needs to be demonstrated to obtain a bio-waiver. Similarity of curves may be tested using the mathematical f2 metric, where a value between 50 and 100 suggests similarity. The f2 metric will be explained in detail. However, in case the variability is too high (between tested units, within a time point), a multivariate distance-based statistic needs to be used instead. Some options will be shown for model-dependent and model-independent approaches. Use of JMP, both with standard functionality and with a dedicated script, will be illustrated for the Mahalanobis distance.
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New and Advanced Views in JMP® 14 Graph Builder (EU 2018 423)
Xan Gregg, JMP Director of Research and Development, SAS
Scott Wise, JMP Principal Systems Engineer, SAS
- Topic: Data Visualization
- Level: 1
In this session, we’ll first take a guided tour of several new and refined Graph Builder features in JMP 14. A new interval drop zone makes custom error bars easier. Several kinds of jitter have been added, including a new default. A completely new bar chart style called “packed bars” extends Pareto charts. More element types support parallel coordinates for compact multivariate views. Contours can be smoothed. Elementary statistics are accessible.
Then we will explore how to build popular and captivating advanced graph views using JMP Graph Builder. We have created brand new pictures for the Gallery 3 journal that features additional views available in the latest versions of JMP. We will show several popular industry graph formats that you may not have known could be easily built within JMP. Views such as incorporating bullet charts, two-way error bars, volcano plots and more can help breathe life into your analytics and provide a compelling platform to help manage your results.
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New DOE Features in JMP® 14 (EU 2018 417)
Ryan Lekivetz, JMP Senior Research Statistician Developer, SAS
Bradley Jones, JMP Distinguished Research Fellow, SAS
- Topic: Design of Experiments
- Level: 2
JMP 14 has several new DOE features. Perhaps the most important is a new design tool for creating balanced incomplete block designs (BIBDs). These designs have one treatment factor and one blocking factor. Each treatment level appears the same number of times overall, and each treatment level appears together with every other treatment level in some block the same number of times. This creates a special kind of balance. We will provide more details about this design and demonstrate the UI of the new tool.
The Custom Designer has added a pair of optimality criteria for generating A-optimal designs and weighted A-optimal designs. An A-optimal design minimizes the average variance of the parameter estimates. The A-criterion is similar to the well-known D-criterion in that it focuses on the precise estimation of model parameters. There are two benefits to A-optimal designs. First, it is easier to understand how the A-criterion produces good designs than it is with the D-criterion, making it easier for new DOE practitioners to adopt. Second, weighted A-optimal designs allow for putting different amounts of emphasis on different groups of parameters. For example, one might want to emphasize pure quadratic effects over two-factor interactions and both of these over main effect estimation.
The Compare Designs tool can now accommodate up to five designs in the UI and up to 10 designs through scripting. Previously, the limit was three designs. We will demonstrate this additional capability with a UI example and a scripting example that includes the new A-optimal and weighted A-optimal designs.
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One-Stop Model Building With the Generalized Regression Platform (EU 2018 421)
Clay Barker, JMP Senior Research Statistician Developer, SAS
- Topic: Predictive Modeling
- Level: 2
Variable selection – the process of choosing a set of factors or predictors to model a response – is an essential part of the model-building process. Luckily, it does not have to be a painful process. The Generalized Regression platform (Genreg) in JMP Pro provides a variety of automated variable selection techniques that make this process fast and easy. After using the platform, we are left with a parsimonious model that will predict well on new data. 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. We will also preview some of the new Genreg features in JMP 14, like modeling multilevel categorical responses and the Dantzig Selector.
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Predictive Control Synthesis of an Industrial Control Loop With JMP® (EU 2018 205)
Thomas Zelikman, Senior Consultant, NNE
- Topic: Predictive Modeling
- Level: 3
An industrial setup is presented, where JMP was used to import and join files from several different temporal data sources, including vibration measurements, machine operation data and video files. With JSL in JMP, it was possible to create a script that performs all necessary conversions and preconditioning of data such as extraction of sound from video files, spectral analysis of sound and vibration and synchronization of temporal data with geometrical measurement positions on the specimens produced in the process to enable linking process events with measurements on the output. Multivariate methods were used to create a prediction of the output suitable for exporting predictor expression to the controller. Design of experiments methods were used to identify how the output should be affected to close the control loop and obtain the desired response.
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Project, Insight, Action!!! (EU 2018 116)
Martin Owen, Director, Insight by Design
Walkiria Schlindwein, Associate Professor of Pharmaceutics, De Montfort University
- Topic: Design of Experiments
- Level: 2
As a lecturer and trainer, I am very interested in the capabilities offered by the new Projects functionality in JMP 14. I see this platform extending our abilities to enhance organizational memory, bringing material (data sets, scripts, model and visualization reports) together from different sources. We can map onto existing file structures and create a new organizational structure via a single document interface. In this presentation, I will give examples of the potential for JMP 14 Projects as a teaching tool. The aim is to accelerate learning and help teams or organizations adopt new workflows, retain insights from prior studies, make better decisions and take action. The flexible Projects workspace brings to life compelling stories. It is now easy to:
- Create a workflow of sequential operations, from designing experiments to analyzing and documenting key information and actions.
- Show how information builds over a series of experimental design work packages.
- Compare and select from different modeling and visualization approaches.
- Create and archive standard reports that update when new data is entered.
A bonus is that it is now easier to manage and curate data as I work – closing a current session ready to regenerate the next day couldn’t be simpler.
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Sharing JMP® Graphs and Reports (EU 2018 412)
Bryan Fricke, JMP Senior Software Developer, SAS
Michael Hecht, JMP Principal Systems Developer, SAS
- Topic: Data Visualization
- Level: 1
JMP is the tool of choice for visually exploring and making discoveries about your data. However, since not everyone is a JMP user, you may need to export your graphs and reports in a form tailored to your audience. In this session, we will demonstrate how to get graphs and reports out of JMP and into forms suitable for sharing. In particular, we will cover exporting graphs and reports in forms ready for presentations, printed journals or articles, and interactive web pages.
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Simulation Methods in JMP® and JMP® Pro, With Special Emphasis on the Fractional-Random-Weight Bootstrap (EU 2018 418)
William Q. Meeker, Professor of Statistics and Distinguished Professor, Iowa State University
Chris Gotwalt, JMP Director of Statistical Research and Development, SAS
- Topic: Predictive Modeling
- Level: 4
In this session we will show several examples of how JMP has made it easy to take advantage of simulation-based statistical methods. We will begin with reliability applications and show how to obtain more refined inferences when there are very few observations using Bayesian techniques in the Life Distribution Platform and in the Fit Life by X platform. We will then introduce the One-Click Bootstrap in JMP Pro with several use examples, with special emphasis on the fractional-random-weight bootstrap.
The classic bootstrap, based on resampling, has for decades been widely used for computing trustworthy confidence intervals for applications where no exact method is available and when sample sizes are not large enough to rely on easy-to-compute large-sample approximate methods.
There are, however, many applications where the resampling bootstrap method cannot be used. These include situations where the data are heavily censored; logistic regression when the “success” response is a rare event or where there is limited mixing of successes and failures across the explanatory variable(s); and designed experiments where the number of parameters is close to the number of observations. What these three situations have in common is that it may be impossible in a substantial proportion of the resamples to estimate all the parameters in the model. The fractional-random-weight bootstrap method can be used to avoid these problems and provide trustworthy confidence intervals.
Next we will demonstrate how to do power calculations using the Simulate Feature in JMP Pro. This makes it straightforward to do power calculations for many analyses throughout JMP, particularly designed experiments where the outcome is binary. Finally, we will show how to obtain confidence intervals on neural network predictions using the Save Bagged Estimates option in the Profiler.
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Statistical Thinking for Industrial Problem Solving (EU 2018 413)
Mia Stephens, JMP Academic Ambassador, SAS
Martin Demel, JMP Systems Engineer, SAS
Ian Cox, JMP Senior Marketing Manager, SAS
- Topic: Data Exploration
- Level: 2
In response to feedback and requests from both academics and industry leaders, JMP is developing an online course in statistical thinking for industrial problem solving. In this breakout session, we introduce the principle of statistical thinking and present a practical, data-driven approach to solving real-world problems that serves as the foundation for this new course. We show how to exploit the visual and interactive nature of JMP for exploratory and confirmatory data analysis, and explore core techniques and modern statistical methods through examples and simulations. An end-to-end case study is used to unify concepts and broaden applicability, and a preview of the online course will be provided.
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The Multivariate Flavors of JMP®: From Continuous to Categorical to Discrete to Functional (EU 2018 425)
Laura Castro-Schilo, JMP Statistical Tester, SAS
Chris Gotwalt, JMP Director of Statistical Research and Development, SAS
- Topic: Data Exploration
- Level: 2
Multivariate data analysis has become an essential skill as the amount of data has skyrocketed and companies become more data-driven in their decision making. In this extended breakout session, we showcase JMP and its unique visual and interactive approach to multivariate data analysis using a variety of approaches that rely on continuous, categorical, discrete and functional data. We will begin with a foundational discussion of the essence of multivariate analysis: the idea that information contained in large numbers of variables can often be efficiently represented with a smaller number of variables. We then give an overview of general tools for analyzing multivariate data in JMP.
The session will be given in two parts. The introduction will use data from sensory analysis and consumer research to motivate and demonstrate classic unsupervised multivariate methods such as principal components analysis, multiple correspondence analysis and a new technique in JMP 14, multiple factor analysis. The second part will cover supervised methods for multivariate data using partial least squares and functional principal components. To illustrate these supervised methods we will build models to predict the yield of a batch manufacturing process with data from multiple signal streams. Throughout, we will give clear guidelines as to when each analytical technique is appropriate and will highlight the most important and useful software options.
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Titanic Revisited: All Hands on Deck! (EU 2018 202)
Alfredo López Navarro, Web Intelligence Knowledge Manager, Digital Command Center, Telefónica Germany
Andrea Cecilia Valencia Silva, Telefónica Germany
Martin Demel, JMP Systems Engineer, SAS
- Topic: Data Exploration
- Level: 1
We would like to provide a compelling description of some little-known facts about the disaster of RMS Titanic, which sank in 1912 after colliding with an iceberg. We all know the history, or so we think. Thanks to Telefónica’s Discovery Methodology – powered by JMP – we will embark on an expedition diving deep into a sea of data points to rescue insights from oblivion. We’ll scrap data using the "Internet Open" feature. We will join, concatenate and update tables until we create an enhanced database capable of providing plenty of insights. Then, by means of ANOVAs, categorical and visual analysis we’ll tell a fascinating data-driven story. Expect a last-minute surprise! Of course, it’s going to be all hands on deck: Everything is documented and explained to be uploaded onto the JMP User Community. We would like to open a conversation for others to build on top of this. Come on board with us!
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Using Fit Model Platform to Predict, in a Short Time, the Shelf Life of Some Formulations During the Development Phases of a Drug Product (EU 2018 115)
Paolo Nencioni, Lab Technician, The Menarini Group
- Topic: Quality and Reliability
- Level: 2
In general, International Council of Harmonisation (ICH) guidelines require that a drug product should be evaluated under predefined storage conditions that test its thermal stability. The storage conditions and the lengths of studies chosen should be sufficient to cover storage, shipment and use. The long-term testing should cover a minimum of 12 months’ duration at the time of submission, while the intermediate and accelerated condition should cover a minimum of six months. During development phases, more than one formulation may meet the project requirements. How can we choose the best one? Surely, we would like to choose the formulation with the longest shelf life possible. A formal ICH stability study takes six months to gain any useful responses. By performing accelerated stability studies using a higher temperature than ICH conditions, we can evaluate the behavior of some formulations in a relatively short time (not more than 30 days). With the JMP Fit Model platform, it has been possible to estimate degradation rate in function of the stress factor, arriving to a data-driven decision about the more stable formula. The estimated degradation rates have been used to predict the quality attribute values at time points and storage conditions required in the ICH guidelines. The predicted results have been compared with the actual value of one batch followed using ICH conditions, in order to verify the goodness of fit of the models. The same approach has been used evaluating together the effect of temperature and oxygen level. The models built with JMP have been used to identify the real effects of oxygen content on the critical quality attributes of the finished product.
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Using JMP® to Eliminate Discrimination in the Workplace (EU 2018 212)
Robert Reul, Founder, Isometric Solutions
- Topic: Predictive Modeling
- Level: 2
Eliminating discrimination in the workplace. Possibly the one goal shared by applicants and employers is the ability to seek and secure employment on a full set of non-discriminatory characteristics: knowledge, skill and experience. The unfortunate reality is that these are all too often trumped by other more easily discernable factors that have very little to do with job performance, and worse, embody unethical and illegal practices when hiring applicants for a job. Can data analytics be used to exclude discriminatory bias during the hiring decision? Using extensive academic literature on personality and performance, several frameworks emerge that serve as outcome vectors for predictive models, namely the “Big 5” personality traits and trustworthiness, likeability and confidence. But what to do about the predictors? Experimentation with facial image processing data from sources such as Google and Microsoft showed predictive promise. By using a series of analytic methods that screen for predictive potential and reduce dimensional complexity, predictive prowess emerges with a completely non-discriminatory set of latent variables that inform the “whom to interview”/ “whom to hire” recommendations based purely on cultural personality fit. This presentation will reveal the challenges and successes of this effort, proving it’s not only feasible – it may become the new normal.
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Using JSL to Develop Efficient, Robust Applications (EU 2018 415)
Joseph Morgan, JMP Principal Research Statistician Developer, SAS
- Topic: JSL Application Development
- Level: 3
This session will focus on JSL constructs that can prove invaluable when developing efficient, robust, production-ready applications. These include expression handling functions, xpath support, namespaces and others. The ideas will be illustrated with examples that the instructor has collected over the past 10 years as he has worked with a wide cross-section of JSL users.
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What Did They Mean by That? The Essentials of Text Explorer in JMP® (EU 2018 414)
Don McCormack, JMP Technical Enablement Engineer, SAS
Bill Worley, JMP Technical Enablement Engineer, SAS
- Topic: Data Exploration
- Level: 2
People make comments, machines output status. There are massive amounts of words and phrases that could be used to better understand comments or summaries. The question many companies are asking is what to do with these mountains of untapped unstructured data. Text Explorer in JMP offers you a data mining tool to help analyze and better understand your customers, or why that new piece of equipment that was supposed to save your business is not behaving as expected. The presenters will show off the new platform features in JMP 14, along with features found in the original Text Explorer in JMP 13. Text Explorer has many features that quickly and easily help you determine frequently used terms and phrases that are driving consumer comments or machine reliability. Several case studies will be used to show how amazingly easy it is to clean your data; for instance, options like Local Recode or adding stop words and phrases that have little or no meaning to your analysis. Word clouds are popping up all over and you will learn how to generate an interactive word cloud in just a couple of clicks. The presenters will demonstrate advanced text exploration tools in JMP Pro and how to use Latent Sematic SVD and Topic Analysis within Text Explorer to uncover new insights from your unstructured data. Finally, the presenters will demonstrate that by taking advantage of your transformed unstructured data along with your structured data you can build more predictive and informative models.
- Beginner: 1
- Intermediate: 2
- Advanced: 3
- Power user: 4
Data Mining for Asymmetric Data Set Under the Curse of Dimensionality (EU 2018 301)
Gianpaolo Polsinelli, Senior Data Mining Engineer, LFoundry, A SMIC Company
Felice Russo, Process Control Monitor and Yield Data Management Senior Manager, LFoundry, A SMIC Company
Nonlinear Design of Experiments to Save Time and Money in R&D (EU 2018 302)
Alberto Martinez, Senior Scientist, Procter & Gamble
Zhiwu Liang, Principal Statistician, Procter & Gamble
Design for Experiment, Analysis for Design: Software Solutions for Scientists to Effectively Deliver Real-Life Improvements (EU 2018 303)
Andrew Macpherson, Managing Director, Prism Training & Consultancy
Paul Nelson, Technical Director, Prism Training & Consultancy
Automated and Interactive Analysis of JMP® Crash Data (EU 2018 305)
Shannon Conners, JMP Director of Research and Development, SAS
Why Definitive Screening Designs Are Very Useful for the Development of Chemical Processes (EU 2018 326)
Sandrine Olazabal, Project Quality by Design Lead, GlaxoSmithKline
Scripting a User Interface to Perform a Validated Model Fit in JMP® (EU 2018 332)
Gwenola Ninon, Process Statistician, Fujifilm Diosynth Biotechnologies
Somaieh Mohammadi, Process Statistician, Fujifilm Diosynth Biotechnologies
JMP® Usage in Six Sigma DMAIC Approach for Continuous Improvement Projects: Application in Automotive Semiconductor Industry (EU 2018 331)
Corinne Bergès, Six Sigma Black Belt, NXP Semiconductors
Honeycomb Designs Add-In (EU 2018 327)
Andy Mauromoustakos, Professor, Agriculture Statistics Lab, University of Arkansas
Kevin Thompson, Research Associate, Agriculture Statistics Lab, University of Arkansas
Vasilia Fasoula, Adjunct Assistant Research Scientist, University of Georgia
Model Selection Strategies for Small Designs: Learnings From Process Chemistry Case Studies in a Quality-by-Design Environment (EU 2018 328)
Daniel Tray, Statistics Manager, GlaxoSmithKline
Developing a Rigorous Methodology for Assessing Process Capability and Identifying Critical Process Parameters (EU 2018 334)
Michael Bease, Johnson Matthey
Andrew Ruddick, Director, Process Insight
‘Meat’ Your Match: Using Choice Modeling in JMP® to Optimize Product Packaging (EU 2018 311)
Matt Geeleher, Data Scientist and Insight Consultant, Linney Group
Prediction of Disease-Specific Death Rates by Using Ecological Indicators and Smoking Prevalence (EU 2018 314)
Smilja Djurdjevic, Modeling Analyst, Philip Morris International
Guillaume de La Bourdonnaye, Senior Scientist, Philip Morris International
Nonlinear Modeling of RPA Data Using JMP® (EU 2018 315)
Nuria Vergara-Irigaray, Lead Scientist, TwistDx
Event Matrix: More Exciting Than Applying Time Series Analysis on 20 Years of Aquatic Environmental Time Series From Lake Erken, University of Uppsala (EU 2018 317)
Anders Hasselrot, Founder, Knowledge Discovery
Individual-Level Choice Modeling in JMP® (EU 2018 320)
Roselinde Kessels, Assistant Professor, University of Amsterdam
Bradley Jones, JMP Distinguished Research Fellow, SAS
Peter Goos, Professor of Statistics, University of Leuven and University of Antwerp
Implementation of Proposed Analytical Procedure Lifecycle USP <1220> General Chapter: Automate the Calculations and Visualize the Results Using JMP® (EU 2018 323)
Christel Kronig, Senior Analytical Scientist, Dr. Reddy’s Laboratories
Digital Twin of a Manufacturing Process in a JMP® Data Table (EU 2018 333)
Stephen Pearson, Chemical Process Statistician, Syngenta
Time is Precious! (EU 2018 325)
Angela Manginelli, Specialist Statistician, GlaxoSmithKline
Swetlana Berger, Statistician, GlaxoSmithKline
Using JMP® to Estimate the Prediction Power of Reliability Analysis on Automotive Parts (EU 2018 329)
Marc Stanke, Software Engineer, Life Science Analytics, accantec
