- 12 developer tutorials
- 3 at a time, side-by-side
- 90 minutes long, 30 minute break between
Schedule
| 8:30 - 10:00 |
Using JSL to Develop Efficient, Robust Applications
Joseph Morgan
|
Variable Selection Made Easy Using the Generalized Regression Platform in JMP® Pro
Chris Gotwalt |
Reliability Analysis Using JMP 13
Peng Liu, Leo Wright
|
| 10:30 - 12:00 |
Wrangling All Your Data With Query Builder in JMP® 13
Eric Hill
|
Fitting Definitive Screening Designs (DSDs), Taking Advantage of Their Structure
Ryan Lekivetz
|
Exploiting the Formula Editor in JMP 13
Mike Muhlada, Ernest Pasour
|
| 1:00 - 2:30 |
Creating Effective Visualizations Using Graph Builder
Xan Gregg |
The Essential Modeling Strategies and Techniques of JMP Pro Made Easy
Chris Gotwalt
|
Integrated Process Improvement Using the Second-Generation Quality Tools in JMP®
Laura Lancaster |
| 3:00 - 4:30 |
Building Dashboards and Applications
Dan Schikore |
Sharing the Ultimate Boon: A Journey from Modeling to Scoring
Nascif Abousalh-Neto, Mia Stephens, Julian Parris
|
Case Studies in Data Visualization for Analysis
Richard Zink and Kelci Miclaus
|
Using JSL to Develop Efficient, Robust Applications
Tuesday, 17 October, 8:30 – 10:00 a.m.
Joseph Morgan, JMP Senior Research Statistician Developer, SAS
This tutorial 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.
Variable Selection Made Easy Using the Generalized Regression Platform in JMP® Pro
Tuesday, 17 October, 8:30 – 10:00 a.m.
Chris Gotwalt, JMP Director of Statistical Research and Development, SAS
Variable selection is the process of selecting a subset of relevant variables or predictors to use in modeling the response. It is a crucial task that yields simpler models that generalize better to new data, but it does not have to be a difficult process. The Generalized Regression ("Genreg") platform in JMP Pro enables you to do variable selection quickly, easily and interactively in a variety of settings (including least squares, logistic and Poisson regression). We will review the variable selection techniques available within Genreg (forward selection, Lasso and more) and then look at how the platform makes these techniques easy to employ. Examples will range from designed experiments to predictive modeling.
Reliability Analysis Using JMP® 13
Tuesday, 17 October, 8:30 – 10:00 a.m.
Peng Liu, JMP Principal Research Statistician Developer, SAS
Leo Wright, JMP Principal Product Manager, SAS
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 tutorial, we will cover the following JMP platforms: Life Distribution, Reliability Forecasting, Reliability Block Diagram, 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. The tutorial will start with an introduction to the censored data and will outline different objectives of reliability analysis and engineering. The instructors will address individual objectives by using examples included with JMP software.
Wrangling All Your Data With Query Builder in JMP® 13
Tuesday, 17 October, 10:30 a.m. – 12:00 p.m.
Eric Hill, JMP Principal Systems Developer, SAS
JMP 12 saw the addition of the Query Builder, a tool designed to vastly improve the experience of retrieving data from relational databases into JMP in analysis-ready form. In JMP 13, we have gone a step further: Query Builder can now be used to manipulate your JMP data tables. So, whether your data is coming from Excel, text files, a relational database, JMP data tables, or all of the above, Query Builder can now be your one tool for managing and bringing together all of this data. This tutorial will demonstrate several scenarios to help you get started and achieve proficiency.
Fitting Definitive Screening Designs (DSDs), Taking Advantage of Their Structure
Tuesday, 17 October, 10:30 a.m. – 12:00 p.m.
Ryan Lekivetz, Sr Research Statistician Developer, SAS
DSDs were introduced in 2011 and quickly became popular due to their many desirable features. One such feature is that the main effects of DSDs are uncorrelated with each other and also uncorrelated to all two-factor interactions and quadratic effects. Taking advantage of this structural feature of DSDs allows for fitting them in two independent steps. This tutorial provides a “peek into the black box” of this new fitting method that is built in for JMP 13. We will provide an add-in for attendees using earlier versions of JMP.
Exploiting the Formula Editor in JMP® 13
Tuesday, 17 October, 10:30 a.m. – 12:00 p.m.
Mike Muhlada, JMP Development Tester, SAS
Ernest Pasour, JMP Principal Software Developer, SAS
This introductory tutorial 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 walk-through will cover various aspects of the formula builder, including the use of drag and drop, keyboard shortcuts, and function buttons to create formulas. As we work through the examples, we will discuss useful functions, uncover and correct common mistakes, and show how to use the log file for debug, data type mismatches, subscripts, and more. Best practices to be covered include: custom formats, using formulas for data cleanup, column transforms, looping efficiency, and using intermediate columns to build more complex formulas.
Creating Effective Visualizations Using Graph Builder
Tuesday,17 October, 1:00 – 2:30 p.m.
Xan Gregg, JMP Director of Research and Development, SAS
This tutorial will teach basic through advanced use of JMP Graph Builder, both from an exploration perspective and a presentation perspective. We will start with the basic components: variables, roles, elements, legends, scales, and the properties of each, as well as how these components work together. We’ll cover common visualizations (points, lines and bars) and their variations, as well as specialized visualizations including filtered views, geographic maps, heat maps, contours, overlays and small multiples. From there, we’ll cover how to create rich displays within Graph Builder by reshaping data and combining graphic elements.
The Essential Modeling Strategies and Techniques of JMP® Pro Made Easy
Tuesday, 17 October, 1:00 – 2:30 p.m.
Chris Gotwalt, JMP Director of Statistical Research and Development, SAS
JMP Pro is a powerful tool for analyzing data, but can appear a bit intimidating to the new user. In this tutorial we show how validation methods streamline the modeling process and make modeling techniques with fancy names approachable and easy to use. Following a few simple guidelines and best practices leads to a simple procedure for trying a variety of models and then either choosing the best one or reusing the data to combine the models into a new one that is superior to each of the individual ones. We will begin with an overview of how to use Validation columns in JMP Pro in combination with a handful of easy-to-understand diagnostic graphs and metrics that are useful for evaluating and comparing models. Then we will apply these ideas to the set of predictive models in JMP Pro that I have found to be the most useful: Regression Trees, Bootstrap Forest, Boosted Tree, Neural Networks, and Generalized Regression. We give an explanation of each of these models in the simplest, most accessible terms possible, and then give clear guidance on how to use them in JMP Pro, while focusing on only the most useful options. At the end of the tutorial we will also give a brief introduction to the Text Explorer platform with an example that shows how to use it in combination with some of the models covered earlier in the presentation to build text regression models and perform a derived sentiment analysis.
Integrated Process Improvement Using the Second-Generation Quality Tools in JMP®
Tuesday, 17 October, 1:00 – 2:30 p.m.
Laura Lancaster, JMP Principal Research Statistician Developer, SAS
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 tutorial will use a case study to show how to use each of these platforms with an integrated process improvement approach.
Building Dashboards and Applications
Tuesday, 17 October, 3:00 – 4:30 p.m.
Dan Schikore, JMP Principal Software Developer, SAS
This tutorial will take you through the process of combining multiple reports using JSL or the JMP Application Builder, so that the process can be repeated on the same or different data tables. You will learn how to enhance your application using data filters and parameterization, as well as manage your end-to-end workflow with import from sources such as the SQL Query Builder and output to interactive HTML. You will also learn about the new dashboard features in JMP 13, with summary report views and drag-and-drop arrangement of live reports.
Sharing the Ultimate Boon: A Journey from Modeling to Scoring
Tuesday, 17 October, 3:00 – 4:30 p.m.
Nascif Abousalh-Neto, JMP Principal Software Developer, SAS
Mia Stephens, JMP Academic Ambassador, SAS
Julian Parris, JMP Academic Ambassador, SAS
Finding the right model to predict new outcomes given new data is an important accomplishment. But many times it is a step in a journey where the final goal is to share the model’s predictive power with a much larger audience. JMP Pro has many tools to facilitate fitting, comparison, and selection of predictive models. JMP Pro 13 added the Formula Depot: an efficient way to collect models, apply them to new tables, and access the Model Comparison and Profiler platforms. To help with deploying models to production, the Formula Depot can also convert them to scoring code in a number of different programming languages. In this tutorial we use a real estate case study to illustrate the predictive modeling workflow. We’ll compile data, prepare the data for modeling, generate predictive models, publish models to the Formula Depot, and explore and select the best model(s). Then, we’ll generate scoring code to support the creation of web applications that can calculate housing prices “on the spot.” We will also explore different methods for scoring data, and provide an overview of current deployment architectures.
Case Studies in Data Visualization for Analysis
Tuesday, 17 October, 3:00 – 4:30 p.m.
Richard Zink, JMP Principal Research Statistician Developer, SAS
Kelci Miclaus, JMP Life Sciences Advanced Analytics R&D Manager, SAS
Statisticians are extremely effective at analyzing data, performing simulations, and generating pages of analysis results. Despite their analytical prowess, however, statisticians continue to struggle to communicate the story hidden within the data to their colleagues. First and foremost, with the high cost of conducting translational clinical research, it is common to collect as much data as possible on as many endpoints as possible. A second challenge to communication lies in the increased use of sensitivity analyses to assess the consistency and robustness of results to varying assumptions. Given the volume of data to review and the variety of analyses to perform, it should come as no surprise that clear insight is often out of reach. In this environment, the traditional means of data summary – tables and listings – are ineffective for gaining insight; visualization is the key to effective communication for the modern statistician. Ben Shneiderman stated that “the purpose of visualization is insight.” Therefore, the goal of this short course is to describe data visualization techniques to aid in the understanding and communication of results from applications in clinical trials and genomics research.
Understanding Reliability with JMP 13
JMP’s state-of-the-art suite of tools for studying reliability is designed to enable organizations to provide superior products required for success in today’s arena of complex products and services. For many years, we have been incorporating cutting edge concepts to integrate robust statistical analysis with dynamic data visualization that allows users to 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 tutorial, the following platforms within JMP’s suite will be covered: Life Distribution, Reliability Forecasting, Reliability Block Diagram, 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. The tutorial will start with an introduction to the censored data and will outline different objectives of reliability analysis and engineering. The instructors will address individual objectives by using examples included with the JMP software.
