Course: JMP®: Reliability Analysis for Non-Repairable Systems
Duration: 4 half-day sessions
Registration Fee: $400 US
Description: This course is for anyone who needs to analyze data, using JMP, about how long an object (reliability) or person (survival) operates within acceptable parameters ("time to event"). The course is presented using manufacturing examples, but those interested in survival analysis or studying recidivism will also find the course useful.
Overview: Learn how to:
- Distinguish unique characteristics of life data.
- Compute non-parametric (Kaplan-Meier product-limit) estimates of failure probability.
- Fit distribution models specific to life data.
- Estimate reliability or survival measures and hazard.
- Estimate survival in the presence of competing causes.
- Use parametric survival models to estimate effects of covariates or experimental factors.
- Design an accelerated life test.
Prerequisites: Before attending this course, it is recommended that you complete the JMP® Software: A Case Study Approach to Data Exploration and JMP®: Statistical Decisions Using ANOVA and Regression courses or have equivalent experience.
Course Outline:
Introduction to Reliability
- Understanding principles of reliability, measures of reliability, and the nature of life data and censored observations.
- Using nonparametric estimation of failure probabilities.
- Applying parametric models of failure reliability and hazard.
- Accounting for uncertainty about reliability estimates.
- Determine sufficient sample size for reliability determination.
Reliability with Factors
- Identifying failure modes and competing causes.
- Fitting mixture models.
- Fitting competing risk mixture models.
- Describing deficient data.
- Including the effect of covariates in the reliability model.
Rapid Reliability
- Understanding stress factors and accelerated failures.
- Computing acceleration factors.
- Using acceleration relationships.
- Designing accelerated life tests.
- Performing degradation analysis for repeated measures and destructive tests.
Course: JMP® Pro: Analyzing Curves and Profiles Using the Functional Data Explorer
Duration: 4 half-day sessions
Registration Fee: $400 US
Description: This course helps you recognize and model functional data using JMP Pro. It teaches 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 are defined as a function, profile, or curve. They are 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.
Overview:
Learn how to:
- Recognize functional data and their uses.
- Preprocess data to suit the functional data analysis.
- Select a model of the functions.
- Obtain optimal representations of the functions.
- Use the new representations as the responses in the analysis of a designed experiment.
Who should attend?
Directors, managers, engineers, scientists, technicians, and analysts who work with functional data to design, develop, improve, and optimize a product or process
Prerequisites: Before attending this course, it is recommended that you complete the JMP® Software: A Case Study Approach to Data Exploration and JMP®: Statistical Decisions Using ANOVA and Regression courses or have equivalent experience.
Course Outline:
Explore Functional Data
- Define functional data and their forms.
Preprocess Functional Data
- Identify different formats of functional data.
- Clean up functional data (recoding tasks).
- Transform functional data output (centering and scaling, other common transformations).
- Align functional data input.
Fit Models of Functional Data
- Describe a basis function expansion as a model for a continuous function.
- Optimize trade-off between model bias and variance with smoothing.
- Select splines or Fourier basis for the model.
- Fit the model.
- Select the best model.
- Diagnose the model.
Extract Functional Principal Components
- Describe the principal components analysis of continuous functions.
- Derive and use functional principal component scores.
Functional Analysis of Experiments
- Identify the goals of the analysis of an experiment.
- Estimate factor effects on the functional principal components.
Capstone Case Studies
- Select one of several new cases chosen from different industries to practice a complete functional data analysis.
Course: JMP®: Statistical Decisions Using ANOVA and Regression
Duration: 4 half-day sessions
Registration Fee: $400 US
Description: This course teaches you how to use analysis of variance and regression methods to analyze data with a single continuous response variable, and introduces statistical model building. You learn how to perform elementary exploratory data analysis (EDA) and discover natural patterns in data. Important statistical concepts such as confidence intervals and hypothesis testing are introduced and applied. The course also covers principles of model building, including model interpretation and addressing violations of statistical assumptions. Capstone practices at the end of the course allow students to apply their knowledge.
Overview:
Learn how to:
- Interpret confidence intervals.
- Perform hypothesis tests and interpret p-values.
- Explore relationships with scatterplots and correlation statistics.
- Compare multiple population means with one-way ANOVA.
- Use simple linear regression to analyze relationships between continuous variables.
- Use the general linear model to build models between a continuous response and any number of continuous or categorical predictors.
- Assess interactions between factors and curvature.
- Evaluate assumptions of statistical hypothesis testing.
Prerequisites: Before attending this course, it is recommended that you complete the JMP® Software: A Case Study Approach to Data Exploration course or have equivalent experience.
Course Outline:
Introduction to Statistics
- Statistical concepts.
- Descriptive statistics and some of their graphs.
- Inferential statistics.
- Hypothesis tests.
- One-sample t test.
Analysis with a Categorical Factor
- One-way ANOVA.
- Multiple comparisons.
- Power and sample size.
Analysis with a Continuous Factor
- Exploring relationships.
- Simple linear regression.
- Polynomial regression.
Model Building
- Introduction to model building.
- Combining factors.
- Model interpretation.
- When things go wrong.
Additional Resources
- Paired t test.
- Alternatives to ANOVA when assumptions are violated.
- Contrasts in n-way ANOVA.
- Equivalence testing.
Course: JMP® Pro: Predictive Modeling
Duration: 4 half-day sessions
Registration Fee: $400 US
Description: This course covers the skills required to develop, assess, tune, compare, and score predictive models using JMP Pro software. This course teaches you how to build and understand predictive models using machine learning techniques such as generalized regression models, k-nearest neighbors, naïve Bayes, support vector machines, decision trees, and neural networks. You will also learn how to validate predictive models using cross-validation, holdback validation, and information-theoretic criteria.
Overview:
Learn how to:
- Develop, compare and explain complex models.
- Use the partition platform for predictive modeling including bagging, bootstrap forest, boosted trees.
- Use neural networks for predictive modeling including k-Fold cross validation, multi-layer neural networks, and boosting.
- Tune predictive models.
- Score new data in JMP and generate score code for use in other software.
- Deploy predictive models to production.
Prerequisites: Before attending this course, it is recommended that you complete the JMP® Software: A Case Study Approach to Data Exploration and JMP®: Statistical Decisions Using ANOVA and Regression courses or have equivalent experience.
Course Outline:
Introduction to Predictive Modeling
- How is predictive modeling different?
- Importing data and cleanup.
- Generalization and honest assessment.
Overview of Predictive Modeling Platforms
Predictive Modeling with the Partition Platform
- Recursive partitioning.
- Bagging.
- Bootstrap forest.
- Boosted tree models.
- Tuning,
Predictive Modeling with Neural Networks
- Fitting a neural network model.
- Neural network architectures.
- k-Fold cross validation.
- Boosted neural networks.
- Tuning.
Choosing and Deploying Predictive Models
- Model comparisons.
- Ensemble models.
- Formula depot.
Course: JMP®: Custom Design of Experiments
Duration: 4 half-day sessions
Registration Fee: $400 US
Description: This course focuses on the core principles of designing an experiment, enabling you to understand and apply those principles to achieve an optimal design using the Custom Design platform in JMP. Custom design is an approach to designing experiments that produces optimal designs for the problem you’re trying to solve, whether that’s identifying important effects, or trying to optimize one or more responses. In addition to learning about custom design in JMP, you’ll explore key design concepts including sample size and power, balance, choice of factor ranges, blocking, and design evaluation. This course is for anyone who works in discovery, research, development, and quality assurance or control.
Overview:
Learn how to:
- Use custom design for any experiment.
- Choose appropriate criteria for optimal design.
- Effectively and efficiently test factor effects or predict responses.
- Augment existing experiments to address new questions.
- Design and analyze experiments with hard-to-change factors.
- Find the best factor settings to achieve desired response levels.
Prerequisites: Before attending this course, it is recommended that you complete the JMP® Software: A Case Study Approach to Data Exploration and JMP®: Statistical Decisions Using ANOVA and Regression courses or have equivalent experience.
Course Outline:
Introduction
- Introduction to design of experiments.
- Introduction to custom design.
- About responses.
Experiments with One Factor
- Testing a categorical factor.
- Testing a continuous factor.
- Validating the model.
- Testing a quadratic effect.
Experiments with Two Factors
- Testing two categorical factors.
- Testing one continuous factor and one categorical factor.
- Testing with one blocking factor.
- Testing two continuous factors.
Experiments with Multiple Factors
- Two responses to five factors.
- Three responses to seven factors.
Course: JMP®: The JMP® Scripting Language
Duration: 5 half-day sessions
Registration Fee: $500 US
Description: This course is for JMP users who want to extend JMP software's functionality using the JMP Scripting Language (JSL) to automate routine tasks, extend or create new procedures, and customize reports. This course covers the basics of scripting with JSL and then progresses to more advanced topics, including working with data tables, using matrices to facilitate computations, scripting analyses, and capturing results to make custom reports. The course also presents suggested best practices throughout. A Self-Study lesson at the end of the course illustrates creating and using dialog boxes to adapt script behavior, and saving JMP scripts as JMP add-ins to make scripts available on demand and facilitate deployment. This course can help prepare you for the following certification exam(s): JMP Scripting using JMP.
Overview:
Learn how to:
- Use the basic elements in JSL, including variables, functions and operators, character strings, lists, and matrices.
- Capture scripts that are automatically generated by JMP and incorporate them in your own scripts.
- Communicate with objects in JMP using messages.
- Use iterative and conditional functions.
- Delay evaluation of expressions using Expr( ).
- Open data tables and import files from other sources.
- Create and work with new data tables and columns.
- Launch analyses and capture or modify reports.
Who should attend
Anyone familiar with JMP who wants to learn the JMP Scripting Language (JSL)
Prerequisites: Before attending this course, it is recommended that you complete the JMP® Software: A Case Study Approach to Data Exploration course or have equivalent experience.
Course Outline:
Introducing the JMP Scripting Language
- Introduction.
- Object-oriented scripting.
JSL Building Blocks
- Basic elements.
- Lists and matrices.
- Script Editor and style.
Script Control
- Iteration.
- Conditional action.
Scripting Data Tables
- Opening data tables.
- Working with columns and rows.
- Using data tables and matrices.
- Using metadata.
- Manipulating data tables.
Scripting Platforms
- Scripting analyses.
- Accessing reports.
- Customizing reports.
Adapting Scripts (Self-Study)
- Using dialog boxes.
- Substituting in expressions.
- Customizing the menu.
Advanced Topics
- Searching character strings.
- Using associative arrays.
Course: JMP®: A Case Study Approach to Data Exploration
Duration: 2 half-day sessions
Registration Fee: $200 US
Description: This course is designed as the first step for those who want to use JMP to explore, manage, and analyze data. It is recommended as a prerequisite for many of our other courses. The course begins with an introduction to the JMP interface with its key tools and features, followed by two case studies that present common data issues that students encounter. Each case study demonstrates the elements of a typical workflow for data exploration. This course will help you prepare to use JMP in more advanced courses covering statistical analysis and modeling, design of experiments and quality control, or the JMP Scripting Language.
Overview:
Learn how to:
- Import data into JMP.
- Explore the data with graphs and tables.
- Identify and correct data problems.
- Create new columns with formulas.
- Save and share graphical and numerical reports.
Prerequisites: No previous JMP knowledge or statistical knowledge is required. Before attending this course, you should be familiar with a macOS or Microsoft Windows operating system.
Course Outline:
Introduction
- Introduction to JMP.
- Exploring data.
Process Change Case Study
- Importing data.
- Creating new columns.
- Presenting and sharing results.
Process Monitoring Case Study
- Importing data.
- Creating new columns.
- Generating and saving results.
Manufacturing Case Study (Self-Study)
- Importing data.
- Creating new tables and columns.
- Generating and saving results.
Course: JMP®: Statistical Process Control
Duration: 4 half-day sessions
Registration Fee: $400 US
Description: This course teaches you how to set up and maintain a statistical process control system using JMP software.
Overview:
Learn how to:
- Describe variation.
- Generate and interpret basic quality charts.
- Generate and interpret control charts for measurement data.
- Improve control chart performance.
- Calculate and interpret process capability indices.
- Monitor many variables at once.
- Handle data that does not meet assumptions, such as batch processing, correlated data, attribute data, or multivariate data.
Prerequisites: Before attending this course, it is recommended that you complete the JMP® Software: A Case Study Approach to Data Exploration course or have equivalent experience.
Course Outline:
Introduction
- Historical perspective of statistical process control.
- Variation.
- Data format.
- Basic quality tools (optional) .
Control Charts for Measurement Data
- Control chart builder.
- X-MR charts.
- XBar-R and XBar-S charts.
- Additional tests.
- Phases.
Process Capability
- Process capability.
- Process capability platform.
- Cautions.
- Manage specific limits.
Special Situations
- Attribute charts.
- Multiple sources of variation.
- CUSUM and EWMA control charts.
- Multivariate control charts.
- Process screening platform.
Course: Quality by Design (QbD) Using JMP®
Duration: 5 half-day sessions
Registration Fee: $500 US
Description: Business Knowledge Series course. Presented by Heath Rushing, co-founder and Principal Consultant, Adsurgo LLC. This course focuses on how to establish a systematic approach to pharmaceutical development that is defined by Quality-by-Design (QbD) principles using design of experiments (DOE). In addition, this course teaches the application of statistics for setting specifications, assessing measurement systems (assays), developing a control plan as part of a risk management strategy, and ensuring process control/capability. All concepts are taught within the product quality system framework defined by requirements in regulatory guidance documents. Analyses in this course use the point-and-click interface of JMP software.
Overview:
Learn how to:
- Implement QbD principles from discovery through product discontinuation.
- Apply statistics to set specifications and validate measurement systems (assays).
- Use risk management tools to identify and prioritize potential critical process parameters.
- Identify critical process parameters and develop a functional relationship between those process parameters and your critical-to-quality attributes (CQAs).
- Establish your design space.
- Develop a control plan as part of a risk management strategy.
- Ensure that your process is in (statistical) control and capable.
Prerequisites: Before attending this course, it is recommended that you complete the JMP® Software: A Case Study Approach to Data Exploration and JMP®: Statistical Decisions Using ANOVA and Regression courses or have equivalent experience.
Course Outline:
Introduction to Quality by Design (QbD)
- Quality by Design (QbD) principles.
- Product Quality System framework.
Primer on Statistical Analysis
- Basic statistics.
- Hypothesis testing.
- ANOVA and regression.
- Blocking.
Foundational Requirements for QbD Studies
- Setting specifications.
- Measurement Systems Analysis (MSA) for assays.
Introduction to Design of Experiments (DOE)
- Steps to DOE.
- Defining critical-to-quality attributes (CQAs).
- Identifying and prioritizing potential process parameters.
Screening Designs
- Factorial designs.
- Fractional factorial designs.
- D-optimal designs.
Response Surface Designs
- Addition of center points.
- Face-centered CCD.
- Box-Behnken Design.
- I-optimal designs.
Specialized Designs
- Definitive screening designs.
- A-optimal designs.
Using Systematic Understanding from QbD Studies
- Presenting results.
- Developing a control plan as part of a risk management strategy.
- Process control and capability.
Advanced DOE Methodologies (Optional)
- Split-plot designs.
- Robust parameter design.
- Supersaturated designs.
- Constrained designs.
- Mixture designs.
Course: JMP®: Measurement System Analysis
Duration: 3 half-day sessions
Registration Fee: $300 US
Description: This course teaches you how to determine the measurement error associated with your process, including both measurement system variability and bias, using JMP software.
Overview:
Learn how to:
- Describe measurement variation.
- Design studies to estimate both bias and variability of a measurement system.
- Estimate repeatability and reproducibility.
- Use graphical analysis to understand measurement error.
- Estimate bias and linearity.
- Perform attribute gauge studies.
Prerequisites: Before attending this course, it is recommended that you complete the JMP® Software: A Case Study Approach to Data Exploration and JMP®: Statistical Decisions Using ANOVA and Regression courses or have equivalent experience.
Course: JMP® and JMP Pro: Finding Important Predictors
Duration: 4 half-day sessions
Registration Fee: $400 US
Description: This course teaches you techniques for fitting statistical models to identify important variables. Manual, graphical, and automated variable selection techniques are presented, along with advanced modeling methods. The demonstrations include modeling both designed and undesigned data. Techniques are illustrated using both JMP software and JMP Pro software. Note that JMP Pro software is needed for the advanced techniques covered in the second half of this course.
Overview:
Learn how to:
- Identify a subset of predictors as important using a statistical model.
- Validate statistical models using cross-validation, holdback validation, and information-theoretic criteria.
- Perform stepwise and all subsets regression.
- Select important predictors using graphical methods and decision trees.
- Perform penalized regression for Gaussian and non-Gaussian responses.
- Use the Generalized Regression platform to identify important predictors.
Who should attend
Analysts, researchers, technicians, or anyone filling similar roles, who want to determine which predictors in a large set are important in predicting a response
Prerequisites: Before attending this course, you should complete the JMP®: Statistical Decisions Using ANOVA and Regression course.
Course Outline:
Introduction to Variable Selection
- Introduction to models.
- Model validation methods.
- Variable importance.
- OLS regression and backward selection.
Classic Methods of Variable Selection
- Graph-based methods.
- Stepwise regression.
- All subsets regression.
Generalized Regression (JMP Pro Required)
- Introduction.
- Lasso/ridge/elastic net.
- Adaptive lasso/adaptive elastic net.
- Methods with multiple passes.
Special Situations for Generalized Regression (JMP Pro Required)
- Binomial response.
- Poisson response.
- Multiple responses.
Course: JMP®: Graphing and Reporting with JMP®
Duration: 2 half-day sessions
Registration Fee: $200 US
Description: This course is designed for anybody who wants to create compelling graphs, reports, and dashboards that effectively share the story in their data.
Overview:
Learn how to:
- Create effective and compelling visualizations with JMP Graph Builder.
- Import and query data to enable tables and graphs.
- Share results with others via reports and dashboards.
Who should attend
Anybody who wants to use JMP to graph and report their data
Prerequisites: No previous JMP knowledge or statistical knowledge is required. Before attending this course, you should be familiar with a macOS or Microsoft Windows operating system.
Course Outline:
Getting Started and Graph Building
- Getting started in JMP.
- Graphing data in Graph Builder.
- Geographic mapping in JMP.
- Filters and the Column Switcher.
- Effective visualizations.
Importing and Querying Data
- Importing and cleaning data.
- Using the JMP Query Builder.
- Creating detailed and summary tables.
Creating Reports and Dashboards
- Generating JMP reports.
- Introduction to JMP applications.
- Using JMP Dashboard Builder.
- Sharing reports and dashboards.
Course: JMP®: Designing and Building a Complete JMP® Script
Duration: 4 half-day sessions
Registration Fee: $400 US
Description: This course teaches you how to approach writing a new script in a methodical way. A realistic case study is used to illustrate the typical steps. A separate case study is developed in the course exercises. A storyboard showing the results from the script, the JMP objects that will be used by the script, and the steps necessary for the script to carry out is developed at the beginning of the course, and it is further developed throughout the course. The results are used to determine the necessary data, so the next step is identifying the analysis variables and their original data stores. Techniques to import data, cleanse and harmonize data, and derive new variables complete the first half of the course. The second half of the course teaches how to script the conversion of the variables into results. JMP objects are used to create a working framework. Each object is then customized or extended as necessary. Optional results or computations are captured in a user dialog to finish the script. This course can help prepare you for the following certification exam(s): JMP Scripting using JMP.
Overview:
Learn how to:
- Design and build a script end to end.
- Identify data sources.
- Import data from flat files and Excel workbooks.
- Clean and transform data.
- Build a presentation from JMP objects.
- Customize objects for final results.
Prerequisites: Before attending this course, you should complete the JMP®: Introduction to the JMP® Scripting Language course or have equivalent experience.
Course Outline:
Script Design
- introduction.
- identifying the data.
Data Import Design
Data Preparation
- Formatting and shaping.
- Data cleansing.
- Transformations.
Building Analytics, Reports, and Visualizations
- Building the framework.
- Building the expressions.
- Capstone and best practices.
Script the Second Analysis (Appendix)
- Script design.
- Data import design.
- Data preparation.
- Building analytics, reports, and visualizations.
Database Access (Appendix)
- Importing data stored in a relational database.
