Learn JMP Events

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Monday, November 14, 2022

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. Duration: 5 half-day sessions Registration Fee: $500 US Learn more, and register, at Intro to the JMP Scripting Language - upcoming class.

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Monday, January 30, 2023

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. 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. Duration: 4 half-day sessions Registration Fee: $400 US Learn more, and register, at JMP® Pro: Finding Important Predictors.

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Monday, February 6, 2023

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. 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. Duration: 2 half-day sessions Registration Fee: $200 US Learn more, and register, at JMP®: A Case Study Approach to Data Exploration.

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Monday, February 27, 2023

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. 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. Duration: 4 half-day sessions Registration Fee: $400 US Learn more, and register, at JMP®: Statistical Decisions Using ANOVA and Regression.

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Monday, March 27, 2023

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. 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. Duration: 4 half-day sessions Registration Fee: $400 US Learn more, and register, at JMP®: Custom Design of Experiments.

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Monday, April 3, 2023

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. 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. Duration: 5 half-day sessions Registration Fee: $500 US Learn more, and register, at The JMP® Scripting Language.

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Tuesday, April 4, 2023

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. 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. Duration: 2 half-day sessions Registration Fee: $200 US Learn more, and register, at JMP®: A Case Study Approach to Data Exploration.

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Monday, April 17, 2023

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. 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. Duration: 4 half-day sessions Registration Fee: $400 US Learn more, and register, at JMP® Pro: Predictive Modeling.

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Monday, April 24, 2023

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. 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. Duration: 4 half-day sessions Registration Fee: $400 US Learn more, and register, at JMP®: Statistical Decisions Using ANOVA and Regression.

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Monday, May 1, 2023

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. 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. Duration: 4 half-day sessions Registration Fee: $400 US Learn more, and register, at JMP®: Reliability Analysis for Non-Repairable Systems.

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Monday, May 22, 2023

This course teaches you how to set up and maintain a statistical process control system using JMP software. 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. Duration: 4 half-day sessions Registration Fee: $400 US Learn more, and register, at JMP®: Statistical Process Control.

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Tuesday, May 30, 2023

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. 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. Duration: 4 half-day sessions Registration Fee: $400 US Learn more, and register, at JMP® Pro: Analyzing Curves and Profiles Using the Functional Data Explorer.

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Monday, June 5, 2023

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. 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. Duration: 4 half-day sessions Registration Fee: $400 US Learn more, and register, at Designing and Building a Complete JMP® Script.

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Tuesday, June 27, 2023

2 Day Course Tuesday, June 27 | 1:00 - 4:30 p.m. ET Wednesday, June 28 | 1:00 - 4:30 p.m. ET 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. 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. Duration: 2 half-day sessions Registration Fee: $200 US Learn more, and register, at JMP®: A Case Study Approach to Data Exploration.

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Tuesday, July 11, 2023

3 Day Course Tuesday, July 11 | 9:00 a.m. - 12:30 p.m. ET Wednesday, July 12 | 9:00 a.m. - 12:30 p.m. ET Thursday, July 13 | 9:00 a.m. - 12:30 p.m. ET This course teaches you how to determine the measurement error associated with your process, including both measurement system variability and bias, using JMP software. 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. Duration: 3 half-day sessions Registration Fee: $300 US Learn more, and register, at JMP®: Measurement System Analysis.

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Monday, July 17, 2023

4 Day Course Monday, July 17 | 9:00 a.m. - 12:30 p.m. ET Tuesday, July 18 | 9:00 a.m. - 12:30 p.m. ET Wednesday, July 19 | 9:00 a.m. - 12:30 p.m. ET Thursday, July 20 | 9:00 a.m. - 12:30 p.m. ET 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. 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. Duration: 4 half-day sessions Registration Fee: $400 US Learn more, and register, at JMP®: Custom Design of Experiments.

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Monday, September 11, 2023

4 Day Course Monday, September 11 | 9:00 a.m. - 12:30 p.m. ET Tuesday, September 12 | 9:00 a.m. - 12:30 p.m. ET Wednesday, September 13 | 9:00 a.m. - 12:30 p.m. ET Thursday, September 14 | 9:00 a.m. - 12:30 p.m. ET 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. 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. Duration: 4 half-day sessions Registration Fee: $400 US 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. Learn more, and register, at JMP® Pro: Finding Important Predictors.

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Monday, October 23, 2023

4 Day Course Monday, October 23 | 9:00 a.m. - 12:30 p.m. ET Tuesday, October 24 | 9:00 a.m. - 12:30 p.m. ET Wednesday, October 25 | 9:00 a.m. - 12:30 p.m. ET Thursday, October 26 | 9:00 a.m. - 12:30 p.m. ET 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. 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. Duration: 4 half-day sessions Registration Fee: $400 US 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. Learn more, and register, at JMP® Pro: Analyzing Curves and Profiles Using the Functional Data Explorer.

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Monday, November 13, 2023

5 Day Course Monday, November 13 | 9:00 a.m. - 12:30 p.m. ET Tuesday, November 14 | 9:00 a.m. - 12:30 p.m. ET Wednesday, November 15 | 9:00 a.m. - 12:30 p.m. ET Thursday, November 16 | 9:00 a.m. - 12:30 p.m. ET Friday, November 17 | 9:00 a.m. - 12:30 p.m. ET 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. 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. Duration: 5 half-day sessions Registration Fee: $500 US Who should attend: Anyone familiar with JMP who wants to learn the JMP Scripting Language (JSL). Learn more, and register, at JMP®: The JMP® Scripting Language.

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Monday, February 5, 2024

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. 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. Duration: 4 half-day sessions (Feb. 5-8, 1:00 p.m. - 4:30 p.m. ET) Registration Fee: $400 US Learn More and Register: JMP: Statistical Decisions Using ANOVA and Regression

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Monday, February 19, 2024

4 Day Course Monday, February 19 | 1:00 p.m. - 4:30 p.m. ET Tuesday, February 20 | 1:00 p.m. - 4:30 p.m. ET Wednesday, February 21 | 1:00 p.m. - 4:30 p.m. ET Thursday, February 22 | 1:00 p.m. - 4:30 p.m. ET This course is for JMP users who deal with mixture or formulation experiments. The course demonstrates how to use various approaches to create an appropriate experimental design for commonly encountered mixture situations. The analysis of mixture experiments is also covered, including finding the optimum formulation. Learn how to: Use factorial-type designs in mixture scenarios. Design mixture experiments using traditional and advanced design strategies. Properly analyze and interpret formulation experiments. Characterize response behavior over the formulation space with a model. Find optimal formulations. Duration: 4 half-day sessions Registration Fee: $400 US Learn more, and register, at JMP®: Design and Analysis of Mixture Experiments

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Tuesday, March 19, 2024

4 Day Course Tuesday, March 19 | 1:00 p.m. - 4:30 p.m. ET Wednesday, March 20 | 1:00 p.m. - 4:30 p.m. ET Thursday, March 21 | 1:00 p.m. - 4:30 p.m. ET Friday, March 22 | 1:00 p.m. - 4:30 p.m. ET 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. 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. Duration: 4 half-day sessions Registration Fee: $400 US Learn more, and register, at JMP®: Reliability Analysis for Non-Repairable Systems.

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Monday, April 1, 2024

4 Day Course Monday, April 1 | 1:00 p.m. - 4:30 p.m. ET Tuesday, April 2 | 1:00 p.m. - 4:30 p.m. ET Wednesday, April 3 | 1:00 p.m. - 4:30 p.m. ET Thursday, April 4 | 1:00 p.m. - 4:30 p.m. ET 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. 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. Duration: 4 half-day sessions Registration Fee: $400 US Learn more, and register, at JMP® Pro: Predictive Modeling.

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Monday, April 15, 2024

4 Day Course Monday, April 15 | 1:00 p.m. - 4:30 p.m. ET Tuesday, April 16 | 1:00 p.m. - 4:30 p.m. ET Wednesday, April 17 | 1:00 p.m. - 4:30 p.m. ET Thursday, April 18 | 1:00 p.m. - 4:30 p.m. ET This course teaches you how to analyze discrete (or categorical) data or outcomes using association, contingency tables, stratification, correspondence analysis, logistic regression, generalized linear models, partitioning, and artificial neural network models. Learn how to: Examine associations among variables Perform chi-square and Fisher exact tests Perform stratified analysis Perform correspondence analysis Perform logistic regression Interpret logistic regression output Fit a binary response and a count of events with generalized linear models (GLM) Fit a decision tree model Fit an artificial neural network model. Duration: 4 half-day sessions Registration Fee: $400 US Learn more, and register, at JMP®: Analyzing Discrete Responses.

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