Manchester, UK 5-7 March
Add-ons

You have a business or research question, you’ve collected or found appropriate data, and you are ready to analyze. But which analytical methods should you try? How will your build your models? Do you need interactions? Polynomials? How will you choose model terms? In this session, we will look at several data scenarios and present modeling options and a framework for comparison. We will look at how different questions or goals affect the modeling choices we make. (Predict? Explain? Find associations?) Models covered will include methods found in the “Fit Y by X,” “Fit Model,” “Predictive Modeling,” “Screening,” “Multivariate Methods,” and “Clustering” menus in JMP.

Are you maximizing your use of JMP software? Could there be a feature or tool perfect for your business problem that you aren’t yet using because you don’t know when to use it, or even that it exists? Join us for a tour of the capabilities in JMP, including examples and use-cases to show when and why you might use various JMP platforms. You may discover a new tool or method that could make a huge impact in your business!

This hands-on session is for JMP users who have at least a basic familiarity with design of experiments and would like to learn more about using the Easy DOE platform. Easy DOE provides a workflow framework to assist in the design and analysis of experiments, using either Guided Mode, with prompts and tips at each step, or Flexible Mode, with access to all of the features of JMP’s DOE platforms. The session uses examples to demonstrate both modes in Easy DOE.

In this session, we explore how to determine how much variation in your process comes from various sources. The methods are applicable to any process with either controllable or uncontrollable factors, including manufacturing processes, measurement processes, or others. We discuss statistical models containing both random and fixed factors, as well as crossed and nested factors. Next, we design a study to support the model. After collecting data, we highlight special features in Graph Builder to visualize these sources of variation. Finally, we discuss how to interpret the model results and what those results mean for process improvement or control.

In this session, we explore how experimentation on mixtures or formulations is different from usual process experimentation. Mixture factors are unique in that their levels need to add up to 100%. This restriction affects the design of the experiment, data visualization, and the statistical model that is used to analyze the experiment. A case study is used to demonstrate how to design, visualize, and analyze mixture experiments in JMP.

You have a business or research question, you’ve collected or found appropriate data, and you are ready to analyze. But which analytical methods should you try? How will your build your models? Do you need interactions? Polynomials? How will you choose model terms? In this session, we will look at several data scenarios and present modeling options and a framework for comparison. We will look at how different questions or goals affect the modeling choices we make. (Predict? Explain? Find associations?) Models covered will include methods found in the “Fit Y by X,” “Fit Model,” “Predictive Modeling,” “Screening,” “Multivariate Methods,” and “Clustering” menus in JMP.

Are you maximizing your use of JMP software? Could there be a feature or tool perfect for your business problem that you aren’t yet using because you don’t know when to use it, or even that it exists? Join us for a tour of the capabilities in JMP, including examples and use-cases to show when and why you might use various JMP platforms. You may discover a new tool or method that could make a huge impact in your business!

This hands-on session is for JMP users who have at least a basic familiarity with design of experiments and would like to learn more about using the Easy DOE platform. Easy DOE provides a workflow framework to assist in the design and analysis of experiments, using either Guided Mode, with prompts and tips at each step, or Flexible Mode, with access to all of the features of JMP’s DOE platforms. The session uses examples to demonstrate both modes in Easy DOE.

Whether you have brand-new JMP users who need to learn how to use the software, experienced users who need to learn advanced analytical techniques or automation with scripting, or you want to start an internal training program, JMP Education offers myriad learning resources. This session provides an overview of available resources and how best to take advantage of them, as well as current strategies for incorporating the materials into a training program. The session concludes with an informal discussion in which participants can share and brainstorm ideas.

In this session, we explore how to determine how much variation in your process comes from various sources. The methods are applicable to any process with either controllable or uncontrollable factors, including manufacturing processes, measurement processes, or others. We discuss statistical models containing both random and fixed factors, as well as crossed and nested factors. Next, we design a study to support the model. After collecting data, we highlight special features in Graph Builder to visualize these sources of variation. Finally, we discuss how to interpret the model results and what those results mean for process improvement or control.

In this session, we explore how experimentation on mixtures or formulations is different from usual process experimentation. Mixture factors are unique in that their levels need to add up to 100%. This restriction affects the design of the experiment, data visualization, and the statistical model that is used to analyze the experiment. A case study is used to demonstrate how to design, visualize, and analyze mixture experiments in JMP.