Expand Your Analytic Skills
1-hour, live, online webinar demos and Q&A by JMP Engineers show ways to apply JMP analytics and visualization to business problems. Demos are made into videos at least once during each JMP release. Four levels are offered:
- Basic – Content useful after completing the New User Welcome Kit.
- Intermediate – Detailed content that goes beyond JMP basic capabilities.
- Advanced – Specialized content requiring in-depth domain knowledge.
- JMP Clinical – Content for using JMP alongside SAS to analyze clinical trials.
You can register for any webinar or for multiple webinars at once. You will receive a confirmation email and two timely reminders for each webinar.
- Using Blocking When Designing Experiments March 28, 2019 | 2 p.m. ET (11 p.m. PT)JMP DOE tools incorporate methods to block designs to increase accuracy by controlling for sources of variability (blocking factors) that are not of primary interest to the outcome. Learn about blocking types, when and how to use them, and situations where they may be useful. See case studies creating several types of blocked designs.
- Using JMP Pro to Discover and Predict Patterns using Neural Network Models March 29, 2019 | 2 p.m. ET (11 a.m. PT)JMP offers neural network tools that are useful for analyzing data that contain both linear and non-linear relationships. Use JMP to develop models that can be used to understand underlying processes and hidden relationships for seemingly complex problems. See case studies that show how to build neural networks, starting with a simple one-layer network, and how to use JMP Pro to build more complicated self-learning and boosted models. In the process you’ll also learn how to build nonlinear principal components.
- Using JMP Pro Functional Data Explorer to Add Value to your Analyses - Part 1 April 4, 2019 | 2 p.m. ET (11 p.m. PT)JMP Pro Functional Data Explorer (FDE) turns data that are presented as functions, signals, or series into a form that can be analyzed. FDE can be used to pre-process data and using it to create surrogate models suitable for Principal Component Analysis.
- Reliability Analysis - Part 1 (Non-Repairable Systems) April 5, 2019 | 2 p.m. ET (11 a.m. PT)JMP helps you pinpoint defects in materials or processes and identify design vulnerabilities so you can understand the best way to correct them. See how to use JMP to determine the most appropriate distribution to use for making reliability lifetime predictions on your non-repairable products and components.
- Building Better Predictive Models Part 1: Understanding the Principles April 11, 2019 | 2 p.m. ET (11 p.m. PT)JMP and JMP Pro offer an extensive set of tools to model your data and build predictive models suited to your data and needs. Understanding model types and techniques; the roles of test, training and validation data; and how to compare models helps you get the most predictive information from your models. This session, the first in a two-part series, covers the principles of predictive modeling.
- Evaluating and Monitoring Your Process Using Measurement System Analysis and Statistical Process Control April 12, 2019 | 2 p.m. ET (11 a.m. PT)JMP offers a variety of tools and techniques to help evaluate a process for stability, bias and the precision of the measurement system. See how to analyze measurement system variation, characterize the common causes of system variability and set up process monitoring approaches that take into account measurement system variability.
- Using Text Explorer to Extend Analysis April 19, 2019 | 2 p.m. ET (11 a.m. PT)Text Explorer lets you include analysis of unstructured text, such as comment fields in surveys or incident reports. See this intriguing case study demo where interacting with unstructured data lets you more fully understand the results of a local government business-related survey.
- Building Better Predictive Models Part 2: Implementing Models using JMP and JMP Pro April 25, 2019 | 2 p.m. ET (11 p.m. PT)JMP and JMP Pro offer an extensive set of tools to model your data and build predictive models suited to your data and needs. Understanding model types and techniques; the roles of test, training and validation data; and how to compare models helps you get the most predictive information from your models. This session, the second in a two-part series, uses case studies to demonstrate the application of principles using JMP and JMP Pro.
- Deriving Sentiments from Opinions or Product Choices April 26, 2019 | 2 p.m. ET (11 a.m. PT)Text Explorer lets you curate free-form text to gain insight into themes and important terms. Combining text analysis with JMP Pro predictive modeling tools also lets you use supervised learning sentiment analysis to determine which words and phrases are most relevant to a specific problem. This information can guide product development and marketing campaigns. See several consumer and social media case studies that show how to use JMP to determine sentiments associated with purchasing behavior or customer reviews.
- Using JMP Pro Functional Data Explorer to Add Value to your Analyses - Part 2 May 9, 2019 | 2 p.m. ET (11 p.m. PT)JMP Pro Functional Data Explorer (FDE) turns data that are presented as functions, signals, or series into a form that can be analyzed. FDE can be used to pre-process data and using it to create surrogate models suitable for Principal Component Analysis.
- Unlocking the Power of Graph Builder May 10, 2019 | 2 PM ET (11 AM PT)Explore and present data easily using JMP’s interactive Graph Builder. The drag-and-drop interface is intuitive and lets you get started quickly. Advanced features allow you to control format and appearance to create persuasive visualizations. See several case studies that demonstrate different techniques to create compelling graphs that uncover relationships in your data and identify areas for further analysis.
- Designing Experiments to Optimize Manufacturing Materials Selection May 16, 2019 | 2 p.m. ET (11 a.m. PT)Designing experiments based on physical or chemical properties significantly minimizes the number of required experimental conditions and produces a model that can predict untested options. These efficient designs are based on QSAR (Quantitative Structure – Activity Relationship). This demonstration is based on a 2017 JMP Discovery Summit presentation by Silvio Miccio, Procter and Gamble.
- Creating, Using and Sharing Journals May 17, 2019 | 2 p.m. ET (11 a.m. PT)JMP journals are an efficient way to store and present results as either static or dynamic presentations. They are useful for organizing presentations and let you link to data and resources outside JMP. This type of journal is dynamic because you can open reports and tables and interact with them to select points, subset data, or make more graphs.
- Advanced Techniques for Visualizing Big Data May 23, 2019 | 2 p.m. ET (11 p.m. PT)Key JMP visualization techniques can help identify patterns in big data – patterns that are often difficult to uncover and represent simply yet compellingly. See how to use and add to your toolbox advanced graphing techniques for coloring, marking, linking, filtering and dashboarding.
- Transforming Data to Make Better Predictions May 24, 2019 | 2 p.m. ET (11 a.m. PT)One of the statistical assumptions for regression is that the error (variance) is distributed normally and uniformly across the range of the data. Although statisticians often transform data to a new scale (e.g. take the logarithm of the response) to make the error better match this criterion, engineers and scientists doing the same modeling might not be aware of this assumption. As a result, neglecting to transform the data when creating a model can yield physically impossible and potentially embarrassing results, such as negative values of hardness, resistivity, or the number of defects. JMP 13 guides users to choose an appropriate transformation that will yield a logical and useful model.
- Essentials of Designing Experiments Using JMP May 30, 2019 | 2 p.m. ET (11 p.m. PT)JMP DOE tools let you create a controlled set of tests to model and explore the relationship between factors and one or more responses for your situation. Learn about the basic types of designs and related terminology essential to making experimental decisions. See two case studies that demonstrate how to build, model, refine and interpret results.
- Basic JSL for Building Interactive Dialogs to Automate Workflows May 31, 2019 | 2 p.m. ET (11 a.m. PT)An easy way to regenerate reports in JMP is to capture and reuse scripts that are automatically generated. Often the next step in automating a workflow is to run these scripts from a customized dialog that allows the user to select specific data tables, columns, or row ranges. The JMP scripting language (JSL) includes a set of functions that make building interactive dialogs relatively simple.
- Using Formulas to Get the Most Value from Your Data June 7, 2019 | 2 p.m. ET (11 a.m. PT)JMP has a very powerful, easy-to-use Formula Editor that lets you create a column whose values are computed by a formula and then store that formula as part of a column information. Formulas can be simple assignments of numeric, character, or row state constants, or they can contain complex evaluations based on conditional clauses. Formula values can be linked to, or dependent on, values in other columns and will be automatically recomputed whenever you edit the values in the columns to which the formula is linked.
- Using JMP Pro to Build Models Using Generalized Regression Variable Selection Techniques June 13, 2019 | 2 p.m. ET (11 a.m. PT)JMP Pro implements generalized regression to model complex data where response variables have arbitrary distributions. Generalized regression handles data that traditional methods cannot handle, such as multicollinearity in model inputs, and provides better screening for the most important factors than traditional methods. See several case studies that show how to use generalized regression to rapidly build models interactively using JMP Pro.
- Tracking and Trending Manufacturing Metrics June 27, 2019 | 2 PM ET (11 AM PT)JMP makes it easy to track processes using continuous measurements or attribute data, compare your process to established limits and use trend information to understand the difference between common sources of variation and special causes.