For JMP Days, we offer a standard menu of presentations so the customer can select topics of interest. Below is an updated list of topics. Please review and reply here by January 20 so it can be rolled out for JMP Days in 2017. Let me know if there are missing topics (remember that we need standard presentations so you are not creating new presentations for every JMP Day), missing details on specific topics, and/or additional feedback. Thank you!
On-Site JMP® Day Topics
What’s New in JMP 13. Learn about exciting new features in the latest version of JMP.
Getting Started With JMP. Great as an introduction to JMP for the new user or as a refresher for the infrequent user. This topic covers the basics of the JMP interface and data tables, getting data into JMP, doing basic summary analysis and charting, saving and sharing your work, and where to get help on JMP.
Preparing Your Data for Analysis. In any analysis problem, most of your time is spent preparing the data for analysis. JMP makes this process easy with powerful data manipulation capabilities. See how to rearrange data to meet any analysis challenge. Topics include: importing data from Excel, Query Builder, Recode, Selecting Matching Cells, Name Selection in Column, Value Labels, Compression of Selected Columns, Stack/Split and Join.
Exploratory Data Analysis and Dynamic Graphics. JMP is the perfect tool for exploring data. Whether you are searching for relationships, patterns or outliers, learn how to look at your data to gain critical insights. This topic walks through a case study showing the basics of analysis using JMP. It covers dynamic linking, visual analytics, Fit Model and the Profiler.
Sharing Analysis Results. You can save results from JMP analyses in many ways. See how to use JMP Reports, Journals and Dashboards to save and organize your work. This topic covers copying/pasting reports and graphics to Microsoft Office programs, adding a Journal to a report, selecting a Journal from a report, adding custom text to a Journal, creating a presentation Journal from scratch, using script links and reports, gathering data tables in a report, interactive HTML and Dashboard Builder.
Data Mining and Predictive Modeling. Discover how to use the JMP Partition, Stepwise Regression and Neural Network platforms to perform data mining and root cause analysis, and to build predictive models on historical data sets.
Saving Time and Analyses With JMP Scripting. The JMP Scripting Language (JSL) will give you the tools you need to make using JMP even more productive. This topic covers saving scripts to journals and data tables, and combining scripts to automate repetitive actions.
Getting the Most From Your Experiments. JMP has powerful, world-class design of experiments (DOE) capabilities. See how to use the DOE platforms to make the most of your experiments. This topic covers Custom Design, Screening Design, Response Surface Design, Full Factorial Design and Choice Design.
Using Graph Builder Effectively. JMP produces specialized graphs and plots with supporting tables, statistics and maps. The drag-and-drop interface is intuitive, lets you get started quickly and gives you a ribbon of options to use to select alternate graph types. 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. This topic covers using obvious and hidden graph zones; building uncommon graph types; and fine-tuning axes, colors, gradients and legends.
Exploring Opportunities for Defect Reduction. Model visualization and Monte Carlo simulation in JMP are useful for designing robust processes and products. This topic covers Profiler, Simulator, Defect Profilers and Simulation experiments.
JMP Tips and Tricks for Maximum Productivity. This highly interactive session features useful tips and powerful tricks to make the most of JMP.
Making JMP Your Analytic Hub. When combined, JMP®, SAS® and R complement each other to form a visualization and analytic powerhouse. See how to use JMP as a front end to SAS and how to take advantage of R libraries to develop, deploy and use analytic solutions.
Introduction to Quality Control. Learn how JMP can help you achieve quality control using control charts to assess a specific process. The robust statistical analysis and data visualization in JMP let you immediately spot trends and outliers in huge data sets about your products. This topic covers Measurement Systems Analysis and Control Chart Builder.
An Introduction to Modeling and Multivariate Analysis. The modeling and multivariate platforms in JMP put powerful analytic techniques at your fingertips. This topic shows how to make the most of the model visualization and data discovery tools.
Introduction to Time Series Analysis. Learn how to create Time Series Models (ARIMA and Smoothing Models) using the Time Series platform.
A Look at Screening Experiments in JMP. When you have many potential factors affecting the outcome of a process, you need to find the vital few influential factors. This topic shows how the DOE and Screening platforms in JMP make it easy to discover the significant factors. See how to use the JMP Definitive Screening Design platform and graphical design diagnostics methods to yield more information in fewer trials than traditional screening designs.
Introduction to Product Reliability. In any manufacturing endeavor, product reliability strongly influences business success. Reliability tools in JMP help you prevent failure and improve warranty performance. By revealing trends and outliers in your data and model predictions, JMP helps you find important design vulnerabilities, pinpoint defects in materials or processes, and then determine how to reduce them.
An Introduction to Visual Six Sigma: Case Studies. Visual Six Sigma is an approach that augments the well-established statistical approaches used by Six Sigma analysts. This topic covers how JMP for Six Sigma uses dynamic visualization and exploratory data mining to streamline Six Sigma project execution.
Using JMP for the Life Sciences. Learn how to analyze biological data with JMP using the Graph Builder, Distribution, Fit Model, Nonlinear and Degradation platforms. Use Graph Builder and Distribution to explore data and visually test assumptions before analysis. See how to perform parallelism and equivalence, and look at relative potencies for bioassays. Discover how to find Km and Vmax for enzymes. This topic concludes by showing how you can use Stability Tests to find expiration date of potency or thresholds of impurity (same slope/intercept vs. different slope/different intercept).
Introduction to JMP Clinical. JMP Clinical software from SAS simplifies data discovery, analysis and reporting in clinical trials. This exceptional ability brings greater efficiency and accuracy to studies of safety and efficacy data at every phase of the drug development process: pre-clinical, clinical and post-market. This session will give an overview of types of reports, analyses and visuals that JMP Clinical can provide for Medical Monitors, Medical Writers, Data Managers and Clinical Operations personnel to use in the areas of Adverse Event/Concomitant Medications/Labs Reporting, Risk-Based Monitoring, and Data Quality/Fraud.
Introduction to JMP Genomics. Whether you’re working in agriculture, pharmacogenomics, biotechnology or other areas of genomic research, JMP Genomics provides comprehensive tools to analyze rare and common variants, detect differential expression patterns, understand NGS data, and discover reliable biomarker profiles. This session will give an overview of the types of analysis commonly performed on genetic data including SNP-Trait Association, Q-K Mixed Model analysis, QTL analysis and genomic selection (typically used for crop improvement). It will also provide an overview of JMP Genomics in gene expression analysis for protein, mRNA, miRNA and metabolite data that covers QC tools, Normalization methods (intensity and count data from RNA-seq) and basic differential gene expression analysis using ANOVA.
