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Academic Webinar Library

The JMP Global Academic Program provides live webinars each semester for faculty and students wanting to learn about JMP. Below you will find recorded versions of our most recent webinar for each topic in the series. 

To sign up for an upcoming live webinar, please visit our Live Academic Web Series page.

 

Special Editions

Learn about special topics, gain insight from academic and industry guests

Teach with JMP

JMP 101: Intro to JMP for Teachers

Whether you’re new to teaching with JMP statistical software or you just need a refresher on the fundamentals, this webinar reviews everything you need to know to start teaching with JMP effectively. Topics include:

  • Navigating the JMP interface
  • Core graphing and analysis tools
  • Importing data, saving and sharing results
  • JMP Academic’s library of free course materials

JMP 101: Intro to JMP for Students

JMP is no-code data visualization and analysis software used across a wide range of industries and academic disciplines. If you're a student who will be using JMP in your coursework or research, this webinar will teach you everything you need to know to begin using the software. We provide an overview of JMP's interface and range of tools, and then show how to import data, make graphs, perform analyses, and save results. We also show the top resources to use when you're wondering how to get JMP to do what you want.

If you're a student who's new to JMP, let the JMP Academic team help you get started with the software. It might even help you land a job someday.

Resources for Teaching with JMP

Teaching with JMP includes access to a variety of teaching resources that can complement your course or lighten your load. This webinar reviews these resources, how to access them, and how to use them effectively. We review:

  • A library of over 100 quick tutorial videos and PDF guides that students can use to learn JMP outside of class
  • Interactive concept applets for teaching sampling distributions, hypothesis testing, confidence intervals, and more
  • Sample data sets for a variety of disciplines and statistical analyses
  • A library of case studies, each presenting a data analysis problem with a multi-step solution path and follow-up exercises
  • A self-paced online stats course with individual modules that can be assigned to students directly or used to complement your course via the available slides, data sets, and example lesson plans

Teaching Univariate Statistics and Probability

Univariate statistics and probability form a foundation for students' understanding of more complex statistical concepts. This webinar provides the knowledge you need to teach this important topic with JMP. Topics include:

  • Describing variables through graphical and numerical summaries
  • Statistical inference for categorial and continuous variables
  • Assessing distributional assumptions
  • Interactive applets for teaching statistical concepts such as area under a probability density function, sampling distributions, and confidence intervals
  • Relevant teaching tips and resources from the JMP Academic team

Teaching Statistical Inference with JMP

Statistical inference – inferring population parameters from sample data – is a central component of statistics education. This webinar will guide you in using JMP to teach core statistical inference techniques. We demonstrate tools for generating point and interval estimates of common parameters and for conducting hypothesis tests using both traditional parametric methods and resampling/randomization techniques. We also review JMP’s interactive concept applets and other resources for teaching statistical inference.

Teaching Exploratory Data Analysis with JMP

Many classroom data analysis exercises involve a clean data set, clearly defined question, and single analysis. But real-world data analysis isn’t always so straightforward. Instead, it often involves some form of data exploration: initially assessing data integrity, searching for patterns with whichever graphs or analyses are needed, formulating new questions based on initial findings, and so forth. Teaching this exploratory process helps prepare students for the real-world data analysis challenges they will face after graduation. This webinar will help you build this important skill into your course by reviewing JMP tools for exploring data visually and statistically as well as for assessing data integrity. We also review relevant teaching resources.

Teaching Multivariable Thinking in Intro Statistics

According to the American Statistical Association’s Guidelines for Assessment and Instruction in Statistics Education (GAISE) reports, introductory stats courses should introduce students to multivariable thinking: thinking about interrelationships of many variables and what those relationships mean. Fostering students' multivariable thinking in intro statistics can be challenging, but JMP’s interactive and visual tools can help.

This webinar demonstrates JMP techniques for fostering multivariable thinking without requiring advanced statistical analyses. Topics will include:

  • Exploring complex and multidimensional data through intuitive interactive graphs
  • Intuitive ways to teach a range of important multivariate concepts (e.g., grouping observations based on multiple variables, combining many variables into a new summary variable)
  • Dynamic visualizations to describe changes over time
  • Relevant teaching tips and resources from the JMP Academic team

Teaching Regression and ANOVA with JMP

Regression and ANOVA are central to statistics education across many disciplines. JMP’s interactive, graph-first interface makes it a strong tool for teaching these foundational techniques and the concepts underlying them. This webinar reviews JMP tools for simple and multiple regression and for one-factor and factorial ANOVA. We also review relevant JMP teaching resources, including case studies, tutorial videos, and interactive concept applets.

Teaching Advanced Regression with JMP and JMP Pro

A solid foundation in ordinary least squares (OLS) regression will take students a long way in their careers. Yet, many will someday encounter situations that OLS cannot readily handle. Perhaps the response distribution is non-normal. Or perhaps the predictors are highly correlated or outnumber the observations. Or maybe there are so many possible models that they need a principled way to select the best one. In these situations, familiarity with advanced regression techniques is critical. This webinar guides you in using JMP and JMP Pro to teach several advanced regression methods: generalized linear models, stepwise regression, and regularized regression. Emphasis is on JMP Pro’s Generalized Regression platform.

Teaching Categorical Data Analysis with JMP

JMP includes comprehensive tools for teaching categorical data analysis concepts and techniques, including categorical probability distributions, confidence intervals and hypothesis tests on proportions, contingency analyses, logistic regression, and more. This webinar provides a tutorial on how to use these tools, with plenty of teaching-focused tips along the way. We also review some of JMP Academic's free teaching resources related to categorical data analysis. By the end, you'll be better prepared to use JMP to teach categorical data analysis in your course.

Teaching Predictive Modeling with JMP Pro

Predictive modeling is a core part of education in data science, business analytics, and other domains. JMP Pro includes machine learning algorithms commonly taught in this area, including decision trees, neural networks, support vector machines, k nearest neighbors, and more. It also includes tools for model tuning, validation, comparison, and deployment both inside and outside of JMP (e.g., in Python), and all are implemented in JMP's interactive, no-code interface that makes powerful techniques accessible to a wide range of students. This webinar demonstrates how to use JMP Pro's predictive modeling tools in the classroom and will highlight free teaching resources available to complement your course.

Teaching Multivariate Methods: Clustering, Principal Component Analysis

Real-world data can be highly multivariate, and students in statistics, data science, and the physical and social sciences should be equipped to analyze such data with appropriate multivariate analysis methods. JMP statistical software can make multivariate methods more accessible and engaging to students through its interactive, no-code interface.

This webinar presents JMP tools and tips for teaching two common and widely applicable multivariate methods: clustering and principal component analysis (PCA). Topics include:

  • Overview of unsupervised learning methods in JMP
  • Exploring and understanding multicollinearity
  • Teaching examples of PCA, hierarchical clustering, and k-means clustering
  • Relevant teaching tips and resources from the JMP Academic team

Teaching Multivariate Methods: Factor Analysis, Structural Equation Modeling

Real-world data can be highly multivariate, and students in statistics, data science, and the physical and social sciences should be equipped to analyze such data with appropriate multivariate statistical methods. JMP software can make learning multivariate methods more accessible and engaging to students through its interactive, no-code interface.

This webinar presents JMP tools and tips for teaching two common multivariate methods: factor analysis and structural equation modeling (SEM). Topics include:

  • Multivariate data exploration
  • Performing exploratory and confirmatory factor analysis
  • Fitting structural equation models
  • Relevant teaching tips and resources from the JMP Academic team

Teaching Multivariate Methods: MANOVA, Partial Least Squares

Multivariate statistical methods are taught across a range of disciplines, from chemistry to psychology and more. JMP makes many basic-to-advanced multivariate methods accessible to students through its interactive, no-code interface.

This webinar demonstrates tools and techniques for teaching two common multivariate techniques: multivariate analysis of variance (MANOVA) and partial least squares regression (PLS). Topics include:

  • Exploring multivariate data with interactive graphs
  • Examples of performing MANOVA and PLS using JMP's Fit Model platform
  • Relevant teaching tips and resources from the JMP Academic team

Teaching Mixed Models with JMP and JMP Pro

Linear mixed models — linear models with both fixed and random effects — have become widely taught in many disciplines due to their ability to handle non-independence among observations, a challenge that students are likely to encounter at some point in their careers. JMP and JMP Pro include extensive mixed modeling capabilities, with an interactive no-code interface that makes mixed models more approachable for many students. This webinar presents tools for analyzing basic blocking structures in JMP before moving on to JMP Pro for tutorials on analyzing data with more complex covariance structures and on mixed models for non-normal response data (generalized linear mixed models). We provide teaching tips along the way, including pointers to relevant teaching resources from the JMP Academic team.

Teaching Text Mining with JMP and JMP Pro

Unstructured text data is everywhere, and modern analytics and data science education is increasingly covering text mining techniques. Text Explorer in JMP and JMP Pro enables students to learn basic and advanced text mining methods through an interactive, no-code interface, making text mining approachable to a wide range of students and disciplines. This webinar demonstrates basic text analyses in JMP, including term frequencies, word clouds, and term co-occurrences before moving on to more advanced techniques in JMP Pro, including latent semantic analysis, topic analysis, and sentiment analysis. We also review free text mining teaching resources that you can incorporate into your course.

Teaching Statistical Quality Control with JMP

This webinar demonstrates the many resources and tools available in JMP to help you teach statistical quality control methods. We review JMP’s interactive control chart, process capability analysis, and measurement systems analysis tools and how to use them effectively for teaching. We also review JMP's free teaching resources related to statistical quality control that you can use to complement your course.

Teaching Survival and Reliability Analysis with JMP

Analysis of time-to-event data – often called survival or reliability analysis – is commonly taught in biostatistics, reliability engineering, and other domains. JMP’s suite of survival and reliability tools support comprehensive teaching of key concepts and techniques in this domain, including survival and hazard functions, censoring, Kaplan-Meier analysis, parametric survival modeling, and more. This webinar provides a tutorial on JMP tools for teaching these concepts and techniques, as as well as a brief review of JMP tools for more specialized reliability analyses common in industry, such as accelerated life testing. We also point you to relevant free teaching resources from the JMP Academic team.

Teaching Design of Experiments

Design of experiments (DOE) is a critical skill to develop in the modern science or engineering student. JMP's world-class suite of DOE tools is used extensively in industry, and JMP's visual, no-code interface makes its DOE tools great for classroom use, too. This webinar reviews the use of JMP for teaching DOE, with a focus on both classical and modern optimal designs, analysis of DOE data, and free DOE teaching resources offered by the JMP Academic Program.

Research with JMP

Cleaning and Preparing Data for Analysis

Researchers are intimately familiar with the amount of work often needed to clean and prepare data so it’s ready to perform specific statistical analyses. At times, these efforts can take quite a bit more time than the analyses themselves. In this webinar, a statistical scientist from JMP demonstrates a variety of easy-to-use tools to help expedite these efforts. Topics include importing data, recoding and transforming variables, filtering data, and recording/automating operations, among others.

Tools for Data Exploration

Each new data set we encounter brings with it the need for initial data exploration. Before building models or running tests, we need to summarize the data numerically and graphically, screen for patterns or anomalies, and ensure that the data are suitable for further analysis. Put more generally, we need to "get to know" our data. In this webinar, you’ll learn JMP tools and techniques you can use for  the initial exploration of every new data set you analyze.

Graph Builder and Beyond: Data Visualization with JMP

This webinar is for any academic looking to visualize their data, either for data exploration or for presentation and publication. We demonstrate how to make a variety of basic and specialized graphs in Graph Builder, with additional graphing platforms covered in the accompanying JMP Journal file. Learn how to create effective data visualizations easily, without writing any code.

This webinar focuses on constructing different types of graphs. To learn how to customize graph aesthetics and export in high quality formats for presentations or publications, see Producing Pubilcation-Quality Graphics.

Producing Publication-Quality Graphics

Research publications and presentations call for high-quality data visualizations, often following specific formatting requirements, aesthetic preferences, or other customization needs. While most JMP users are aware of JMP's strength in exploratory graphing, fewer are aware that JMP also is capable of producing highly customized, publication-quality graphs. In this webinar, you'll learn basic and advanced techniques for customizing graph formats and aesthetics as well as how to export graphs in high-quality image formats that can be resized without becoming blurry or pixelated. You'll also see how to perform extremely fine customizations on JMP graphs after exporting them, opening up a nearly infinite level of customization.

Essential Statistical Tests

Academic researchers often need to determine whether their quantitative data support certain conclusions. Did the experimental treatment have the predicted effect? Are these two measures correlated with each other? Does the probability of some event or outcome depend on certain factors? A researcher can answer many questions like these with basic knowledge of a small set of statistical tests.

This webinar introduces a range of essential statistical tests and how to perform them in JMP. Topics include:

  • Comparing group means
  • Testing whether the probability of an event or outcome is related to other factors
  • Testing whether two continuous measures are related to one another  

Step-by-step guide

View Guide

Building, Diagnosing, and Interpreting Linear Regression Models

Linear regression modeling is one of the most widely used statistical tools in academic research. Applying this foundational technique effectively requires a degree of technical knowledge regarding model construction, diagnostics, and interpretation. This webinar, which is appropriate for novice to intermediate linear modelers, will help you build the technical knowledge to be able to confidently use linear regression models in your work. We'll see how to: build standard least squares regression models with a mix of continuous and categorical factors, diagnose and address common problems with regression models, and interpret model parameters appropriately.

Designing and Analyzing Experiments, Pt. 1: An Introduction

Design of experiments (DOE) is a foundational statistical skill in science and engineering. Using DOE, researchers can develop efficient plans for collecting data to reveal causal relationships between factors and responses. This webinar, intended for those with little-to-no DOE background, introduces fundamental DOE techniques and how to apply them in JMP. Topics include:

  • Designing basic one-factor and factorial experiments
  • Determining sample sizes for one-factor experiments
  • Analyzing experimental data with regression models
  • JMP’s Easy DOE platform

Designing and Analyzing Experiments, Pt. 2: Advanced Topics

Following on the previous webinar, Designing and Analyzing Experiments, Pt. 1: An Introduction, this webinar introduces a selection of intermediate and advanced concepts and techniques in the design of experiments (DOE) and how to apply them in JMP. Topics include:

  • Statistically optimal designs that are custom-built to unique experimental settings
  • Accommodating restrictions on randomization (e.g., split plot designs)
  • Accommodating constraints on the factor space
  • Constructing sequential designs

Statistical Analyses for Comparison Research Studies

Many research studies involve testing for and quantifying the differences between treatments. These studies could consist of a simple A/B comparisons of a single factor all the way to exploring the impact that multiple factors including their potential interactions have on an outcome. In this webinar, a statistical scientist from JMP demonstrates a variety of tools in JMP that researchers can use to design comparative studies and perform the proper analyses needed to determine if there are statistical differences across treatments.

Research Methods with JMP: Clustering, Factor Analysis, and SEM

In this webinar we will explore techniques needed for Research Methods, including high-dimensional data visualization and modeling using JMP's graphing and multivariate analysis platforms (e.g. Multivariate, Cluster, PCA, Factor Analysis, and SEM).

April 28th, 2020. Presented by: Ruth Hummel

Nov 4th, 2016. Presented by: Julian Parris 

Survey Analysis Fundamentals

Survey data is ubiquitous in social science, business, and healthcare research, and its analysis poses special considerations and challenges. This webinar reviews fundamental survey analysis techniques and how to implement them in JMP, including:

  • Preparing survey data for analysis (e.g., recoding response levels, handling multiple-response questions)
  • Creating custom cross-tabulation tables
  • Effectively visualizing and analyzing large groups of categorical responses
  • Mining information from free text responses

Using JMP to Analyze Data with Many Variables:

Sept 15th, 2020. Presented by: Kevin Potcner

In the webinar you will learn tools in JMP that can analyze data with a large number of variables. Techniques will include: 1. Using animation-based visualization to explore data over time (using Bubble Plot). 2. Performing a large number of statistical tests simultaneously to find those response variables that result in statistically significant results (using Response Screener). 3. Finding the variables that most impact a response when there are more variables than observations (using Bootstrap Forest). 4. Comparing variation between two groups of highly-dimensional data (using Principal Component Analysis).

Analyzing Functional (aka Curve) Data with JMP Pro

Researchers in numerous domains often encounter functional data, or one continuous measure that unfolds across another. Prominent examples include chemical spectra, financial series, and sensor data; think any data where the unit of analysis is not a single point, but a curve. Functional data analysis (FDA) offers techniques to analyze these curves in order to characterize their shapes and to understand how other variables affects their shapes, how their shape characteristics affect other variables, and even how to optimize their shapes. JMP Pro's Functional Data Explorer  enables researchers to use FDA techniques to solve analytic problems involving this potentially challenging type of data. This webinar will provide an overview of FDA and Functional Data Explorer in order to help you add FDA to your research tool belt.
 

Genomics Research with JMP Pro

JMP Pro 17 marked a significant change in how genomic data analysis is performed in JMP. New platforms were introduced for genetic marker analysis and breeding simulations, and several existing platforms were optimized for the handling of large, wide data sets. Unlike the specialized JMP Genomics software, these new features are built directly into JMP Pro and do not require a SAS backend. This webinar demonstrates how to apply JMP Pro in genomics research through case studies on Differential Gene Expression and GWAS.
 

Tools for Reproducibility and Automation

Reproducibility of statistical results is paramount in scientific research. Many researchers use code-based software in part for this very reason: code both documents and executes the exact steps needed to reproduce an analysis. Even with its point-and-click interface, JMP provides several ways to capture and re-execute JSL code for your actions, offering the best of both worlds: the ease of point-and-click and the inherent reproducibility of code.

This webinar demonstrates several tools for reproducibility in JMP, including Workflow Builder, a new tool in JMP 17 for automatically recording and re-executing point-and-click actions, as well as for applying those recorded actions to new data sets.

Integrating JMP and Python

JMP and Python are great complements to one another: JMP offers wide data visualization and analysis capabilities in an easy, visual, point-and-click interface, and Python brings a versatile set of additional capabilities. As of JMP 18, users have greatly improved options for integrating Python into their JMP workflow. This webinar reviews techniques for integrating JMP and Python, with topics including:

  • JMP's built-in Python script editor and extensible Python environment
  • Bidirectional communication between JMP and Python
  • Running Python scripts on JMP data tables
  • Creating JMP add-ins that leverage Python capabilities

JMP for Your Discipline

Teaching Analytics in Chemistry and Chemical Engineering with JMP

This webinar demonstrates teaching tools and free resources commonly used within Chemistry and Chemical Engineering. An important topic will be Design of Experiments using classical, mixture, functional or flexible custom designs, and how to analyze and understand experimental data. In addition to multivariate methods like Partial Least Squares or Functional Data Analysis, see how to teach quality methods and six sigma in the most visual, interactive and engaging way.  

Jan 7th, 2021. Presented by: Volker Kraft

Teaching Engineering Statistics with JMP

JMP’s interactive, no-code interface makes it an excellent choice for teaching statistics to future engineers. Its use by engineers at leading companies around the world makes it a valuable career skill, too. This JMP Academic webinar reviews the use of JMP to teach engineering statistics, from basic techniques such as regression and ANOVA to more specialized techniques such as design of experiments and statistical quality control. We also review JMP’s range of teaching resources useful in engineering disciplines. If you teach engineering stats, this webinar will help you decide whether JMP might be the right tool for your course.  

Teaching Business Analytics and Data Science with JMP Pro

Analytics and data science skills are in-demand and widely taught across business, marketing, information systems, and many other fields. JMP Pro’s combination of an interactive, no-code interface with powerful statistical and machine learning capabilities makes it a strong choice for teaching in this area. This webinar provides a comprehensive overview of JMP Pro for teaching business analytics and data science, including data visualization, basic statistical modeling, machine learning for predictive modeling and data mining, and more. We also review relevant teaching resources that make it easier to adopt JMP Pro in your course, including textbooks with JMP integration, quick tutorial videos, and case studies for demonstrating real-world analytics problems with JMP Pro.

This webinar is appropriate both for instructors who are new to JMP Pro and for those who already teach with JMP Pro but would appreciate a broad overview of its capabilities for business analytics and data science.

Better Teaching (and using) Data Analytics for Materials Science

In this webinar we show university instructors and students best practices to better teach and learn the best data analysis to easily discover, design and manufacture new materials and accelerate materials property calculation using exploratory data analysis, Machine Learning, design of experiments and data modeling.

Jun 19th, 2019. Presented by: Volker Kraft

JMP for Teaching Statistics in Life Sciences

Health and life sciences are innovative, fast-growing industries that present many career opportunities for students with data analysis skills. JMP includes comprehensive data visualization and analysis capabilities for life sciences, all packaged in an easy-to-use, visual interface that is great for the classroom. This webinar provides a comprehensive overview of JMP tools for teaching statistics in life sciences, including data visualization, basic statistical tests, regression and ANOVA, categorical data analysis, survival analysis, and more. We also review relevant teaching resources that make it easier to adopt JMP in your course, including textbooks with JMP integration, quick tutorial videos, and case studies for demonstrating real-world analytics problems with JMP.

This webinar is appropriate both for instructors who are new to JMP and for those who already teach with JMP but would appreciate a broad overview of its statistical capabilities for life sciences.

Special Editions

New in JMP 18 and JMP Pro 18 for Academics

JJMP 18 and JMP Pro 18 are being released in Spring 2024, bringing a host of new features useful to academics, whether for teaching or research. This webinar provides an overview of new features before demonstrating several in detail, including:

– An improved new user experience

– The Columns Manager for easier data management

– Platform Presets for creating and reusing customized report templates

– Python integration improvements

– Deep learning via the Torch library

Modern Design of Experiments

Special Guests:
Bradley Jones, PhD, Distinguished Research Fellow, JMP Statistical Discovery
Douglas Montgomery, PhD, Regents Professor of Industrial Engineering and Statistics, Arizona State University

In this JMP Academic Series webinar, we are joined by Dr. Bradley Jones and Dr. Douglas Montgomery to learn about their new book "Design of Experiments: A Modern Approach." They describe the need for a modern approach to teaching and using DOE. In the process, they point out the connections between optimal modern designs, orthogonality in designs, and well-known traditional designs.

Statistical Quality Control

Special Guests:
Brenda S. Ramírez, MS, Industrial Statistician and JMP Author
José G. Ramírez, PhD, Industrial Statistician and JMP Author

Ms. Brenda Ramírez and Dr. José Ramírez are long-time practitioners and educators of Statistical Quality Control techniques in the semiconductor, chemical, electronics, and biotechnology industries. This webinar covers examples from their new book, "Douglas Montgomery’s Introduction to Statistical Quality Control: A JMP® Companion." This JMP-focused companion book demonstrates the powerful Statistical Quality Control (SQC) tools found in JMP. Geared toward students and practitioners of SQC who are using these techniques to monitor and improve products and processes, this companion provides step-by-step instructions on how to use JMP to generate the output and solutions found in Montgomery’s book, and we cover several of these examples in this webinar.