# 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.

# Data Exploration and Analysis

### JMP Basics for Professors and Students

This webinar is designed to serve as an initial introduction to JMP. It covers basic navigation, JMP menus and data tables, summarizing and graphing data, and resources for getting started.

###### JMP features demonstrated:

Analyze > Distribution, Rows > Hide and Rows > Exclude, dynamic plot linking, Rows > Data Filter, Analyze > Fit Y by X (Oneway), Analyze > Multivariate Methods > Multivariate, Analyze > Text Explorer, Analyze > Fit Model, and Graph > Graph Builder

### Data Summary and Analysis

This webinar reviews methods of summarizing and graphing data in JMP, including tables, box plots, scatterplots, and geographic maps.  We also cover analyses for univariate and bivariate data (ANOVA, regressions, logistic regression, and contingency tables) and extending these to multiple predictor models. Finally, we briefly cover multivariate summary (correlations) and clustering, plus creating word clouds to summarize text data.

• Who should watch?
Any student, researcher or faculty member interested in using JMP for data summarization and/or analysis.
• What can I expect to get out of the webinar?
You will learn how to conduct univariate, bivariate, and multivariate data summarization and analysis in JMP.

### Visualization and Graphics

Overview of graphing in JMP using the Graph Builder. Topics include: using drop zones, graph types and controls, creating custom error bars, graph customizations, and exporting graphics for publication or sharing on the web.

###### JMP features demonstrated:

Graph > Graph Builder

### Data Preparation and Modeling

In this webinar we explore several platforms in JMP that make data preparation quick and easy. Then, we show how to build predictive models in JMP Pro. The emphasis is on tools and techniques commonly used by academics in business schools and analytics programs, including multiple linear and logistic regression, classification and regression trees, advanced tree methods, neural networks, model validation, and model comparison and selection.

### Data Visualization and Modeling

Data Visualization and Modeling will show how to construct interactive data visualizations and predictive models in JMP Pro. The emphasis will be on tools and techniques commonly used by students and faculty in business schools and analytics programs, including dynamic graphics, multiple linear and logistic regression, classification and regression trees, advanced tree methods, neural networks, model validation, and model comparison and selection.

### Preparing Data for Analysis with JMP

Guest Speaker:
Robert H. Carver, Ph.D.
Professor of Management and Data Science Program Director
Stonehill College

The 2016 GAISE College Report advocates the incorporation of technology, real data, multivariate thinking, and the importance of the full analytic process in college-level statistics courses. It is clear that data management –including acquisition, merging, subsetting, and preparation -- is a key part of the process.  Yet it is a challenge for college instructors to add data management to an already-full course. Data management tasks can be daunting for a student, and the prospect of teaching SQL in Intro Stat can appear overwhelming.

# Advanced Research Methods

### 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).

### Advanced Research Methods with JMP

With JMP Pro for Academic Research, you get all the sophisticated techniques and advanced analytics available in JMP Pro. For academic researchers, this translates into a greater ability to efficiently extract meaning from complex data and build better-performing models. In this webinar we see how to estimate a priori power for complex designs using the new Simulate facility in JMP Pro 13. We also expore tools for finding outliers, calculating new variables, shaping and restructuring data, as well as analysis methods for multiple comparisons in the context of large ANOVA designs.

### JMP for Social Science Research

Overview of tools and analyses useful for research in the social sciences, including tabulating, graphing, ANOVA and regression.

# Teaching with JMP

### Resources for Teaching Statistics with JMP

Are you preparing to update or prepare a new basic statistics or data analysis course? Looking for new examples, assignments, or instructional videos? Looking for interactive concept demos, case studies, eLearning courses with certificates of completion, or other instructional or supplementary materials?

In these webinars, we identify resources that you can use in your statistics and data analysis courses to help teach concepts, to provide examples and instructional videos, to teach JMP software skills, and to support student learning.

### Resources for Teaching Engineering Statistics

In this webinar, we identify resources, specific to an engineering statistics or DOE course, that you can use to help teach concepts, provide examples and instructional videos, to teach JMP software skills, and to support student learning. Resources include examples, assignments, or instructional videos, interactive concept demos, case studies, eLearning courses with certificates of completion, and other instructional or supplementary materials.

Watch this alone, or supplement it with additional resources from the first webinar of this series, “Resources for Teaching Intro Stats.”

### Resources for Teaching Business Statistics

In this webinar, we identify resources, specific to a Business Statistics or Analytics course, that you can use to help teach concepts, provide examples and instructional videos, to teach JMP software skills, and to support student learning. Resources include examples, assignments, or instructional videos, interactive concept demos, case studies, eLearning courses with certificates of completion, and other instructional or supplementary materials.

Watch this alone, or supplement it with additional resources from the first webinar of this series, “Resources for Teaching Intro Stats.”

### Teaching Introductory Statistics with JMP

The webinar is designed to serve as an initial introduction to teaching basic statistics with JMP. It covers how to teach basic navigation, JMP menus and data tables, summarizing and graphing data, and resources for getting started.

### Teaching ANOVA and Regression with JMP

In this webinar, we will explore platforms in JMP made for carrying out ANOVA and Regression analyses, as well as interactive teaching and learning tools to explore concepts related to ANOVA and Regression.

### Teaching Predictive Modeling with JMP Pro

In this webinar we show how to teach the tools and techniques commonly used by academics in business schools and analytics programs, including multiple linear and logistic regression, classification and regression trees, advanced tree methods, neural networks, model validation, and model comparison and selection.  We also introduce new predictive modeling features in JMP 13 Pro, including Formula Depot and Text Explorer.

### Using the JMP Statistical Concept Applets

In this webinar we demonstrate the Built-in applets in JMP:

1. Distribution Calculator — calculate critical values and p-values in various distributions
2. Distribution Generator — build discrete distributions and explore PDF and CDF
3. Sampling Distribution of Sample Means
4. Sampling Distribution of Sample Proportion
5. Confidence Interval for the Population Mean
6. Confidence Interval for the Population Proportion
7. ***I skipped the Hypothesis Test for Mean and the Hypothesis Test for Population Proportion, but you can get the idea from the other applets, and use the Help documentation for more help!***
8. Demonstrate Regression
9. Demonstrate ANOVA
10. Additional Resources: Demonstrate P-Value
11. Additional Resources: Permutation Test for Two Means or Medians
12. ***I skipped the Additional Resources options for Demonstrate Power, Bayes Rule, and Collinearity. Use the Help documentation for more help!***

### JMP Student Edition: Data Exploration and Analysis

JMP Student Edition is a very inexpensive (and in many cases free) option for students and teachers in Intro and Intermediate Statistics courses. In this webinar we cover using JMP Student Edition for data exploration and analysis. (The topics covered are also in JMP and JMP Pro.) You can request a free instructor copy of JMP Student Edition here. Find more licensing and textbook integration information here.

# Special Guest Panel

### Best Practices for Preparing Students for a Career in Business Analytics/Data Science

As the fields of Business Analytics and Data Science for Business continue to evolve, there is much uncertainty about what business programs should teach.

The panel, hosted by George Recck, Babson College, discusses:

• What skills and tool sets business analytics professionals are critical in this environment?
• How can academia can prepare students to be able to perform in this ever-changing environment?

Panelists are:

• Kymm Hockman, recently retired from Dow/Dupont
• Roger Hoerl, Associate Professor of Statistics at Union College and a former manager in the Applied Statistics Laboratory at General Electric
• Michael Posner, Associate Professor of Statistics and Director of the Center for Statistics Education at Villanova University

First presented at the Joint Statistical Meetings in August 2020, now offered as a free encore guest session in the JMP Academic Webinar Series and sponsored by the ASA’s Business Analytics SE group.

# Design of Experiments and Engineering Statistics

### Teaching Analytics in Chemistry

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.

### Modern Design of Experiments

Guest Speakers:
Dr. Bradley Jones and Dr. Douglas Montgomery

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

Guest Speakers:
Brenda S. Ramírez, M.S. and José G. Ramírez, Ph.D.
ZenEos, LLC

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.

### Engineering Statistics

In this webinar we show how to graph and analyze data in JMP, with an emphasis on tools commonly used by academics in engineering and industrial fields, including control charts, measurement systems analysis and designed experiments.

### Teaching Design of Experiments

In this webinar we demonstrate JMP tools and resources to make teaching DOE most effective.

### Teaching Engineering Statistics with JMP

In this webinar we demonstrate tools in JMP to make teaching engineering statistics most effective.

### 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.

# Health and Life Sciences

### Teaching Statistics in the Health and Life Sciences

This webinar will show how to teach the tools and techniques commonly used by professors and students in health and life sciences fields, including ANOVA and regression, mixed models, survival analysis, designed experiments, and graphical tools.

### Biostatistics and Health and Life Sciences in JMP (Basics)

This webinar covers tools commonly used within the health sciences, including interactive graphics, descriptive statistics, fitting distributions, confidence intervals and hypothesis tests, odds ratios, relative risk, linear and logistic regression, and a very brief introduction to survival analysis and fitting mixed models (in JMP and JMP Pro).

### Biostatistics and Health and Life Sciences in JMP (Advanced)

This webinar covers how to use the tools and techniques commonly needed by researchers, practitioners, professors, and students in biostatistics and the health and life sciences fields. Topics covered include ANOVA and regression (including variable selection using penalized regression), mixed models (including split-plot or heirarchical and repeated measures), survival analysis, and designing an experiment in JMP.

### Biostatistics

Guest Speaker:
Trevor Bihl, Ph.D.
Faculty in the Department of Pharmacology and Toxicology and Adjunct Faculty in the Biomedical, Industrial & Human Factor Engineering Department, Wright State University

Dr. Bihl is both a research scientist/engineer and an educator who teaches biostatistics, engineering statistics, and programming. In this webinar, Dr. Bihl talks about data wrangling and data analysis methods and examples from his recent book, "Biostatistics Using JMP: A Practical Guide," and he discusses his experience teaching these topics and provides a bit of advice to academics in this field.

# Genetics and Genomics with JMP Genomics

### An Overview of JMP Genomics: Expression, GWAS, and Plant Breeding

JMP Genomics is a statistical discovery software package for universities and research organizations that enables statistical geneticists, biologists, bioinformatics experts and statisticians to uncover meaningful patterns in high-throughput genetics, methylation and expression (metabolomic, transcriptomic and proteomic) data. In this webinar we look at three topic areas in genetic and genomic research and see how JMP Genomics speeds up the analysis and discovery process for (1) Expression Analysis, (2) GWAS, and (3) Plant Breeding.

### Running and Interpreting a Basic Expression Analysis

This video shows how to set up, run, and interpret a basic expression analysis in JMP Genomics. In this example I use the Basic RNAseq Workflow, which is a pipeline of some simple exploration on the markers and samples (looking at expression distributions for samples, removing "bad" samples, normalizing the samples' expression, exploring correlations and principle variance components) directly into an ANOVA analysis on the log2(expression) to find markers that are statistically significantly differentially expressed. We explore the resulting output as volcano plots (which we can filter further), Venn Diagrams to find markers significant in multiple comparisons, and tables of information about the final set of selected significant markers. This analysis can apply to any kind of continuous data across any kind of biological markers, such as metabolomics, proteomics, or expression.

### Getting Started with JMP Genomics: Overview of Studies, Import, and Workflows

In this video we take a tour of the Genomics Started in JMP Genomics software.
Use this Genomics Starter to explore examples, add studies, import data, and use workflows for common analytical processes.

### Getting Started with JMP Genomics: Creating the EDF and Importing SAM and BAM Files

Learn to import SAM or BAM files into JMP Genomics, taking raw (but aligned) data files and creating appropriate count data files that can be used in further analyses within JMP Genomics.

### JMP Genomics: Move or Share Results

When you move or share a folder of JMP Genomics results, the paths will break. This is a short video to show you how to easily fix those paths.

# Special Topics

### The Scientific Workflow in JMP: Creating Reproducible Analyses

In the webinar we will see how to use JMP journals and scripting to keep track of an analysis workflow in a professional research environment. Our attention will be on how to create analyses that are easily reproducible if data were to change, and the documentation of an analysis process for reporting in journals or to professional colleagues.

### JMP Integration and Extensibility

In this webinar we explore many ways to extend core JMP functionality through its integration with R and Python. We show how to connect JMP to R and Python, how to create your own add-ins for JMP that take advantage of these connections, and where to find more examples and help on this topic. We also reference the JMP integration with SAS, CAS, and Matlab, and even code-generation in Python, C, SAS, SQL, and Javascript.

This webinar is geared for JMP users who need an analysis available only in R or Python, or users of R and Python who also want to make use of time-saving, interactive, and powerful features in JMP, as well as JMP users who want to package a JMP GUI front-end for an R or Python routine, to allow other JMP users to “use” R and Python in a JMP point-and-click interface.

### JMP for Grading and Assessment

The JMP for Grading and Assessment webinar introduces features of JMP useful for managing student grades in courses. You will learn how to: Import and export student grades to campus CMS systems, check grades for entry errors, create weighted averages across assignments, create standardized scores for assignments, create score columns dropping a lowest score, and how to generate letter grades based on scoring criteria.

### JMP for Institutional Research

JMP for Institutional Research webinars will demonstrate a variety of analysis tools and platforms in JMP useful for analyzing data commonly used by professionals in Academic Institutional Research.

### 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).

### Generalized Linear Mixed Models in JMP: GLMM Add-In

#### Jan 27, 2021. Presented by: Ruth Hummel

This webinar covers how to use Generalized Linear Mixed Models in JMP using a new JMP Add-In. These models are commonly needed by researchers, practitioners, professors, and students in the life sciences and related fields. Example will cover mixed models with continuous normally distributed data and then with count data (with integer values 0,1,2,…), and with proportion data (with the number of successes out of a specified number of trials). We also cover the basics of random effects, the concept of LSMeans, back-transforming estimates and CIs, creating interaction plots on the original data scale, warnings about the interpretation of differences, and the idea of overdispersion and accounting for this in the Poisson distribution with an experimental unit effect.

Go directly to these topics at these time stamps:

• 1:00 Overview of Resources
• 12:16 T-test
• 23:34 ANOVA
• 31:12 Interpret Compare Means
• 32:13 Multiple Comparisons Adjustments in ANOVA
• 34:30 Filter the results to only certain groups
• 34:59 Simple Regression (and Regression Through the Origin)
• 44:51 Polynomial Regression: Quadratic Fit
• 45:40 Log-Log and other special fits
• 46:03 Spline Fit and Cubic Fit
• 46:48 Regression in Graph Builder
• 48:00 Fit Model, with ANOVA + Regression in one model
• 49:41 Concept Applets in JMP
• 52:50 Good Regression Data Sets: Anscombe, Ro, Slope