Resource Center
White papers, book chapters and articles
-
- All Application Areas
- Design of Experiments
- Data Visualization and Exploratory Data Analysis
- Quality Engineering, Reliability and Six Sigma
- Statistics, Predictive Modeling and Data Mining
- Consumer and Market Research
- Dashboard Building
- Life Sciences
- Academic
- Application Development
- Culture of Analytic Excellence
-
Dark data and the pandemicAuthor David Hand explains the novel statistical challenges presented by the novel coronavirus.
-
The Profiler at 30Brad Jones, the inventor of JMP's Custom Designer and Prediction Profiler , introduces one of his favorite new features in JMP 16: Extrapolation Control .
-
Building analytics communities across the enterpriseAlex Pamatat explains how NXP Semiconductors empowers its people to use analytics throughout their organization.
-
Moving from SPSS to JMP: A Transition GuideThis guide explains how SPSS and JMP differ in the handling of descriptive statistics and visualizations. It is meant to ease the user’s transition from SPSS Version 27 to JMP 16 and JMP Pro 16.
-
Just add water....Tim Gardner shows how applying designed experiments can turn something as simple as "adding a little water" into an operational gain worth hundreds of thousands of dollars per run.
-
Cautionary Tales in Designed ExperimentsThe beauty of DOE is about learning—from mistakes, from trying new things, and from working with others.. In this free e-book legendary author Dr. David Salsburg looks at the history of DOE and shares what to do (and what not to do) to successfully execute designed experiments.
-
The CatalystIn Catalyst, Wharton Professor and author Jonah Berger explores change and its impact on human behavior. Taken from the introduction, this excerpt discusses how we can discover the hidden barriers preventing change and learn how to mitigate them.
-
Dark DataIn Dark Data, Dr. Hand explores the many ways we can be blind to missing or unseen data and how, in our rush to be a data-driven society, we might be missing things that matter, leading to dangerous decisions that can sometimes have disastrous consequences.
-
JMP takes the struggle out of data wranglingThis paper provides an introduction to the unglamorous, time-consuming, laborious, and sometimes dreaded “dirty work” of statistical investigations – data preparation. The good news: JMP can perform these operations with ease.
-
Art of StatisticsIn this introduction from his book, Art of Statistics, Sir David Spiegelhalter outlines why it's critically important to use statistical science to answer the kind of questions that arise when we want to better understand the world.
-
Measurement Systems Analysis for Curve DataJMP Pro does an impressive job quickly and simply solving Measurement Systems Analysis with curve data, a complex and relevant problem.
-
Never Stop LearningIn this chapter from Bradley Staats book, Never Stop Learning, you'll learn strategies for overcoming an inward focus, building relationships, and how best to learn from, and teach, others.
-
JMP Synergies: Using JMP and JMP Pro With Python and R There may be occasions where you'll want (or need) to use JMP in conjunction with open source tools. This paper will help you to get started using the Python and R connections in JMP.
-
The JMP Design of Experiments AdvantageThis paper details the state-of-the-art DOE capabilities offered by JMP to help you tailor designs to your specific problem and resource limitations.
-
The Integration of Big Data Analytics into a More Holistic ApproachThis paper explores the fundamentals of big data analysis, showing how big data applications can be better integrated to achieve a more streamlined, holistic outcome.
-
Improving Processes with Statistical ModelsHow, when and where do I apply statistical modeling to a problem? Find out in these two real-world case studies.
-
Optimizing Processes with Design of ExperimentsWhat is design of experiments (DOE), and how does it deliver value? Through a case study approach, this white paper answers these questions and more.
-
Reliability Data AnalysisLearn about new trends in the statistical assessment of product reliability from expert Dr. Bill Meeker of Iowa State University.
-
Optimizing Pharmaceutical Production Processes Using Quality by Design MethodsGood news! Did you know that your quality by design goals can be derived in a straightforward way, the results can be easy to verify and that this method allows for further improvement of the processes without the need for re-registration? Find out more with this case study from pharma.
-
Predictive Analytics Via Text MiningDownload a complimentary chapter about text mining for predictive analytics.
-
Effective Visualization Techniques for Data Discovery and AnalysisHow do you communicate highly technical data to a non-analytical audience? Find out how in this paper on best practices in data visualization.
-
Tap Into Unstructured DataThis white paper discusses ways to explore, analyze and use insights from unstructured text data.
-
Optimal Design of Experiments: A Case Study ApproachWhat is a blocked experiment? When is one used and how is it constructed? Find out in this case study.
-
Conjoint AnalysisThis article explains how a local grocer seeks to increase beer revenue by better understanding customer preference, pricing strategy and packaging with conjoint analysis.
-
Evaluating the Measurement Process: A Better Way to Do Repeatability & Reproducibility StudiesStatistician, author and quality expert Donald J. Wheeler, explains data collection strategy in the context of a basic EMP and gage R&D study.
-
Split-Plot Designs: What, Why, and HowLearn how to design and analyze split-plot experiments and why split-plot designs are often cheaper and more efficient than other methods.
-
Statistics with JMP: Hypothesis Tests, ANOVA and RegressionAnother great instruction guide by Professors Peter Goos and David Meintrup! This chapter specifically covers discrete and continuous random variables, with a focus on bivariate probability distributions and densities.
-
Guaranteeing the Quality, Efficacy and Safety of PharmaceuticalsLearn best practices for ensuring quality, efficacy and safety across all levels of drug development and manufacturing.
-
Moving From Minitab to JMP: A Transition GuideA practitioner's review of JMP vs. Minitab: unearth two different approaches to common statistical techniques and principles.
-
Survey Data Analysis: The FundamentalsNew case study demonstrates best practices for survey analysis in JMP (including tips for dealing with missing data).
-
Smart Machine Learning for ManufacturersThis paper introduces the 'smart machine learning' framework which unites the need to understand both the unique aspects of the data that is collected and the perspective of the data scientists who oversee its collection.
-
Pharmaceutical Quality by Design Using JMPLearn how QbD and good data visualization is used in early stage product development to help pharma organizations create more effective quality controls.
-
Big Data, Pharma 4.0 and Process ModelingHow can you prepare for Industry 4.0? This paper outlines how to improve process knowledge, close the gaps for legacy products and set the foundation for big data use.
-
Combinatorial Testing: An Approach to Systems and Software Testing Based on Covering ArraysThis chapter explores the testing approach known as combinatorial testing, using examples derived from real software systems.
-
Basics of Experimentation and Response Surface Methodology This book chapter explores the fundamentals of good experimentation. The authors' approach uses models to understand formulation systems and identify formulations that meet study objectives.
-
Mixed Models: The Flexible Solution For Correlated DataWhy are mixed models at the center of so many analyses? Russ Wolfinger explains in these seven diverse case studies.
-
Why Design of Experiments Keeps Science in ScienceA former Kodak chemist outlines how DOE spurs process improvement and efficiency, leaving more time for researchers to focus on the science they love.
-
Beyond Lean Six SigmaTwo leading quality engineers discuss the importance of adopting a holistic improvement strategy, while acknowledging no one methodology is best for all.
-
Digital Transformation StrategyCompanies are making big investments in data and analytics, generating increased demand for skilled analytical talent and many other business challenges. In this article, Stan Maklan proposes a framework for addressing some of these obstacles.
-
Experimental Design MethodsSelect a design with N runs: How to negotiate experiment size when choosing a design.
-
Variable selection: the most important problem in statistics?Renowned statistician Brad Efron name variable selection as the most important problem in the field, but why? This article will answer while outlining the key objectives behind variable selection, main methods and tips for selection.
-
Three Ways to Create More Effective Data VisualizationsAuthor and distinguished info graphic expert outlines the three "must do's" for better visualizations. He also explains how he's used these techniques in his own work and shows how you can easily apply to yours.
-
What Is Experimental Design?Experiments are more than a demonstration of scientific principles. Bradley Jones describes a utopia where experimental design is a standard engineering procedure and where all products get to market more quickly with better quality and lower cost.
-
The Analytics Lifecycle ToolkitDownload a complimentary chapter from The Analytics Lifecycle Toolkit, which provides a framework for the effective use of analytics at your organization.
-
Problem Solving for New Engineers: What Every Engineering Manager Wants You to KnowDownload a complimentary chapter about the strategy and tools needed to solve problems through experimentation.
-
Information Quality: The Potential of Data and Analytics to Generate KnowledgeNew case studies: find out how Information Quality was used as a guide to predict water quality and control a film deposition process.
-
Class of Three-Level Designs for Definitive Screening in the Presence of Second-Order EffectsIntroducing….a class of economical three-level designs for screening quantitative factors.
-
Definitive Screening Designs with Added Two-Level Categorical FactorsInventors of Definitive Screening designs share a new development in this reprint from the Journal of Quality Technology.
-
Building Better Models with JMP ProDiscover the “what, why and how” of neural networks through case studies, figures and exercises. Examples demonstrate how models are used for both classification and prediction across an array of applications.
-
Statistics with JMP: Graphs, Descriptive Statistics and ProbabilityNeed a better understanding of basic statistical theory and its applications? Let Professors Peter Goos and David Meintrup be your guide! An overview of descriptive statistics for nominal, ordinal and quantitative data, with particular attention to graphical representations is provided.
-
Detecting Fraud at the Clinical SiteOrganizations find a better way to protect participants in clinical trials. Learn the graphical and statistical approaches being used to identify site- and patient-perpetrated fraud.
-
Advantages of Bootstrap Forest for Yield AnalysisMaintain a high level of quality and control (in less time) with the bootstrap forest method. Here’s how!
-
Discovering Partial Least Squares with JMPSee how Partial Least Squares (PLS) is used to solve a drug development dilemma. You'll also learn how this flexible statistical technique can be used to models relationships--even those with noisy inputs and multiple outputs
-
Explaining Reliability GrowthLearn about statistical techniques used to improve product and process reliability over time, and see specific examples using JMP software.
-
Data Exploration in Preparation for ModelingCase study: uncover the process a leading catalog retailer uses to prep data before building response models.
-
Data Mining Techniques, Third EditionData Miners, Inc. co-founders explain how to create derived variables through a study on modeling customer attrition.
-
Visualizing Change: An Innovation in Time-Series AnalysisData in motion: how good interactive graphs illustrate the shape, velocity and direction of change.
-
Statistical Intervals: Confidence, Prediction, EnclosureThis paper uses a manufacturing example to describe the differences between confidence, prediction and tolerance intervals.
-
Visual Six Sigma: Making Data Analysis LeanThis paper introduces the idea of "Visual Six Sigma," a practical, pragmatic and yes, visual approach to data analysis and process improvement.
-
How R&D Labs Can Learn More From Their Positive ControlsThe ultimate goal is to build models that predict a future that can be precisely reproduced by anyone,at any time So, how can two people get different results despite using the same equipment and protocols? Find out.
-
Generating Adverse Event Narratives Using JMP ClinicalNew analytical process available to streamline the time-consuming task of creating patient narratives. Learn more about this function in JMP Clinical.
-
Risk-Based Monitoring of Clinical Trials Using JMP ClinicalClinicians discover a better way to protect the well-being of patients by reviewing trial data remotely.
-
The Improbability PrincipleDownload a complimentary chapter from David Hand's well-known book, The Improbability Principle: Why Coincidences, Miracles, and Rare Events Happen Every Day.