Resource Center
White papers, book chapters and articles
-
- All Application Areas
- Academic
- Analytical Application Development
- Consumer and Market Research
- Dashboard Building
- Data Visualization and Exploratory Data Analysis
- Design of Experiments
- Life Sciences
- Quality Engineering, Reliability and Six Sigma
- Statistics, Predictive Modeling and Data Mining
-
Ad Hoc and Statistical Model Visualization Using JMP, SAS and Microsoft ExcelLearn how creators of scientific, spreadsheet and statistical models can communicate their findings to each other more effectively.
-
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!
-
Analyzing and Interpreting Continuous Data Using JMPDownload a complimentary chapter from Analyzing and Interpreting Continuous Data Using JMP by Brenda Ramirez and Jose Ramirez.
-
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.
-
Big Statistics Will Force Us to Change Our WaysIf you only had one measurement to scrutinize, then it would easy. But what should you do when the problem is big? SAS Co-Founder John Sall has a few ideas.
-
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.
-
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.
-
Classification of Breast Cancer Cells Using JMPSee how the Wisconsin Breast Cancer Diagnostic Data Set is being used to classify malignant and benign tumors.
-
Component Integrated Importance: Modeling Complex Aging SystemsLearn about the two alternative methods that are better suited to evaluate aging systems.
-
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.
-
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.
-
Data Visualization TechniquesExcel has made it simple for anyone to make data graphics, just like the smartphone has turned everyone into filmmakers. Fung gives some specific examples on how to bring a new level of professionalism to your graphics.
-
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.
-
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.
-
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.
-
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
-
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.
-
Efficient Modeling & Simulation of Biological Warfare Using Innovative Design of Experiments MethodsSee how proven DOE methods can be used to model complex scenarios involving many variables – such as biological warfare attacks.
-
Evaluating the Measurement Process: A Better Way to Do Reliability & 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.
-
Experimental Design MethodsSelect a design with N runs: How to negotiate experiment size when choosing a design.
-
Explaining Reliability GrowthLearn about statistical techniques used to improve product and process reliability over time, and see specific examples using JMP software.
-
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.
-
Implementing CDISC Using SAS: An End-to-End ApproachJMP Clinical automates the data review process for you. Learn about new built-in processes for evaluating patient demographics, constructing patient profiles and event narratives, summarizing adverse events and exploring and reviewing safety data.
-
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.
-
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.
-
JMP Extensibility Synergy with MATLABLearn how to integrate JMP with MATLAB. Three case studies show how to best take advantage each tool's strengths.
-
Lean Data Analysis: Simplifying the Analysis and Presentation of Data for Manufacturing Process ImprovementPredict, analyze, improve and control the quality of your product with helpful tips in this article on data analysis maturity models.
-
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.
-
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.
-
Moving from SPSS to JMP: A Transition GuideSPSS vs. JMP: What are the key differences between these two data analysis tools?
-
NumbersenseDo you want to better target your marketing offers to potential customers using predictive modeling? Statistician Kaiser Fung explains his definition of successful targeting and the law of diminishing returns.
-
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.
-
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.
-
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.
-
Predictive Analytics for the Eyes and MindWhat makes for great data visualization? Find out from expert Stephen Few, IT Innovator, Author and Prinicipal of Perceptual Edge.
-
Predictive Analytics Via Text MiningDownload a complimentary chapter about text mining for predictive analytics.
-
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.
-
Reliability Data AnalysisLearn about new trends in the statistical assessment of product reliability from expert Dr. Bill Meeker of Iowa State University.
-
Risk-Based Monitoring and Fraud Detection in Clinical Trials Using JMP and SASCreate a centralized view of trial data to alert you to quality and safety issues with this introductory guide.
-
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.
-
SPC Data Visualization of Seasonal and Financial Data Using JMPVoila: how to access your Shewhart, CUSUM and moving average charts with a simple drag and drop interface.
-
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.
-
Spreadsheets: Friend or Foe? A Best Practice Approach for Conducting Your What-if AnalysesThis paper highlights the inherent risks and compromises in using a spreadsheet tool like Excel to analyze data. There’s a better way!
-
Statistical Intervals: Confidence, Prediction, EnclosureThis paper uses a manufacturing example to describe the differences between confidence, prediction and tolerance intervals.
-
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.
-
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.
-
Survey Data Analysis: The FundamentalsNew case study demonstrates best practices for survey analysis in JMP (including tips for dealing with missing data).
-
Tap Into Unstructured DataThis white paper discusses ways to explore, analyze and use insights from unstructured text data.
-
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.
-
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.
-
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 Perils of Technical JargonEditor Adam Brownsell reports that each and every one of us should be able to explain the biggest of ideas with small words and modest intent.
-
The State of Market ResearchWhat are the top two tools market researchers need to turn data into information through analysis? Find out in this chapter that outlines the state of market research.
-
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.
-
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.
-
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.
-
Visual Six Sigma: Making Data Analysis LeanFind out how Visual Six Sigma was used to discover what was plaguing a plastic manufacturer’s polymer production for nearly 20 years.
-
Visualizing Change: An Innovation in Time-Series AnalysisData in motion: how good interactive graphs illustrate the shape, velocity and direction of change.
-
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.
-
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.