JMP Statistical Discovery
  • Welcome Edit Profile

    • About JMP
    • Our Leadership
    • International Offices
    • Careers with JMP
    • Contact Us
  • Corporate Site (www.jmp.com)

    Worldwide Sites

    • Argentina
    • Australia
    • Austria
    • Belgium
    • Brazil
    • Canada
    • Chile
    • China
    • Colombia
    • Denmark
    • Finland
    • France
    • Germany
    • Hong Kong
    • Iceland
    • India
    • Italy
    • Japan
    • Korea
    • Malaysia
    • Mexico
    • Netherlands
    • Norway
    • Peru
    • Philippines
    • Singapore
    • Spain
    • Sweden
    • Switzerland
    • Taiwan
    • United Kingdom
    • United States

    JMP International Offices

    To access contact information for all of our worldwide offices, please visit the JMP International Offices page.

  • Why JMP
    • JMP Overview
    • Analytic Workflow
    • JMP for the Enterprise
    • Build an Analytics Culture
    • Our Customers
    • JMP Academic Program
  • Products
    • Software Overview
    • JMP
    • JMP Pro
    • JMP Live
    • JMP Clinical
    • New in JMP 17
  • Capabilities
    • Capabilities Overview
    • Advanced Statistical Modeling
    • Automation and Scripting
    • Basic Data Analysis and Modeling
    • Consumer and Market Research
    • Content Organization
    • Data Access
    • Data Blending and Cleanup
    • Data Exploration and Visualization
    • Design of Experiments
    • Mass Customization
    • Predictive Modeling and Machine Learning
    • Quality and Process Engineering
    • Reliability Analysis
    • Sharing and Communicating Results
  • Industries
    • Industries Overview
    • Chemical
    • Consumer Products
    • Pharmaceutical
    • Semiconductor
  • For Users
    • Learn to Use JMP
    • JMP Community
    • Discovery Summits
    • Get Support
  • Buy JMP
  • Try JMP

JMP Learning Library

    • All Topics
    • JMP Basics
    • Graphical Displays and Summaries
    • Probabilities and Distributions
    • Basic Inference - Proportions and Means
    • Correlation and Regression
    • Time Series
    • Multivariate Methods
    • Mixed Models and Repeated Measures
    • Data Mining and Predictive Modeling
    • Quality and Process
    • Reliability and Survival
    • Using SAS from JMP
  • Download All Guides

Design and Analysis of Experiments

Learn how to design and analyze various types of statistical experiments (e.g., full factorial, fractional factorial, custom) to discover the factors that most impact an outcome from those that have little to no effect. Compare different experimental designs to determine the one that is best for the desired objectives.

  • DOE Full Factorial DesignDesign a full factorial experiment.
  • DOE Full Factorial AnalysisAnalyze a full factorial experiment.
  • DOE Fractional Factorial DesignDesign a fractional factorial experiment.
  • DOE Fractional Factorial AnalysisAnalyze a fractional factorial experiment.
  • DOE Screening Experiment AnalysisAnalyze a two-level screening experiment.
  • DOE - Custom DesignsDesign a variety of optimal experiments.
  • DOE - Evaluate and Compare DesignsEvaluate and compare the properties of experimental designs.
  • Monte Carlo SimulationUse Monte Carlo simulation to estimate the distribution of a response variable as a function of a model fit to data and estimates of random variation.

Want them all?

Download all the One-Page PDF Guides combined into one bundle.

Download PDF bundle

About

  • Why JMP
  • Products
  • Capabilities
  • Industries
  • Academic Program

Resources

  • Live Events
  • Our Customers
  • Online Statistics Course
  • Statistics Knowledge Portal
  • Resource Center

For Users

  • User Community
  • Learn to Use JMP
  • Online Documentation
  • Discovery Summits
  • Support

Company

  • About Us
  • Blogs
  • Careers
  • Contact Us
  • © 2023 JMP Statistical Discovery LLC. All Rights Reserved.
  • Terms of Use
  • Privacy Statement
  • Contact Us
Back to Top