Data Analysis and Statistics Using JMP Software for Engineers and Scientists

This 3-day course is the most popular of our standard training courses. It combines the use of JMP software with basic, intermediate, and advanced analytical methods, including descriptive statistics, graphical analysis, hypothesis testing, analysis of variance, and model building. Interactive illustrations enable participants to understand statistical concepts by engaging and experimenting with them. For guidance, a set of flowcharts is provided for integrating JMP tools into a process that shows participants where they are, where they are going, and when they have fully completed the analysis. This course also includes specific advice on how to reap the benefits of these methods in real-world scenarios.

Taking this course in 3 consecutive days leads to higher retention, better understanding of how the methods relate to one another, and more opportunity to harness the benefits of statistical methods right away.

Who Should Attend

Engineers, scientists, and technicians who intend to make improvements to products and/or processes


3 days


General familiarity with computers and spreadsheet software, such as Microsoft Excel, is helpful

Expected Results

After completing this course, participants will be able to:

  • Import and arrange data for analysis including JMP productivity features for data tables
  • Overcome challenges in data format with a variety of JMP tools
  • Explore data visually and interactively
  • Create summaries, tables, charts, and graphs in the JMP software application
  • Solve a variety of scientific and engineering problems from single to multiple factors and responses
  • Learn how to recognize and overcome statistical challenges
  • Describe and analyze the distribution of data
  • Understand issues related to sampling and calculate appropriate sample sizes
  • Generate and analyze statistical intervals and hypothesis tests to make data-driven decisions
  • Complete univariate, bivariate and multivariate analyses using both continuous and categorical factor variables
  • Identify problems with analyses, such as violating statistical assumptions, and how to solve them
  • Convert data from analyses into presentations.