Skip to main content

Foundations of Data Analysis for Product and Process Improvement

About this Course

It’s essential that you not only master core data analysis skills, but that you quickly apply them to your work. 

That’s why Foundations of Data Analysis for Product and Process Improvement provides you with the hands-on skills you need to get ahead. 

During the course, you will master basic to advanced analytical methods, you will solve problems that arise in real-world scenarios, and most importantly, you will improve the effectiveness of your work. 

What you will learn

Fundamental data analysis skills. These include:

  • Creating samples and calculating appropriate sample sizes
  • Describing and analyzing distributions of data
  • Performing graphical analysis, hypothesis testing, analysis of variance, and model building
  • Solving scientific and engineering problems from single to multiple factors and responses
  • Identifying and overcoming statistical challenges

The fundamental skills needed to understand and use JMP. These include:

  • Importing and arranging data
  • Avoiding time-consuming tasks using JMP productivity features
  • Solving formatting challenges using JMP tools
  • Visualizing and exploring your data
  • Converting data into presentations

Skills to uncover deep insights into your products and processes.

Who Should Attend

Engineers, scientists and technicians who work in product and process development or manufacturing

Duration
3 days

Prerequisites
Familiarity with computers and spreadsheet software, such as Microsoft Excel

Pricing
Individual and corporate pricing available on request

Instructors

Cy Wegman
Sr. Analyst

Cy Wegman

Specialist in data analytics, empirical modeling and optimization

BSCE: Rose Hulman Institute of Technology
• 38 years Procter & Gamble
• Paper Manufacturing, Paper Engineering, Global Engineering
• Corporate Leader for Empirical Modeling & Optimization
• P&G Prism Society
• 7 years Predictum

Cy Wegman specializes in data analytics and empirical modeling and optimization. Before joining Predictum, he worked for 38 years at Procter & Gamble, where his last assignment was the Empirical Modeling leader for the company.

Cy is a member of the P&G Prism Society, which represents the top 1% of P&G engineers, for profitably applying his technical mastery. His contributions have resulted in hundreds of millions of dollars in annual savings. His cross-industry work in manufacturing, engineering, and R&D has spanned molecular-scale to full-scale technologies, as well as material, formulation, process, packaging, and consumer models.

Cy holds a B.S. in Civil Engineering from Rose-Hulman Institute of Technology.

Marie Gaudard
Sr. Data Scientist

Marie Gaudard

Specialist in data science, predictive modeling, design of experiments, and machine learning.

Marie is a Senior Data Scientist at Predictum. She specializes in predictive modeling, design of experiments, statistics, and machine learning. She has extensive experience in consulting and statistical training in a wide variety of industries.
Marie is Professor Emerita of Statistics at the University of New Hampshire (UNH), where she has worked extensively with students and companies on the practical application of statistics. She is also a co-author of two books, one of which is about the use of JMP software and statistical methods to improve quality and the other is about the partial least squares technique. She was also a statistical writer for several years as a member of the JMP documentation team at SAS Institute.

Marie holds a Ph.D. in Statistics from the University of Massachusetts at Amherst.

Course Syllabus

Day 1

  • Introduction to JMP
    • Navigating JMP 
    • Data Cleaning
    • Data Exploration
  • Graphical Analysis
    • Univariate Graphs
    • Bivariate Graphs
    •  Multivariate Graphs

Day 2

  • Inferential Analysis
    • Means Tests
    • Standard Deviation/Variance Tests
    • Proportion Tests 
    • Testing for Normality
  • Intervals
    • Confidence Intervals
    • Prediction Intervals
    • Tolerance Intervals: Establishing Expectations and Specifications

Day 3

  • ANOVA
    • Analysis of Variance
    •  One-Way ANOVA in JMP
    • Multiple Comparisons
    • Nonparametric Tests
  • Model Building 
    • ANOVA
    • Regression
    • Combine Continuous and Nominal Factors
    • Design of Experiments (DOE)

Knowledge you can put to work