ELearning Library

Design of Experiments


Title: Design of Experiments
Course Type: Self Paced
Estimated time to complete: 14 Hours

Price: $495.00

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The challenge facing corporations is to cut design and development time while producing low-cost quality products that are ready to perform. Many organizations are being challenged to cut delivery time in half or more.

Demanding that people work harder is not the solution. Providing proper tools aids people in working more efficiently and effectively. Experimental Design is a premier tool in helping meet these challenges. By understanding and applying Experimental Design techniques, scientists, researchers, and engineers typically obtain a 50% reduction in the time required to conduct tests. This translates into enhanced understanding of technologies, reduced design and development time, and decreased costs.

This course is geared toward the technician, engineer, scientist or manager who wants to understand how to conduct simple, but powerful, designed experiments without becoming statisticians. The course does not require previous experience with DOE or statistical methods. It does more then simply give the student the "theory" behind design of experiments. Numerous examples and "hands-on" techniques are used to help make the learning experience enjoyable. After completing this course, students will be able to set up and analyze their own designed experiments.

This course contains seven modules. An abbreviated course outline is below. For a more detailed outline, please contact us at

Course Outline

  • DOE Definition
  • Process Diagram
    • Inputs and Outputs
    • Responses
    • Factors
  • Examples
    • Factor Levels
    • Tree Diagram
    • Full Factorial Design
    • Design Matrix
    • Orthogonal Design
  • Data Collection
  • Analysis
    • Pareto Chart
    • Main Effects Plot
    • Hitting a Target
    • Contour Plot
  • Fractional Factorial Designs
  • Interactions
  • Aliasing
  • Fractional Factorial Example
    • Factors
    • Worksheet
    • Data Entry
    • Pareto Chart
  •  Analysis of Variance
    • Squared Multiple R
    • P (2 tail)
    • F Ratio
    • P Value
  • Optimizing Several Responses
  • Mountain Bike Tire Example
    • Factors and Responses
    • Factor Levels
    • Design Matrix
  • Potential Data Collection Problems
    • Measurement Errors
    • Errors In Conducting the Experiment
    • Wrong assumptions regarding interactions
    • Process Changes
    • Extrapolation
  • Desirability Functions
  • Other Design Types
    • Plackett-Burman Design
    • Taguchi Designs
    • L4, L8, L9, L18, L12
  • Signal To Noise Ratio
    • Smaller Is Better
    • Larger Is Better
    • Nominal Is Better
  • Modeling Designs
    • Box-Behnken
    • Central Composite
  • D-optimal Designs
  • Types of Factors
    • Affect the average
    • Affect the variation
    • Affect average and variation
    • Have no effect
  • Example - "Hitting the Target"
  • Example - "Reducing Variation"
  • DOE Software
    • DOE Wisdom
    • Minitab


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