Design of Experiments (DOE) with Minitab – 3 day

Course Description
Design of Experiments (DOE) is an off-line quality improvement technique that can be employed to dramatically improve industrial products and processes. Through its use, it is possible to isolate the cause and effect linkages between product/process variables and the resulting output measures of function, quality, cost, and performance. This intensive course provides participants with in-depth understanding of the basic principles of experimental designs. Actual industrial examples are emphasized throughout, and the use of games such as catapults and helicopters accelerate the learning process.

At the end of the course, participants will have an opportunity to use their newly acquired knowledge to design, run, and analyze a series of experiments via Minitab computer simulation.

Who Should Attend
This course is appropriate for managers and engineers from process, research, development, quality, and production, as well as for production executives. Basic Statistics is a required prerequisite for this course.

Learning Objectives
Upon completion of the course, participants will:

  • Understand Experimental Design Fundamentals
  • Understand Product/Process Characterization
  • Be able to identify Key Product and Process Parameters for Optimization
  • Possess the knowledge and skills to reduce Cycle Time and Cost, and improve Product/Process Performance Relating to Cost, Quality, and Reliability
  • Have the ability to use Minitab to design and analyze experiments

Course Outline
How Experimental Design is Useful in Industry

  • The need for Experimental Design in Industry
  • Using Experimental Design as part of a Total Quality Solution
  • Attributes of a Well-Designed Experiment

Simple Comparative Experiments

  • Hypothesis Testing
  • Confidence Intervals
  • Two Sample Comparisons

Single-Factor Experiments

  • Randomized Experiments
  • Blocking and Nuisance Variables
  • Analysis of Variance
  • Simultaneous Multiple Comparisons
  • Residual Analysis for checking assumptions

Factorial Experiments

  • The General Factorial Design
  • The 2^k Factorial Design
  • Main Effects and Interactions: Analysis and Interpretation
  • Blocking in Factorial Designs
  • Unreplicated Full Factorial Designs

2^k-p Fractional Factorials

  • Hierarchy of Terms and Alias Structure
  • Resolution III, IV and V designs
  • Foldover Designs
  • Blocking Unreplicated Full/Fractional Factorials
  • Sequential Experimentation

DOE Planning

  • 10 Step Planning Guide
  • Reporting Formats and Examples

Demonstration and Use of Statistical Software for Design and Analysis

Course Format
24 hours
Combination lecture and classroom exercises
Available at QSG’s training facilities and on-site at your organization


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