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Home DOE

Design of Experiments (DOE)

 

WHAT IS DOE?


DOE or Experimental Design is defined as the design of any information-gathering exercises where variation is present,whether under the full control of the experimenter or not.It refers to the process of planning,designing and analysing the experiment so that valid and objective conclusions can be drawn effectively and efficiently.

Duration – 2 days (16 contact hrs) Program Content
  • Introduction

    • Strategic Forces
    • Decision for Daily Problems
  • Analysis through Historical Data
    • Cause and Effect Diagram
    • Testing of Hypothesis
    • Regression
  • DOE Basics

    • What is DOE?
    • DOE – Is A Valuable Tool
    • Philosophy of DOE
    • Requirements of DOE
    • Terms & Terminologies
    • Stages of DOE
    • Creation of number of trials
    • Full Factorial Design
    • Standard Order: 2^3 Factorial Layout
    • How Full Factorial covers all combinations of X’s?
  • Example: Create Factorial Design

    • Creating a Design
    • Randomization: the Experimenter’s Insurance
    • Why Randomize: Example
    • Lurking Variables
    • Analyze the Response
  • Steps in Data Analysis
    • Add Response
    • Look for Problems
    • Residuals
    • Example: Residuals for 2^3 Design with 2 Replicates
    • Assumptions of DOE Analysis
    • Checking Assumptions About Residuals
    • Identifying Significant Effects
    • Effects Plot
    • Main Effects Plot
    • Interpreting Interaction Plots
    • Drawing Conclusions
    • Dropping Terms From the Prediction Equation
    • Dropping Terms From the Prediction Equation
    • Factor coefficients
    • Using Coefficients in the Prediction equation
    • Prediction equation
  • Replicated Full Factorial
    • Replicated Full Factorial: Example
    • Power
    • Power and Replication
    • Factors Influencing Power
    • Factors Influencing Power
    • Summary
  • Fractional Factorial Design:
  • Full Factorial Design
    • Runs Required: 2-Level Factorial/k Factors
    • Disadvantages of Full factorial
  • Half Fractional Factorial
    • 2^3 factorial
    • Half Fractional Factorial
    • Defining Equations
    • Need of half fraction
    • Alias Structure and Confounding
    • Importance of Interaction
  • Design Resolution
    • Definitions Leading to Resolution
    • Definition of Resolution
    • Finding Resolution
    • Choosing a Resolution
    • Example : Half Fraction
  • Nesting & Blocking
  • Nested factors
    • Aim
    • When to use nested factors?
    • Example – Creating Nested design
    • Create Nested design
    • Analysis
    • Results
    • Analysis of Variance table
    • Analysis for variance components
    • Analysis for expected mean squares
  • Blocking Variables
    • Blocking factor
    • Example
  • Block Design
    • Create factorial design
    • Analyze the design
    • Results
  • Screening of Experiments
    • Reducing Experimental Trials: Other Fractional Designs
    • Choosing the Design
    • Available Factorial Designs
    • Resolution
    • Resolution and Design Choice
    • Tips on Resolution
    • Notation 2^k-p
    • Screening Designs
      • Example: 6 Factor Screening
      • Confounding
      • Tips for Screening Designs
      • Summary
    • Full Factorial Design with More than Two Levels
      • Full Factorial Designs
      • Designing a Full Factorial
      • Specifying the Design
      • Analyzing the Design
      • Analyzing the Design
      • DOE Checklist
    • The Response Surface Methodology:
      • The basics of robust designs
      • Taguchi designs
      • A short robust design example
  •  
       

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