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Introduction to the Design of Experiments (DOE)

Introduction to the Design of Experiments (DOE)

Variation is present in every experiment. Learn about DOE techniques to control variation, and to maximise data quality. Commonly used experimental designs are discussed as well as the statistical data analysis tools.

Course Details

Learning Objectives

Learning Objectives:

Upon completion of this module, participants will be able to: 

  • Understand the importance of statistical design of experiments and benefits in R&D
  • Learn the experimental designs most widely used in practice
  • Choose an appropriate experimental design based on the study objectives
  • Construct and implement the design selected
  • Analyze the data collected based on the design used and its underlying assumptions
  • Interpret the results of the experiment and report the conclusions 

Target Audience

Target Audience:

This module is aimed at all scientific staff who wish to design and implement efficient studies and experiments and who must make decisions based on the data collected.

Prerequisite

Prerequisite:

Participants should have an excellent working knowledge of the following topics: 

  • Calculation & interpretation of centrality and dispersion indicators : mean, median, standard deviation, standard error, coefficient of variation, quartiles, interquartile range
  • Use of Box-plots
  • The hypothesis testing approach
  • Confidence interval and p-values
  • α and β risks and their impact on the scope and the precision of the results
  • Power and sample size 

If this is not the case, participants must attend the training module Fundamental Tools in Statistics for Research.

Course Outline

Course Outline:
  • Sources of Variation
    • Why Design an Experiment?
    • Measurement Variability and Error
    • The Notion of Experimental Unit
    • Controlling and Minimizing Variability: Replication, Randomization, Blocking and Controls
    • Integrating Experimental & Budgetary Constraints into the Experimental Design
  • Constructing Experimental Designs
    • Two-Sample Designs (Complete Randomized Design, Paired Comparison Design)
    • Factorial Designs for more than Two Groups (Unreplicated and Replicated)
  • Statistical Analysis Tools
    • Exploratory Analysis
    • Student's T-Test (Independent and Paired T-Test) 
    • Analysis of Variance (ANOVA) / F-Test
  • The Notion of Interactions between Factors
  • Locating Statistical Differences with Multiple Comparison Techniques
  • Understanding and Interpreting Results from Real Data

Practical Info

Practical Info:

 

Recommended Duration: 2 days

Course Materials:

  • Course notes on statistical techniques
  • Sample datasets
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    Course Reviews

    • posted by Giovanna Sebastiani

      The course served to strengthen my understanding of concepts that I thought I knew. Furthermore, it provided me with the necessary tools to plan more powerful experiments from a statistical point of view. The course was well structured and quite enjoyable!

    • posted by Heather Averett

      This was a very useful class, Taught in a no-nonsense manner so everyone could apply the concepts to their research and walk away with practical knowledge for their jobs. Thank you. I found the class to be very beneficial and concisely taught.

    • posted by Matt Busch

      Nothing short of brilliant! There are a million things we could do, hundreds of things we should do, and only a handful of things we can do in a year. How do you decide which will have the greatest impact? Learn it here! Measuring the impact of your actions isn't always easy. Measuring the combined impact of your actions and their interactions is even harder. This course makes it easy and can easily be applied no matter what industry you are in.

    • posted by Dana Coombs

      This was a good introductory course to DOE. It was just the right amount of material for a 2 day class and focused more on application than equations. A background in basic descriptive and inferential statistics is needed.

    • posted by Randall Maxwell

      This was a good course in understanding the basics of DOE. I liked the focus on concepts in data analysis.

    • posted by Zoltan Bodor

      I have found that the "Introduction to the Design of Experiments" course is essential for anyone who wishes to apply a disciplined approach to practical applications in a product development or design applications. The program appropriately covered the required fundamentals by working through practical examples. The material was very well organized and due to having a small group all questions relating to concepts were very well explored. The instructor was very helpful and well acquainted with the subject matter.

    • posted by Roger L. Roy

      DOE helped me to understand the different sources of error and variability as well as the tools available to minimize variability - repetition, randomization, blocking and controls. I now have a much better understanding of simple experimental designs and more complex factorial designs - along with the corresponding statistical tools - t-test, and Analysis of Variance (ANOVA) F-test. The breaking down of the components of variability when performing an ANOVA was of great benefit. Moreover, the notion of an interaction between two factors and how to test multiple comparisons were also very useful. The XLSTAT add-in to Microsoft Excel provides powerful and efficient statistical tools that allowed me to obtain faster, more detailed, reliable and accurate results than I have seen with other statistical packages.

    • posted by Roger L. Roy

      DOE helped me to understand the different sources of error and variability as well as the tools available to minimize variability - repetition, randomization, blocking and controls. I now have a much better understanding of simple experimental designs and more complex factorial designs - along with the corresponding statistical tools - t-test, and Analysis of Variance (ANOVA) F-test. The breaking down of the components of variability when performing an ANOVA was of great benefit. Moreover, the notion of an interaction between two factors and how to test multiple comparisons were also very useful. The XLSTAT add-in to Microsoft Excel provides powerful and efficient statistical tools that allowed me to obtain faster, more detailed, reliable and accurate results than I have seen with other statistical packages.

    • posted by Ricky Ghilarducci

      Natalie, thank you so much for being such a great teacher. The Stats/DOE courses were absolutely brilliantly put together and taught. I learned so much in 3 days and feel so much more confident in my work environment now.

    Related Sessions

    • Several experiments are conducted to determine whether differences exists between procedures, methods, treatments. Learn about the design and the analysis of simple comparative experiments and more complex situations.

    • Efficient experiments must be large enough to detect meaningful scientific differences and maximize the use of available resources. Learn about sample size and power calculations.

    • Conceptually similar to PCA, correspondence analysis a method is designed for discovering associstions in categorical rather than continuous data. Discover the informative 2D-plots for efficient data mapping.

    • In preliminary research phases, the number of potentially influential factors to investigate is usually large. Screening designs are essential to identify the most influential factors with a reasonable number of runs in preliminary research phases.