Machine Learning With a Human Touch
Introduction to DOE

Introduction to DOE

This course covers classical Design of Experiments – DoE techniques to design efficient experiments as well as tools to analyse the collected data. The principles of sample size calculations, strategies to remove and control undesirable sources of variability like the use of blocks and controls, as well as the most commonly used experimental designs will be discussed. The statistical analysis of designed experiments will be progressively introduced, starting with the t-test method used to compare two groups. Then, the analysis of variance technique (ANOVA) will be extensively covered from simple one-factor experiments to more advanced multi-factor situations where the interaction between factors needs to be considered. Multiple comparisons techniques used to locate differences will also be presented.

 

  • Sources of Variation
    • Variation is everywhere
    • 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
 
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.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 Fundamentals Statistical Tools for Research
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
    • Analyse the data collected based on the design used and its underlying assumptions
    • Interpret the results of the experiment & report the conclusions

Recommended Duration: 2 day(s)

Course Materials:

TBD

Case Studies:

N/A

This Session Has 8 Reviews

  1. 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!

  2. 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.

  3. 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.

  4. This was a great 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.

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

  6. 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.

  7. 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.

  8. 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.

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