Design and analysis of experiments

This is a work in progress. It will be completed by the end of the day on 04 January 2016.

Learning outcomes

Learn the basic terminology of experiments: responses, factors, outcomes, real-world units vs coded units, confounding
Analyze and interpret data from an experiment with 2, 3 or more factors by hand
Use R to do the analysis, and interpret the various plots, such as the Pareto plot
Analyze and interpret data from an experiment with 3 or more factors
Recognize when to use a fractional factorial, to go mostly the same results as from a full factorial
Understand when to use screening experiments
Use the concepts of response surface methods to systematically reach an optimum
What you can do when you make a mistake, or hit against constraints.

Extended readings/practice

Don't accept what is often believed to be true: always experiment.
In case you've been wondering how the trade-off table was constructed.
You must cover steps 16, 21 and 22 of the detailed software tutorial to master this section of the course.

Resources

Class notes 2015
Class notes 2014
Textbook, chapter 5
Quizzes (with solutions): attempt these after you have watched the videos

Tasks to do first	Quiz	Solution
Watch videos AAA	Quiz	Solution
Watch videos BBB	Quiz	Solution
Watch videos CCC	Quiz	Solution
Watch videos DDD	Quiz	Solution
Watch videos EEE	Quiz	Solution
Watch videos FFF	Quiz	Solution

Class videos from prior years

Videos from 2015

00 - Introduction video for the Coursera online course [01:56]
1A - Why experiments are so important [07:48]
1B - Some basic terminology [06:37]
1C - Analysis of your first experiment [09:00]
1D - How NOT to run an experiment [03:07]
2A - Analysis of experiments in two factors by hand [13:37]
2B - Numeric predictions from two-factor experiments [07:25]
2C - Two-factor experiments with interactions [15:15]
2D - In-depth case study: analyzing a system with 3 factors by hand [17:28]
3A - Setting up the least squares model for a 2 factor experiment [05:46]
3B - Solving the mathematical model for a 2 factor experiment using software [08:46]
3C - Using computer software for a 3 factor experiment [08:37]
3D - Case study: a 4-factor system using computer software [09:03]
4A - The trade-offs when doing half-fraction factorials [13:20]
4B - The technical details behind half-fractions [09:38]
4C - A case study with aliasing in a fractional factorial [06:38]
4D - All about disturbances, why we randomize, and what covariates are [11:00]
4E - All about blocking [09:21]
4F - Fractional factorials: introducing aliasing notation [12:00]
4G - Fractional factorials: using aliasing notation to plan experiments [10:45]
4H - An example of an analyzing an experiment with aliasing [09:50]
5A - Response surface methods - an introduction [06:13]
5B - Response surface methods (RSM) in one variable [18:40]
5C - Why changing one factor at a time (OFAT) will mislead you [05:33]
5D - The concept of contour plots and which objectives should we maximize [03:40]
5E - RSM in 2 factors: introducing the case study [19:20]
5F - RSM case study continues: constraints and mistakes [13:45]
5G - RSM case study continues: approaching the optimum [17:05]
06 - Wrap-up: the course in review, multiple objectives, and references for the future [08:10]