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
Resources
- Class notes 2015 This is a large file, at 219 Mb. You might prefer the smaller set of notes from 2014, but they are less comprehensive.
- Class notes 2014
- Textbook, chapter 5
- What experiments are you going to do for your course project? What are the factors, what are the levels? How did you chose your levels? What is your response? How will you measure your response?
- Quizzes (with solutions): attempt these after you have watched the videos
Tasks to do first
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Quiz
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Solution
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Watch videos 1A, 1B, 1C and 1D
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Quiz
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Solution
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Watch videos 2A, 2B, 2C, 3A and 3B (note!)
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Quiz
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Solution
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Watch videos 2D, 3C and 3D and 4A (note!)
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Quiz
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Solution
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Watch videos 4B, 4C, 4D, and 4E
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Quiz
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Solution
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Watch videos 4F, 4G, 4H, 5A and 5B
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Quiz
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Solution
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Watch videos 5C, 5D, 5E and 5F
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Quiz
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Solution
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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]
Videos from 2014
See the webpage from 2014
Videos from 2013
See the webpage from 2013
Software codes for this section
Code to show how to build and plot a least squares model in R
Try this code in a web-browser