Many experiments in engineering, science and business involve several factors. This course is an introduction to these types of multifactor experiments. The appropriate experimental strategy for these situations is based on the factorial design, a type of experiment where factors are varied together. This course focuses on designing these types of experiments and on using the ANOVA for analyzing the resulting data. These types of experiments often include nuisance factors, and the blocking principle can be used in factorial designs to handle these situations. As the number of factors of interest grows full factorials become too expensive and fractional versions of the factorial design are useful. This course will cover the benefits of fractional factorials, along with methods for constructing and analyzing the data from these experiments.
Factorial and Fractional Factorial Designs
This course is part of Design of Experiments Specialization
Instructor: Douglas C. Montgomery
6,144 already enrolled
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(75 reviews)
What you'll learn
Conduct a factorial experiment in blocks and construct and analyze a fractional factorial design
Apply the factorial concept to experiments with several factors
Use the analysis of variance for factorial designs
Use the 2^k system of factorial designs
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There are 4 modules in this course
What's included
9 videos5 readings2 assignments1 app item1 discussion prompt
What's included
12 videos1 reading2 assignments
What's included
4 videos1 reading2 assignments
What's included
9 videos1 reading2 assignments1 peer review
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Recommended if you're interested in Probability and Statistics
Arizona State University
Arizona State University
Arizona State University
Arizona State University
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Reviewed on Sep 20, 2020
thanks montgomery sir, and thanks to arizona state university
Reviewed on Jul 25, 2020
Gain valuable insights into the Design of Experiments.
Reviewed on Jun 13, 2023
The course was very nice. The only issues I encountered concerned the fact that the questions in the quiz were not always matching with what was explained in the lectures.
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