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.
Dieser Kurs ist Teil der Spezialisierung Spezialisierung Versuchsplanung
von
Spezialisierung VersuchsplanungArizona State UniversityÜber diesen Kurs
Was Sie lernen werden
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
von
Arizona State University
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Lehrplan - Was Sie in diesem Kurs lernen werden
Unit 1: Introduction to Factorial Design
Unit 2: The 2^k Factorial Design
Unit 3: Blocking and Confounding in the 2^k Factorial Design
Unit 4: Two-Level Fractional Factorial Designs
Bewertungen
Top-Bewertungen von FACTORIAL AND FRACTIONAL FACTORIAL DESIGNS
Dense, very to the point and extremely useful course for me. I only wish there was more example videos in JMP.
thanks montgomery sir, and thanks to arizona state university
Gain valuable insights into the Design of Experiments.
Über den Spezialisierung Versuchsplanung
Learn modern experimental strategy, including factorial and fractional factorial experimental designs, designs for screening many factors, designs for optimization experiments, and designs for complex experiments such as those with hard-to-change factors and unusual responses. There is thorough coverage of modern data analysis techniques for experimental design, including software. Applications include electronics and semiconductors, automotive and aerospace, chemical and process industries, pharmaceutical and bio-pharm, medical devices, and many others.
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