15. Match each experiment design to its description. Answer Description Correlation matrix Resolution III Resolution V Generator Folding Confounding Half-fractional factorial Resolution IV Pareto chart of effects Plackett-Burman (a) Describes how a term in a model gets contributions from/ is polluted by other terms in the model (b) Method of adding runs to a Resolution III design to make it Resolution IV (c) Bar chart of effect size or t statisic used to display coefficient magnitude and signifi- cance (d) Design that confounds main effects with two-factor interactions (e) Design that confounds main effects with four-factor interactions (f) Design that confounds two-factor interactions with each other (g) Experiment design with one-half of the runs from a full-factorial design (h) Confounding relation used to create additional columns (one or more) of factor levels in a fractional factorial design (i) Reveals the degree of confounding between all of the terms that could be included in a model (j) Experiment design with an integer multiple of 4 runs that can have Runs - 1 study variables
15. Match each experiment design to its description. Answer Description Correlation matrix Resolution III Resolution V Generator Folding Confounding Half-fractional factorial Resolution IV Pareto chart of effects Plackett-Burman (a) Describes how a term in a model gets contributions from/ is polluted by other terms in the model (b) Method of adding runs to a Resolution III design to make it Resolution IV (c) Bar chart of effect size or t statisic used to display coefficient magnitude and signifi- cance (d) Design that confounds main effects with two-factor interactions (e) Design that confounds main effects with four-factor interactions (f) Design that confounds two-factor interactions with each other (g) Experiment design with one-half of the runs from a full-factorial design (h) Confounding relation used to create additional columns (one or more) of factor levels in a fractional factorial design (i) Reveals the degree of confounding between all of the terms that could be included in a model (j) Experiment design with an integer multiple of 4 runs that can have Runs - 1 study variables
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Question

Transcribed Image Text:15. Match each experiment design to its description.
Answer
Description
Correlation matrix
Resolution III
Resolution V
Generator
Folding
Confounding
Half-fractional factorial
Resolution IV
Pareto chart of effects
Plackett-Burman
(a) Describes how a term in a model gets contributions from/ is polluted by other terms
in the model
(b) Method of adding runs to a Resolution III design to make it Resolution IV
(c) Bar chart of effect size or t statisic used to display coefficient magnitude and signifi-
cance
(d) Design that confounds main effects with two-factor interactions
(e) Design that confounds main effects with four-factor interactions
(f) Design that confounds two-factor interactions with each other
(g) Experiment design with one-half of the runs from a full-factorial design
(h) Confounding relation used to create additional columns (one or more) of factor levels
in a fractional factorial design
(i) Reveals the degree of confounding between all of the terms that could be included in
a model
(j) Experiment design with an integer multiple of 4 runs that can have Runs - 1 study
variables
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