DOE Glossary - Revision history

Lisa Hacker at 19:05, 15 September 2023

2023-09-15T19:05:00Z

← Older revision		Revision as of 19:05, 15 September 2023
Line 1:		Line 1:
	{{Template:~~DOEBOOK_SUB~~\|Appendix G\|DOE Glossary}}		{{Template:Doebook\|Appendix G\|DOE Glossary}}

	'''Alias'''		'''Alias'''

Richard House at 15:23, 10 August 2017

2017-08-10T15:23:37Z

← Older revision		Revision as of 15:23, 10 August 2017
Line 153:		Line 153:
	'''Full Model'''		'''Full Model'''

	The model that includes all the main effects and their interactions. In ~~DOE~~++, a full model is the model that contains all the effects that are specified by the user. ''See also'' Reduced Model.		The model that includes all the main effects and their interactions. In Weibull++ DOE folios, a full model is the model that contains all the effects that are specified by the user. ''See also'' Reduced Model.


Line 165:		Line 165:
	'''Hierarchical Model'''		'''Hierarchical Model'''

	In ~~DOE~~++, a model is said to be hierarchical if, corresponding to every interaction, the main effects of the related factors are included in the model.		In Weibull++ DOE folios, a model is said to be hierarchical if, corresponding to every interaction, the main effects of the related factors are included in the model.


Line 309:		Line 309:
	'''Reduced Model'''		'''Reduced Model'''

	A model that does not contain all the main effects and interactions. In ~~DOE~~++, a reduced model is the model that does not contain all the effects specified by the user. ''See also'' Full Model.		A model that does not contain all the main effects and interactions. In Weibull++ DOE folios, a reduced model is the model that does not contain all the effects specified by the user. ''See also'' Full Model.

Chris Kahn at 20:17, 30 January 2013

2013-01-30T20:17:25Z

← Older revision		Revision as of 20:17, 30 January 2013
Line 1:		Line 1:
	{{~~Doebook~~\|F}}		{{Template:DOEBOOK_SUB\|Appendix G\|DOE Glossary}}

	'''Alias'''		'''Alias'''

Chris Kahn at 15:59, 30 October 2012

2012-10-30T15:59:57Z

← Older revision		Revision as of 15:59, 30 October 2012
Line 15:		Line 15:
	'''ANOVA Model'''		'''ANOVA Model'''

	The regression model where all factors are treated as qualitative factors. ANOVA models are used in the analysis of experiments to identify significant ~~factor(s)~~ by investigating each level of the ~~factor(s)~~ individually.		The regression model where all factors are treated as qualitative factors. ANOVA models are used in the analysis of experiments to identify significant factors by investigating each level of the factors individually.


Line 47:		Line 47:
	A closed interval where a certain percentage of the population is likely to lie. For example, a 90% confidence interval with a lower limit of ''A'' and an upper limit of ''B'' implies that 90% of the population lies between the values of ''A'' and ''B''.		A closed interval where a certain percentage of the population is likely to lie. For example, a 90% confidence interval with a lower limit of ''A'' and an upper limit of ''B'' implies that 90% of the population lies between the values of ''A'' and ''B''.



	'''Confounding'''		'''Confounding'''
Line 53:		Line 53:
	Confounding occurs in a design when certain effects cannot be distinguished from the block effect. This happens when full factorial designs are run using incomplete blocks. In such designs the same linear combination of observations estimates the block effect and the confounded effects. ''See also'' Incomplete Blocks.		Confounding occurs in a design when certain effects cannot be distinguished from the block effect. This happens when full factorial designs are run using incomplete blocks. In such designs the same linear combination of observations estimates the block effect and the confounded effects. ''See also'' Incomplete Blocks.



	'''Contrast'''		'''Contrast'''
Line 59:		Line 59:
	Any linear combination of two or more factor level means such that the coefficients in the combination add up to zero. The difference between the means at any two levels of a factor is an example of a contrast.		Any linear combination of two or more factor level means such that the coefficients in the combination add up to zero. The difference between the means at any two levels of a factor is an example of a contrast.



	'''Control Factors'''		'''Control Factors'''
Line 81:		Line 81:
	'''Defining Relation'''		'''Defining Relation'''

	For two level fractional factorial experiments, the equation that is used to obtain the fraction from the full factorial experiment. The equation shows which of the columns of the design matrix in the fraction are identical to the first column. For example, the defining relation ''I''=''ABC'' can be used to obtain a half-fraction of the two level full factorial experiment with three factors ''A'', ''B'' and ''C''. The ~~effect(s)~~ used in the equation is called the ''~~generator~~'' or ''~~word~~''.		For two level fractional factorial experiments, the equation that is used to obtain the fraction from the full factorial experiment. The equation shows which of the columns of the design matrix in the fraction are identical to the first column. For example, the defining relation ''I''=''ABC'' can be used to obtain a half-fraction of the two level full factorial experiment with three factors ''A'', ''B'' and ''C''. The effects used in the equation are called the ''generators'' or ''words''.


Line 165:		Line 165:
	'''Hierarchical Model'''		'''Hierarchical Model'''

	In DOE++, a model is said to be hierarchical, if, corresponding to every interaction, the main effects of the related factors are included in the model.		In DOE++, a model is said to be hierarchical if, corresponding to every interaction, the main effects of the related factors are included in the model.


Line 191:		Line 191:
	The portion of the error sum of squares that represents variation in the data not captured because of using a reduced model. ''See also'' Reduced Model, Pure Error Sum of Squares.		The portion of the error sum of squares that represents variation in the data not captured because of using a reduced model. ''See also'' Reduced Model, Pure Error Sum of Squares.



	'''Least Squares Means'''		'''Least Squares Means'''
Line 197:		Line 197:
	The predicted mean response value for a given factor level while the remaining factors in the model are set to the coded value of zero.		The predicted mean response value for a given factor level while the remaining factors in the model are set to the coded value of zero.



	'''Level'''		'''Level'''
Line 233:		Line 233:
	Observations that are not part of the data set used to fit the model.		Observations that are not part of the data set used to fit the model.



	'''Noise Factors'''		'''Noise Factors'''

Chris Kahn at 15:51, 30 October 2012

2012-10-30T15:51:34Z

← Older revision		Revision as of 15:51, 30 October 2012
Line 81:		Line 81:
	'''Defining Relation'''		'''Defining Relation'''

	For two level fractional factorial experiments, the equation that is used to obtain the fraction from the full factorial experiment. The equation shows which of the columns of the design matrix in the fraction are identical to the first column. For example, the defining relation ''I''=''ABC'' can be used to obtain a half-fraction of the two level full factorial experiment with three factors ''A'', ''B'' and ''C''. The effect(s) used in the equation is called the generator or word.		For two level fractional factorial experiments, the equation that is used to obtain the fraction from the full factorial experiment. The equation shows which of the columns of the design matrix in the fraction are identical to the first column. For example, the defining relation ''I''=''ABC'' can be used to obtain a half-fraction of the two level full factorial experiment with three factors ''A'', ''B'' and ''C''. The effect(s) used in the equation is called the ''generator'' or ''word''.

Chris Kahn at 15:50, 30 October 2012

2012-10-30T15:50:08Z

Show changes

Chris Kahn at 15:45, 30 October 2012

2012-10-30T15:45:24Z

← Older revision		Revision as of 15:45, 30 October 2012
Line 39:		Line 39:
	'''Coded Values'''		'''Coded Values'''

	The factor values that are such that the upper limit of the investigated range of the factor becomes and the lower limit becomes . Using coded values makes the experiments with all factors at two levels orthogonal.		The factor values that are such that the upper limit of the investigated range of the factor becomes +1 and the lower limit becomes -1. Using coded values makes the experiments with all factors at two levels orthogonal.


Line 45:		Line 45:
	'''Confidence Interval'''		'''Confidence Interval'''

	A closed interval where a certain percentage of the population is likely to lie. For example, a 90% confidence interval with a lower limit of and an upper limit of implies that 90% of the population lies between the values of and .		A closed interval where a certain percentage of the population is likely to lie. For example, a 90% confidence interval with a lower limit of ''A'' and an upper limit of ''B'' implies that 90% of the population lies between the values of ''A'' and ''B''.


Line 81:		Line 81:
	'''Defining Relation'''		'''Defining Relation'''

	For two level fractional factorial experiments, the equation that is used to obtain the fraction from the full factorial experiment. The equation shows which of the columns of the design matrix in the fraction are identical to the first column. For example, the defining relation can be used to obtain a half-fraction of the two level full factorial experiment with three factors , and . The effect(s) used in the equation is called the generator or word.		For two level fractional factorial experiments, the equation that is used to obtain the fraction from the full factorial experiment. The equation shows which of the columns of the design matrix in the fraction are identical to the first column. For example, the defining relation ''I''=''ABC'' can be used to obtain a half-fraction of the two level full factorial experiment with three factors ''A'', ''B'' and ''C''. The effect(s) used in the equation is called the generator or word.


Line 411:		Line 411:
	'''Word'''		'''Word'''

	The effect used in the defining relation. For example, for the defining relation , the word is .		The effect used in the defining relation. For example, for the defining relation ''I''=''ABC'', the word is ''ABC''.

Lisa Hacker at 06:01, 29 August 2012

2012-08-29T06:01:59Z

← Older revision		Revision as of 06:01, 29 August 2012
Line 1:		Line 1:
	{{~~doebook~~}}		{{Doebook\|F}}

	~~=Glossary=~~

	'''Alias'''		'''Alias'''

Lisa Hacker: moved EDAR Appendix G to DOE Glossary

2012-08-29T06:01:41Z

moved EDAR Appendix G to DOE Glossary

← Older revision	Revision as of 06:01, 29 August 2012
(No difference)

Nicolette Young at 00:06, 6 August 2011

2011-08-06T00:06:23Z

← Older revision		Revision as of 00:06, 6 August 2011
Line 1:		Line 1:
			{{doebook}}

	=Glossary=		=Glossary=