ASTM E456-2006 Standard Terminology Relating to Quality and Statistics《与质量和统计学相关的标准术语》.pdf

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1、Designation: E 456 06An American National StandardStandard TerminologyRelating to Quality and Statistics1This standard is issued under the fixed designation E 456; the number immediately following the designation indicates the year oforiginal adoption or, in the case of revision, the year of last re

2、vision. A number in parentheses indicates the year of last reapproval. Asuperscript epsilon (e) indicates an editorial change since the last revision or reapproval.This standard has been approved for use by agencies of the Department of Defense.1. Scope1.1 This standard is the general terminology st

3、andard forterms defined in the standards of Committee E11 on Qualityand Statistics.1.2 A term in this standard which lists an attribution to anE11 technical standard indicates that the standard is normativefor that term. Any changes in the term definition in thenormative standard will be editorially

4、 changed in this standard.Any terms added to an E11 standard will be editorially addedto this standard with an attribution to that standard.1.3 Term definitions that are similar to ISO 3534 will benoted in this standard, but ISO 3534 will not be considerednormative for any E11 terms.2. Referenced Do

5、cuments2.1 ASTM E11 Standards with terms in this standard:2E29 Practice for Using Significant Digits in Test Data toDetermine Conformance with SpecificationsE 177 Practice for Use of the Terms Precision and Bias inASTM Test MethodsE 178 Practice for Dealing With Outlying ObservationsE 1169 Guide for

6、 Conducting Ruggedness TestsE 1325 Terminology Relating to Design of ExperimentsE 1488 Guide for Statistical Procedures to Use in Develop-ing and Applying Test MethodsE 1994 Practice for Use of Process Oriented AOQL andLTPD Sampling PlansE 2234 Practice for Sampling a Stream of Product byAttributes

7、Indexed by AQLE 2281 Practice for Process and Measurement CapabilityIndicesE 2282 Guide for Defining the Test Result of a Test MethodE 2334 Practice for Setting an Upper Confidence Bound Fora Fraction or Number of Non-Conforming items, or a Rateof Occurrence for Non-conformities, Using Attribute Dat

8、a,When There is a Zero Response in the SampleE 2489 Practice for Statistical Analysis of One-Sample andTwo-Sample Interlaboratory Proficiency Testing Programs2.2 ISO Standards:ISO 3534 Statistics-Vocabulary and SymbolsPart 1: Probability and General Statistical TermsPart 2: Applied Statistics3. Term

9、inologyacceptance (control chart or acceptance control chartusage, ), na decision that the process is operating in asatisfactory manner with respect to the statistical measuresbeing plotted: action limits: control limits.acceptance quality limit AQL , nquality limit that is theworst tolerable proces

10、s average when a continuing series oflots is submitted for acceptance sampling. E 2234accepted reference value, na value that serves as anagreed-upon reference for comparison, and which is derivedas: (1) a theoretical or established value, based on scientificprinciples, (2) an assigned or certified

11、value, based onexperimental work of some national or international organi-zation, or (3) a consensus or certified value, based oncollaborative experimental work under the auspices of ascientific or engineering group. E 177accuracy, nthe closeness of agreement between a test resultand an accepted ref

12、erence value. E 177aliases, nin a fractional factorial design, two or more effectswhich are estimated by the same contrast and which,therefore, cannot be estimated separately. E 1325assignable cause, na factor that contributes to variation, andwhich is feasible to detect and identify.NOTE 1Many fact

13、ors will contribute to variation but it may not befeasible (economically or otherwise) to identify some of them.attribute data, nobserved values or determinations whichindicate the presence or absence of specific characteristics.DISCUSSIONItems or units of material may be evaluated by countingor mea

14、surement. Attributes are counted whereas variables are mea-sured. Attribute distributions are discrete. See variables data.1This terminology is under the jurisdiction ofASTM Committee E11 on Qualityand Statistics and is the direct responsibility of Subcommittee E11.70 on Editorial/Terminology.Curren

15、t edition approved Sept. 1, 2006. Published November 2006. Originallyapproved in 1972. Last previous edition approved in 2004 as E 456 04e1.2For referenced ASTM standards, visit the ASTM website, www.astm.org, orcontact ASTM Customer Service at serviceastm.org. For Annual Book of ASTMStandardsvolume

16、 information, refer to the standards Document Summary page onthe ASTM website.1Copyright ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959, United States.attributes, method of, nmeasurement of quality by themethod of attributes consists of noting the presence (

17、orabsence) of some characteristic or attribute in each of theunits in the group under consideration, and counting howmany units do (or do not) possess the quality attribute, orhow many such events occur in the unit, group, or area.E 2334average outgoing quality (AOQ)the average percent defec-tive of

18、 outgoing product including all accepted lots orbatches, after any defective units found in them are replacedby acceptable units, plus all lots or batches which are notaccepted after such lots or batches have been effectively100 % inspected and all defective units replaced by accept-able units. E 19

19、94average outgoing quality limit (AOQL)the maximum oftheAOQs for all possible incoming percentages defective forthe process, for a given acceptance sampling plan. E 1994average quality protectiona type of protection in whichthere is prescribed some chosen value of average percentdefective in the pro

20、duct after inspection (average outgoingquality limit (AOQL), that shall not be exceeded in the longrun no matter what may be the level of percent defective inthe product submitted to the inspector. E 1994average run length (ARL)(1) sample sense, nthe aver-age number of times that a process will have

21、 been sampledand evaluated before a shift in process level is signaled, and(2) unit sense, nthe average number of units that will havebeen produced before a shift in level is signaled.DISCUSSIONA long ARL is desirable for a process located at itsspecified level (so as to minimize calling for unneede

22、d investigation orcorrective action) and a short ARL is desirable for a process shifted tosome undesirable level (so that corrective action will be called forpromptly). ARL curves are used to describe the relative quickness indetecting level shifts of various control chart systems.average standard d

23、eviation, s, narithmetic average ofsample standard deviations. E 2281balanced incomplete block design (BIB), nan incompleteblock design in which each block contains the same numberk of different versions from the t versions of a singleprincipal factor arranged so that every pair of versionsoccurs to

24、gether in the same number, l, of blocks from the bblocks. E 1325batch, na definite quantity of some product or materialproduced under conditions that are considered uniform.NOTE 2A batch is usually smaller than a lot.batch, in inspection, na collection of units of productproduced under conditions th

25、at are considered uniform andfrom which a sample is drawn and inspected, and may differfrom a collection of units designated as a batch for otherpurposes, for example, production, shipment, etc. E 2234batch size, nthe number of units of product in a batch.E 2234bias, nthe difference between the expe

26、ctation of the testresults and an accepted reference value. E 177characteristic, na property of items in a sample or popula-tion which, when measured, counted or otherwise observed,helps to distinguish between the items. E 2282classification of defects, nthe enumeration of possibledefects of the uni

27、t of product arranged according to theirseriousness, that is, critical, major, or minor defect.E 2234cluster sampling, nwhen the primary sampling unit com-prises a bundle of elementary units or a group of subunits,the term cluster sampling may be applied.DISCUSSIONExamples of cluster sampling are: s

28、election of cityblocks as primary sampling units; selection of a household as a clusterof people (of which only one may be interviewed); selection of bundlesof rods or pipe from a shipment; and selection, from a shipment, ofcartons that contain boxes or packages within them.collaborative study, nint

29、erlaboratory study in which eachlaboratory uses the defined method of analysis to analyzeidentical portions of homogeneous materials to assess theperformance characteristics obtained for that method ofanalysis. E 2489collaborative trial, nsee collaborative study. E 2489completely randomized design,

30、na design in which thetreatments are assigned at random to the full set of experi-mental units. E 1325completely randomized factorial design, na factorial ex-periment (including all replications) run in a completelyrandomized design. E 1325component of variance, na part of a total variance identifie

31、dwith a specified source of posite design, na design developed specifically forfitting second order response surfaces to study curvature,constructed by adding further selected treatments to thoseobtained from a 2nfactorial (or its fraction). E 1325confidence bound, nsee confidence limit. E 2334confi

32、dence coefficient, nthe value, C, of the probabilityassociated with a confidence interval or statistical coverageinterval. It is often expressed as a percentage. ISO 3534-1E 2334confidence level, nsee confidence coefficient. E 2334confidence limit, neach of the limits, T1and T2, of the twosided conf

33、idence interval, or the limit T of the one sidedconfidence interval. E 2334confounded factorial design, na factorial experiment inwhich only a fraction of the treatment combinations are runin each block and where the selection of the treatmentcombinations assigned to each block is arranged so that o

34、neor more prescribed effects is(are) confounded with the blockeffect(s), while the other effects remain free from confound-ing. E 1325confounding, ncombining indistinguishably the main effectof a factor or a differential effect between factors (interac-tions) with the effect of other factor(s), bloc

35、k factor(s) orinteractions(s). E 1325consumers riskthe probability that a lot whose percentagedefective is equal to the LTPD will be accepted by the plan.E 1994contrast, na linear function of the observations for whichthe sum of the coefficients is zero. E 1325contrast analysis, na technique for est

36、imating the param-eters of a model and making hypothesis tests on preselectedlinear combinations of the treatments (contrasts). E 1325E456062control(evaluation), nan evaluation to check, test, orverify; (authority): the act of guiding, directing, or manag-ing; (stability): a state of process in whic

37、h the variability isattributable to a constant system of chance causes.control chart factor, na factor, usually varying with samplesize, to convert specified statistics or parameters into acentral line value or control limit appropriate to the controlchart.control chart method, nthe method of using

38、control chartsto determine whether or not processes are in a stable state.control limits, nlimits on a control chart which are used ascriteria for signaling the need for action, or for judgingwhether a set of data does or does not indicate a state ofstatistical control.critical defect, na defect tha

39、t judgment and experienceindicate would result in hazardous or unsafe conditions forindividuals using, maintaining, or depending upon the prod-uct, or a defect that judgment and experience indicate islikely to prevent performance of the function of a major enditem. E 2234critical defective, na unit

40、of product which contains one ormore critical defects and may also contain major and/orminor defects. E 2234defect, nany nonconformance of the unit of product withspecified requirements. E 2234defective, na unit of product which contains one or moredefects. E 2234defects per hundred units, nany give

41、n quantity of units ofproduct is one hundred times the number of defects con-tained therein (one or more defects being possible in any unitof product) divided by the total number of units of product,that is:Defects per hundred units 5Number of defects 3100Number of units inspectedE 2234dependent var

42、iable, nSee response variable. E 1325design of experiments, nthe arrangement in which anexperimental program is to be conducted, and the selectionof the levels (versions) of one or more factors or factorcombinations to be included in the experiment. Synonymsinclude experiment design and experimental

43、 design.E 1325deviation, nthe difference between a measurement or quasi-measurement and its stated value or intended level.DISCUSSIONDeviation should be stated as a difference in terms ofthe appropriate data units. Sometimes these units will be originalmeasurement units; sometimes they will be quasi

44、-measurements; thatis, a scaled rating of subjective judgments; sometimes they will bedesignated values representing all continuous or discrete measurementsfalling in defined cells or classes.error of result, nthe test result minus the accepted referencevalue (of the characteristic).NOTE 3It is not

45、possible to correct for random error.evolutionary operation (EVOP), na sequential form ofexperimentation conducted in production facilities duringregular production. E 1325experimental design, nsee design of experiments. E 1325experiment space, nthe materials, equipment, environmen-tal conditions an

46、d so forth that are available for conductingan experiment. E 1325experimental unit, na portion of the experiment space towhich a treatment is applied or assigned in the experiment.E 1325factorial experiment (general), nin general, an experimentin which all possible treatments formed from two or more

47、factors, each being studied at two or more levels (versions)are examined so that interactions (differential effects) as wellas main effects can be estimated. E 13252nfactorial experiment, na factorial experiment in which nfactors are studied, each of them in two levels (versions).E 1325fractional fa

48、ctorial design, na factorial experiment inwhich only an adequately chosen fraction of the treatmentsrequired for the complete factorial experiment is selected tobe run. E 1325frame, na list, compiled for sampling purposes, whichdesignates the items (units) of a population or universe to beconsidered

49、 in a study.DISCUSSIONWhen a frame is available, sampling schemes can bedevised for selection of the units directly (one-stage), or in two or morestages. In multi-stage sampling, a frame is needed for each stage.As anexample, the cartons of a lot could be the first-stage units, packageswithin the carton could be second-stage units, and items within thepackages could be the third-stage units.fully nested experiment, na nested experiment in which thesecond factor is nested within levels (versions) of t