ASTM E2334-2009(2013) Standard Practice for Setting an Upper Confidence Bound For a Fraction or Number of Non-Conforming items or a Rate of Occurrence for Non-conformities Using Ate.pdf

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1、Designation: E2334 09 (Reapproved 2013) An American National StandardStandard Practice forSetting an Upper Confidence Bound For a Fraction orNumber of Non-Conforming items, or a Rate of Occurrencefor Non-conformities, Using Attribute Data, When There is aZero Response in the Sample1This standard is

2、issued under the fixed designation E2334; the number immediately following the designation indicates the year oforiginal adoption or, in the case of revision, the year of last revision. A number in parentheses indicates the year of last reapproval. Asuperscript epsilon () indicates an editorial chan

3、ge since the last revision or reapproval.1. Scope1.1 This practice presents methodology for the setting of anupper confidence bound regarding a unknown fraction orquantity non-conforming, or a rate of occurrence fornonconformities, in cases where the method of attributes isused and there is a zero r

4、esponse in a sample. Three cases areconsidered.1.1.1 The sample is selected from a process or a very largepopulation of discrete items, and the number of non-conforming items in the sample is zero.1.1.2 A sample of items is selected at random from a finitelot of discrete items, and the number of non

5、-conforming itemsin the sample is zero.1.1.3 The sample is a portion of a continuum (time, space,volume, area etc.) and the number of non-conformities in thesample is zero.1.2 Allowance is made for misclassification error in thisstandard, but only when misclassification rates are well under-stood or

6、 known and can be approximated numerically.2. Referenced Documents2.1 ASTM Standards:2E141 Practice for Acceptance of Evidence Based on theResults of Probability SamplingE456 Terminology Relating to Quality and StatisticsE1402 Guide for Sampling DesignE1994 Practice for Use of Process Oriented AOQL

7、andLTPD Sampling PlansE2586 Practice for Calculating and Using Basic Statistics2.2 ISO Standards:ISO 3534-1 StatisticsVocabulary and Symbols, Part 1:Probability and General Statistical Terms3ISO 3534-2 StatisticsVocabulary and Symbols, Part 2:Statistical Quality Control3NOTE 1Samples discussed in th

8、is standard should meet the require-ments (or approximately so) of a probability sample as defined inTerminologies E1402 or E456.3. Terminology3.1 Definitions:3.1.1 Terminology E456 provides a more extensive list ofterms in E11 standards.3.1.2 attributes, method of, nmeasurement of quality bythe met

9、hod of attributes consists of noting the presence (orabsence) of some characteristic or attribute in each of the unitsin the group under consideration, and counting how many ofthe units do (or do not) possess the quality attribute, or howmany such events occur in the unit, group or area.3.1.3 confid

10、ence bound, nsee confidence limit.3.1.4 confidence coeffcient, nthe value, C, of the prob-ability associated with a confidence interval or statisticalcoverage interval. It is often expressed as a percentage. ISO3534-13.1.5 confidence interval, nan interval estimate of apopulation parameter, calculat

11、ed such that there is a givenlong-run probability that the parameter is included in theinterval.3.1.5.1 DiscussionA one-sided confidence interval is onefor which one of the limits is plus infinity, minus infinity, or anatural fixed limit (such as zero).3.1.6 confidence level, nsee confidence coeffci

12、ent.3.1.7 confidence limit, nthe upper or lower limit of aconfidence interval.3.1.8 item, nan object or quantity of material on which aset of observations can be made.1This practice is under the jurisdiction ofASTM Committee E11 on Quality andStatistics and is the direct responsibility of Subcommitt

13、ee E11.30 on StatisticalQuality Control.Current edition approved April 1, 2013. Published April 2013. Originallyapproved in 2003. Last previous edition approved in 2009 as E2334 09. DOI:10.1520/E2334-09R13.2For referenced ASTM Standards, visit the ASTM website, www.astm.org, orcontact ASTM Customer

14、Service at serviceastm.org. For Annual Book of ASTMStandardsvolume information, refer to thestandards Document Summary page onthe ASTM website.3Available from American National Standards Institute (ANSI), 25 W. 43rd St.,4th Floor, New York, NY 10036, http:/www.ansi.org.Copyright ASTM International,

15、100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959. United States13.1.8.1 DiscussionAs used in this standard, “set” denotesa single variable (the defined attribute). The term “samplingunit” is also used to denote an “item” (see Practice E141).3.1.9 non-conforming item, nan item con

16、taining at leastone non-conformity. ISO 3534-23.1.9.1 DiscussionThe term “defective item” is also usedin this context.3.1.10 non-conformity, nthe non-fulfillment of a specifiedrequirement. ISO 3534-23.1.10.1 DiscussionThe term “defect” is also used in thiscontext.3.1.11 population, nthe totality of

17、items or units ofmaterial under consideration. E25863.1.12 sample, na group of observations or test resultstaken from a larger collection of observations or test results ,which serves to provide information that may be used as a basisfor making a decision concerning the larger collection. E25863.2 D

18、efinitions of Terms Specific to This Standard:3.2.1 probability sample, na sample of which the sam-pling units have been selected by a chance process. At eachstep of selection, a specified probability of selection can beattached to each sampling unit available for selection. E14023.2.2 zero response

19、, nin the method of attributes, thephrase used to denote that zero non-conforming items or zeronon-conformities were found (observed) in the item(s), unit,group or area sampled.3.3 Symbols:3.3.1 Athe assurance index, as a percent or a probabilityvalue.3.3.2 Cconfidence coefficient as a percent or as

20、 a prob-ability value.3.3.3 Cdthe confidence coefficient calculated that a pa-rameter meets a certain requirement, that is, that p p0, that D D0or that 0, when there is a zero response in the sample.3.3.4 Dthe number of non-conforming items in a finitepopulation containing N items.3.3.5 D0a specifie

21、d value of D for which a researcher willcalculate a confidence coefficient for the statement, D D0,when there is a zero response in the sample.3.3.6 Duthe upper confidence bound for the parameter D.3.3.7 Nthe number of items in a finite population.3.3.8 nthe sample size, that is, the number of items

22、 in asample.3.3.9 nRthe sample size required.3.3.10 pa process fraction non-conforming.3.3.11 p0a specified value of p for which a researcher willcalculate a confidence coefficient, for the statement p p0,when there is a zero response in the sample.3.3.12 puthe upper confidence bound for the paramet

23、er p.3.3.13 the mean number of non-conformities (or events)over some area of interest for a Poisson process.3.3.14 0a specific value of for which a researcher willcalculate a confidence coefficient for the statement, 0,when there is a zero response in the sample.3.3.15 uthe upper confidence bound fo

24、r the parameter .3.3.16 1the probability of classifying a conforming itemas non-conforming; or of finding a nonconformity where noneexists.3.3.17 2the probability of classifying a non-conformingitem as conforming; or of failing to find a non-conformitywhere one should have been found.4. Significance

25、 and Use4.1 In Case 1, the sample is selected from a process or avery large population of interest. The population is essentiallyunlimited, and each item either has or has not the definedattribute. The population (process) has an unknown fraction ofitems p (long run average process non-conforming) h

26、aving theattribute. The sample is a group of n discrete items selected atrandom from the process or population under consideration,and the attribute is not exhibited in the sample. The objectiveis to determine an upper confidence bound, pu, for the unknownfraction p whereby one can claim that p puwi

27、th someconfidence coefficient (probability) C. The binomial distribu-tion is the sampling distribution in this case.4.2 In Case 2, a sample of n items is selected at randomfrom a finite lot of N items. Like Case 1, each item either hasor has not the defined attribute, and the population has anunknow

28、n number, D, of items having the attribute. The sampledoes not exhibit the attribute. The objective is to determine anupper confidence bound, Du, for the unknown number D,whereby one can claim that D Duwith some confidencecoefficient (probability) C. The hypergeometric distribution isthe sampling di

29、stribution in this case.4.3 In Case 3, there is a process, but the output is acontinuum, such as area (for example, a roll of paper or othermaterial, a field of crop), volume (for example, a volume ofliquid or gas), or time (for example, hours, days, quarterly, etc.)The sample size is defined as tha

30、t portion of the “continuum”sampled, and the defined attribute may occur any number oftimes over the sampled portion. There is an unknown averagerate of occurrence, , for the defined attribute over the sampledinterval of the continuum that is of interest. The sample doesnot exhibit the attribute. Fo

31、r a roll of paper this might beblemishes per 100 ft2; for a volume of liquid, microbes percubic litre; for a field of crop, spores per acre; for a timeinterval, calls per hour, customers per day or accidents perquarter. The rate, , is proportional to the size of the interval ofinterest. Thus, if = 1

32、2 blemishes per 100 ft2of paper, this isequivalent to 1.2 blemishes per 10 ft2or 30 blemishes per 250ft2. It is important to keep in mind the size of the interval in theanalysis and interpretation. The objective is to determine anupper confidence bound, u, for the unknown occurrence rate ,whereby on

33、e can claim that uwith some confidencecoefficient (probability) C. The Poisson distribution is thesampling distribution in this case.4.4 Avariation on Case 3 is the situation where the sampled“interval” is really a group of discrete items, and the definedE2334 09 (2013)2attribute may occur any numbe

34、r of times within an item. Thismight be the case where the continuum is a process producingdiscrete items such as metal parts, and the attribute is definedas a scratch. Any number of scratches could occur on anysingle item. In such a case the occurrence rate, , might bedefined as scratches per 1000

35、parts or some similar metric.4.5 In each case a sample of items or a portion of acontinuum is examined for the presence of a defined attribute,and the attribute is not observed (that is, a zero response). Theobjective is to determine an upper confidence bound for eitheran unknown proportion, p (Case

36、 1), an unknown quantity, D(Case 2), or an unknown rate of occurrence, (Case 3). In thisstandard, confidence means the probability that the unknownparameter is not more than the upper bound. More generally,these methods determine a relationship among sample size,confidence and the upper confidence b

37、ound. They can be usedto determine the sample size required to demonstrate a specificp, D or with some degree of confidence. They can also beused to determine the degree of confidence achieved indemonstrating a specified p, D or .4.6 In this standard allowance is made for misclassificationerror but

38、only when misclassification rates are well understoodor known, and can be approximated numerically.4.7 It is possible to impose the language of classicalacceptance sampling theory on this method. Terms such as LotTolerance Percent Defective, Acceptable Quality Level, Con-sumer Quality Level are not

39、used in this standard. For moreinformation on these terms, see Practice E1994.5. Procedure5.1 When a sample is inspected and a zero response isexhibited with respect to a defined attribute, we refer to thisevent as “all_zeros.” Formulas for calculating the probabilityof “all_zeros” in a sample are b

40、ased on the binomial, thehypergeometric and the Poisson probability distributions.When there is the possibility of misclassification error, adjust-ments to these distributions are used. This practice will clarifywhen each distribution is appropriate and how misclassificationerror is incorporated. Th

41、ree basic cases are considered asdescribed in Section 4. Formulas and examples for each caseare given below. Mathematical notes are given in AppendixX1.5.2 In some applications, the measurement method isknown to be fallible to some extent resulting in a significantmisclassification error. If experim

42、ents with repeated measure-ments have established the rates of misclassification, and theyare known to be constant, they should be included in thecalculating formulas. Two misclassification error probabilitiesare defined for this practice:5.2.1 Let 1be the probability of reporting a non-conforming i

43、tem when the item is really conforming.5.2.2 Let 2be the probability of reporting a conformingitem when the item is really non-conforming.5.2.3 Almost all applications of this standard require that 1be known to be 0 (see 6.1.2).5.3 Formulas for upper confidence bounds in three cases:5.3.1 Case 1The

44、item is a completely discrete object andthe attribute is either present or not within the item. Only oneresponse is recorded per item (either go or no-go). The sampleitems originate from a process and hence the future populationof interest is potentially unlimited in extent so long as theprocess rem

45、ains in statistical control. The item having theattribute is often referred to as a defective item or a non-conforming item or unit. The sample consists of n randomlyselected items from the population of interest. The n items areinspected for the defined attribute. The sampling distribution isthe bi

46、nomial with parameters p equal to the process (popula-tion) fraction non-conforming and n the sample size. Whenzero non-conforming items are observed in the sample (theevent “all_zeros”), and there are no misclassification errors, theupper confidence bound, pu, at confidence level C (0 C 1),for the

47、population proportion non-conforming is:pu5 1 2 =n1 2 C (1)5.3.1.1 Table 1contains the calculated upper confidencebound for the process fraction non-conforming when x=0non-conforming items appear in a sample of size n. Confidenceis 100C%. For example, if n=250 objects are sampled and thereare x=0 no

48、n-conforming objects in the sample, then the upper95% confidence bound for the process fraction non-conformingis approximately 0.01191 or 1.191% non-conforming. Eq 1was applied.5.3.1.2 For the case with misclassification errors, when zeronon-conforming items are observed in the sample (all_zeros),th

49、e upper confidence bound, pu, at confidence level C is:TABLE 1 Upper 100C% Confidence Bound, pu, for the ProcessFraction Non-Conforming, p, When Zero non-conforming Unitsappear in a sample of Size, nn C=0.90 C=0.95 C=0.995 0.369043 0.450720 0.60189310 0.205672 0.258866 0.36904315 0.142304 0.181036 0.26435820 0.108749 0.139108 0.20567230 0.073881 0.095034 0.14230440 0.055939 0.072158 0.10874950 0.045007 0.058155 0.08798960 0.037649 0.048703 0.07388170 0.032359 0.041893 0.06367180 0.028372 0.036754 0.0