03.120.30 - Application of statistical methods
ICS 03.120.30 Details
Application of statistical methods
Anwendung statistischer Methoden
Application des méthodes statistiques
Uporaba statističnih metod
General Information
Frequently Asked Questions
ICS 03.120.30 is a classification code in the International Classification for Standards (ICS) system. It covers "Application of statistical methods". The ICS is a hierarchical classification system used to organize international, regional, and national standards, facilitating the search and identification of standards across different fields.
There are 506 standards classified under ICS 03.120.30 (Application of statistical methods). These standards are published by international and regional standardization bodies including ISO, IEC, CEN, CENELEC, and ETSI.
The International Classification for Standards (ICS) is a hierarchical classification system maintained by ISO to organize standards and related documents. It uses a three-level structure with field (2 digits), group (3 digits), and sub-group (2 digits) codes. The ICS helps users find standards by subject area and enables statistical analysis of standards development activities.
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1.1 This document - amplifies the general principles for designing experiments for the numerical estimation of the precision of measurement methods by means of a collaborative interlaboratory experiment, - provides a detailed practical description of the basic method for routine use in estimating the precision of measurement methods, and - provides guidance to all personnel concerned with designing, performing or analysing the results of the tests for estimating precision. NOTE Modifications to this basic method for particular purposes are given in other parts of ISO 5725. 1.2 It is concerned exclusively with measurement methods which yield measurements on a continuous scale and give a single value as the test result, although this single value can be the outcome of a calculation from a set of observations. 1.3 It assumes that in the design and performance of the precision experiment, all the principles as laid down in ISO 5725-1 are observed. The basic method uses the same number of test results in each laboratory, with each laboratory analysing the same levels of test sample; i.e. a balanced uniform-level experiment. The basic method applies to procedures that have been standardized and are in regular use in a number of laboratories. 1.4 The statistical model of ISO 5725-1:2023, Clause 5, is accepted as a suitable basis for the interpretation and analysis of the test results, the distribution of which is approximately normal. 1.5 The basic method, as described in this document, (usually) estimates the precision of a measurement method: a) when it is required to determine the repeatability and reproducibility standard deviations as defined in ISO 5725-1; b) when the materials to be used are homogeneous, or when the effects of heterogeneity can be included in the precision values; c) when the use of a balanced uniform-level layout is acceptable. 1.6 The same approach can be used to make a preliminary estimate of precision for measurement methods which have not reached standardization or are not in routine use.
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This document presents methods for determining the critical value of the response variable and the minimum detectable value in Poisson distribution measurements. It is applicable when variations in both the background noise and the signal are describable by the Poisson distribution. The conventional approximation is used to approximate the Poisson distribution by the normal distribution consistent with ISO 11843-3 and ISO 11843-4. The accuracy of the normal approximation as compared to the exact Poisson distribution is discussed in Annex B.
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This document describes the use of robust methods for analysing the results of precision experiments without using outlier tests to exclude data from the calculations, and in particular, the detailed use of several such methods. The robust methods described in this document allow the data to be analysed in such a way that it is not required to make decisions about outliers that affect the results of the calculations.
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This document specifies a fundamental principle on grid square statistics and includes the following: - To define or specify a set of methods that can be recommended as the standard method to enable policy decisions based on common recognition from grid statistics produced with different grid square reference system standards in different countries and areas that can have different geographic coding systems with different geometric shapes. - To standardize methods to accommodate the conversion calculation among grid statistics with different grid square reference systems having different geometric shapes by employing prorating method based on the grid areas as well as the method to calculate the approximation errors, for exchanging the converted grid statistics. Spatial expressions other than grid square also exist, but this document does not apply to the spatial expressions other than grid square. NOTE Clause 4 and Clause 5 define grid square statistics and specify some methods to generate grid square statistics. REF Section_sec_6 \r \h Clause 6 recommends conversion methods between grid square statistics generated based on different grid square reference systems.
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This document describes quantitative approaches for acquisition of the voice of customer (VOC) and voice of stakeholder (VOS) and its purpose, and provides recommendations on the use of the applicable tools and methods. It is not a management system standard. NOTE It does not provide requirements or guidelines for organizations to develop and systematically manage their policies, processes, and procedures in order to achieve specific objectives. Users of this document include all organization functions necessary to assure customer satisfaction, including business planning, marketing, sales, research and development (R&D), engineering, information technology (IT), manufacturing, procurement, quality, production, service, packaging and logistics, support, testing, regulatory, and other phases in hardware, software, service, and system organizations.
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This document specifies the practical use of the fundamental concepts in ISO 11843 in case of the background noise predominance in instrumental analysis. This document specifies basic methods to - extract the stochastic properties of the background noise, - use the stochastic properties to estimate the SD or CV of the response variable, and - calculate the minimum detectable value based on the SD or CV obtained above. The methods described in this document are useful for checking the detection of a certain substance by various types of measurement equipment in which the background noise of the instrumental output predominates over the other sources of measurement uncertainty. Feasible choices are visible and ultraviolet absorption spectrometry, atomic absorption spectrometry, atomic fluorescence spectrometry, luminescence spectrometry, liquid chromatography and gas chromatography.
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IEC 62309:2024 introduces the concept to check the reliability and functionality of reused parts and their usage within new products. It also provides information and criteria about the assurance [for example, testing and analysis, required for products containing reused parts, which are declared "qualified-as-good-as-new" (QAGAN)] relative to the designed life of the product.
This document specifies requirements to be satisfied before making a declaration or applying a designation of QAGAN. This document also gives guidance to support any organisation that makes declarations about dependability of products containing reused parts.
In this document, the term "product" covers electrical, electro-mechanical, mechanical parts or hardware that can contain software.
"Qualified-as-good-as-new" (QAGAN) does not apply to reused materials or large structures and large systems, nor does it cover software products, concepts, and ideas.
The purpose of this document is to ensure by tests and analysis that the reliability and functionality of a new product containing reused parts is comparable to a product that contains only new parts. This would justify the manufacturer granting the next customer the full warranty of the product with "qualified-as-good-as-new" (QAGAN) parts.
Annex A describes extending useful life by refurbishment, updating, upgrading, maintenance and used as second-hand. These concepts are defined and the requirements for using the term with reference to this document are stated.
This second edition cancels and replaces the first edition published in 2004. This edition constitutes a technical revision.
This edition includes the following significant technical changes with respect to the previous edition:
a) the previous Annex A has been separated into Annex B (Dependability aspects) and Annex C (Example with QAGAN parts);
b) a new normative Annex A has been written with expansion of lifecycle activities, to describe extending the useful life by refurbishment, life extension, updating, upgrading and second-hand use;
c) revision of Figure 1 accordingly;
d) minor editorial alignments throughout the document;
e) the abbreviation "quagan" has been changed "QAGAN" to reflect more contemporary use.
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IEC 62309:2024 introduces the concept to check the reliability and functionality of reused parts and their usage within new products. It also provides information and criteria about the assurance [for example, testing and analysis, required for products containing reused parts, which are declared "qualified-as-good-as-new" (QAGAN)] relative to the designed life of the product. This document specifies requirements to be satisfied before making a declaration or applying a designation of QAGAN. This document also gives guidance to support any organisation that makes declarations about dependability of products containing reused parts. In this document, the term "product" covers electrical, electro-mechanical, mechanical parts or hardware that can contain software. "Qualified-as-good-as-new" (QAGAN) does not apply to reused materials or large structures and large systems, nor does it cover software products, concepts, and ideas. The purpose of this document is to ensure by tests and analysis that the reliability and functionality of a new product containing reused parts is comparable to a product that contains only new parts. This would justify the manufacturer granting the next customer the full warranty of the product with "qualified-as-good-as-new" (QAGAN) parts. Annex A describes extending useful life by refurbishment, updating, upgrading, maintenance and used as second-hand. These concepts are defined and the requirements for using the term with reference to this document are stated. This second edition cancels and replaces the first edition published in 2004. This edition constitutes a technical revision. This edition includes the following significant technical changes with respect to the previous edition: a) the previous Annex A has been separated into Annex B (Dependability aspects) and Annex C (Example with QAGAN parts); b) a new normative Annex A has been written with expansion of lifecycle activities, to describe extending the useful life by refurbishment, life extension, updating, upgrading and second-hand use; c) revision of Figure 1 accordingly; d) minor editorial alignments throughout the document; e) the abbreviation "quagan" has been changed "QAGAN" to reflect more contemporary use.
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IEC 62309:2024 introduces the concept to check the reliability and functionality of reused parts and their usage within new products. It also provides information and criteria about the assurance [for example, testing and analysis, required for products containing reused parts, which are declared "qualified-as-good-as-new" (QAGAN)] relative to the designed life of the product.
This document specifies requirements to be satisfied before making a declaration or applying a designation of QAGAN. This document also gives guidance to support any organisation that makes declarations about dependability of products containing reused parts.
In this document, the term "product" covers electrical, electro-mechanical, mechanical parts or hardware that can contain software.
"Qualified-as-good-as-new" (QAGAN) does not apply to reused materials or large structures and large systems, nor does it cover software products, concepts, and ideas.
The purpose of this document is to ensure by tests and analysis that the reliability and functionality of a new product containing reused parts is comparable to a product that contains only new parts. This would justify the manufacturer granting the next customer the full warranty of the product with "qualified-as-good-as-new" (QAGAN) parts.
Annex A describes extending useful life by refurbishment, updating, upgrading, maintenance and used as second-hand. These concepts are defined and the requirements for using the term with reference to this document are stated.
This second edition cancels and replaces the first edition published in 2004. This edition constitutes a technical revision.
This edition includes the following significant technical changes with respect to the previous edition:
a) the previous Annex A has been separated into Annex B (Dependability aspects) and Annex C (Example with QAGAN parts);
b) a new normative Annex A has been written with expansion of lifecycle activities, to describe extending the useful life by refurbishment, life extension, updating, upgrading and second-hand use;
c) revision of Figure 1 accordingly;
d) minor editorial alignments throughout the document;
e) the abbreviation "quagan" has been changed "QAGAN" to reflect more contemporary use.
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This document covers EWMA control charts, originally proposed by Roberts (1959)[16], as a statistical process control technique to detect small shifts in the process mean. It makes possible the faster detection of small to moderate shifts in the process mean. In this chart, the process mean is evaluated in terms of exponentially weighted moving average of all previous observations or averages. The EWMA control chart’s application is worthwhile in particular when - production rate is slow, - a minor or moderate shift in the process mean is vital to be detected, - sampling and inspection procedure is complex and time consuming, - testing is expensive, and - it involves safety risks. NOTE EWMA control charts are applicable for both variables and attributes data. The given examples illustrate both types (see 5.5, Annex A, Annex B and Annex C).
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This document
- introduces conditions, constraints and resources necessary to evaluate a measurement method or a result;
- defines an organizational scheme for the acquisition of trueness and precision data by study;
- provides the necessary definitions, statistical model and principles for ISO 5725 (all parts).
- is not applicable to proficiency testing or production of the reference item that has their own standards (ISO 13528, respectively and ISO Guide 35).
This document is concerned exclusively with measurement methods which yield results on a continuous scale and give a single value as the test result, although this single value may be the outcome of a calculation from a set of observations.
It defines values which describe, in quantitative terms, the ability of a measurement method to give a true result (trueness) or to replicate a given result (precision). Thus, there is an implication that exactly the identical item is being measured, in exactly the same way, and that the measurement process is under control.
This document may be applied to a very wide range of test items, including gas, liquids, powders and solid objects, manufactured or naturally occurring, provided that due consideration is given to any heterogeneity of the test item.
This document does not include methods of calculation that are described in the other parts.
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This document provides
a) a discussion of alternative experimental designs for the determination of trueness and precision measures including reproducibility, repeatability and selected measures of intermediate precision of a standard measurement method, including a review of the circumstances in which their use is necessary or beneficial, and guidance as to the interpretation and application of the resulting estimates, and
b) worked examples including specific designs and computations.
Each of the alternative designs discussed in this document is intended to address one (or several) of the following issues:
a) a discussion of the implications of the definitions of intermediate precision measures;
b) a guidance on the interpretation and application of the estimates of intermediate precision measures in practical situations;
c) determining reproducibility, repeatability and selected measures of intermediate precision;
d) improved determination of reproducibility and other measures of precision;
e) improving the estimate of the sample mean;
f) determining the range of in-house repeatability standard deviations;
g) determining other precision components such as operator variability;
h) determining the level of reliability of precision estimates;
i) reducing the minimum number of participating laboratories by optimizing the reliability of precision estimates;
j) avoiding distorted estimations of repeatability (split-level designs);
k) avoiding distorted estimations of reproducibility (taking the heterogeneity of the material into consideration).
Often, the performance of the method whose precision is being evaluated in a collaborative study will have previously been assessed in a single-laboratory validation study conducted by the laboratory which developed it. Relevant factors for the determination of intermediary precision will have been identified in this prior single-laboratory study.
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SIGNIFICANCE AND USE
4.1 The methodology was originally developed (1-4)6 for use in drug content uniformity and dissolution but has general application to any multistage test with multiple acceptance criteria. Practice E2709 summarizes the statistical aspects of this methodology. This practice applies the general methodology of Practice E2709 specifically to the UDU test.
4.1.1 While other methods can be used to estimate the probability of passing the UDU test, they are outside the scope of this practice.
4.2 The UDU test procedure describes a two-stage sampling test, where at each stage one can pass or continue testing, and the decision to fail is deferred until the second stage. At each stage there are acceptance criteria on the test results as outlined in Table 1.
4.3 The UDU test is a market standard. The USP General Notices include the following statement about compendial standards. “The similarity to statistical procedures may seem to suggest an intent to make inference to some larger group of units, but in all cases, statements about whether the compendial standard is met apply only to the units tested.” Therefore, the UDU procedure is not intended for inspecting uniformity of finished product for lot/batch release or as a lot inspection procedure.
4.3.1 The UDU test defines a product requirement to be met at release and throughout the shelf-life of the product.
4.3.2 Passing the UDU test once does not provide statistical assurance that a batch of drug product meets specified statistical quality control criteria.
4.4 This practice provides a practical specification that may be applied when uniformity of dosage units is required. An acceptance region for the mean and standard deviation of a set of test results from the lot is defined such that, at a prescribed confidence level, the probability that a future sample from the lot will pass the UDU test is greater than or equal to a prespecified lower probability bound. Having test results fall in the acceptance r...
SCOPE
1.1 This practice provides a general procedure for evaluating the capability to comply with the Uniformity of Dosage Units (UDU) test. This test is given in General Chapter Uniformity of Dosage Units of the USP, in 2.9.40 Uniformity of Dosage Units of the Ph. Eur., and in 6.02 Uniformity of Dosage Units of the JP, and these versions are virtually interchangeable. For this multiple-stage test, the procedure computes a lower bound on the probability of passing the UDU test, based on statistical estimates made at a prescribed confidence level from a sample of dosage units.
1.2 This methodology can be used to generate an acceptance limit table, which defines a set of sample means and standard deviations that assures passing the UDU test for a prescribed lower probability bound, confidence level, and sample size.
1.3 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety, health, and environmental practices and determine the applicability of regulatory limitations prior to use.
1.4 This international standard was developed in accordance with internationally recognized principles on standardization established in the Decision on Principles for the Development of International Standards, Guides and Recommendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee.
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This document specifies an acceptance sampling system of single sampling plans for inspection by variables, primarily designed for use under the following conditions: a) where the inspection procedure is applied to an isolated lot of discrete products all supplied by one producer using one production process; b) where only a single quality characteristic, x, of this process is taken into consideration, which is measurable on a continuous scale; c) where the quality characteristic, x, is distributed according to a normal distribution or a close approximation to a normal distribution; d) where the quality characteristic can be measured without error or with moderate measurement error; e) where a contract or standard defines a lower specification limit, L, an upper specification limit, U, or both; an item is qualified as conforming if and only if its measured quality characteristic, x, satisfies the appropriate one of the following inequalities: 1) x ≥ L (i.e. the lower specification limit is not violated); 2) x ≤ U (i.e. the upper specification limit is not violated); 3) x ≥ L and x ≤ U (i.e. neither the lower nor the upper specification limit is violated). Inequalities 1) and 2) are cases with a single specification limit, whereas inequality 3) is a case with double specification limits. Where double specification limits apply, it is assumed in this document that conformance to both specification limits is equally important to the integrity of the product. In such cases, it is appropriate to apply a single LQ to the combined fraction of a product outside the two specification limits. This is referred to as combined control.
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SIGNIFICANCE AND USE
5.1 This practice may be used to continuously demonstrate the proficiency of analytical measurement systems that are used for establishing and ensuring the quality of petroleum and petroleum products.
5.2 Data accrued, using the techniques included in this practice, provide the ability to monitor analytical measurement system precision and bias.
5.3 These data are useful for updating test methods as well as for indicating areas of potential measurement system improvement.
5.4 Control chart statistics can be used to compute limits that the signed difference (Δ) between two single results for the same sample obtained under site precision conditions is expected to fall outside of about 5 % of the time, when each result is obtained using a different measurement system in the same laboratory executing the same test method, and both systems are in a state of statistical control.
SCOPE
1.1 This practice covers information for the design and operation of a program to monitor and control ongoing stability and precision and bias performance of selected analytical measurement systems using a collection of generally accepted statistical quality control (SQC) procedures and tools.
Note 1: A complete list of criteria for selecting measurement systems to which this practice should be applied and for determining the frequency at which it should be applied is beyond the scope of this practice. However, some factors to be considered include (1) frequency of use of the analytical measurement system, (2) criticality of the parameter being measured, (3) system stability and precision performance based on historical data, (4) business economics, and (5) regulatory, contractual, or test method requirements.
1.2 This practice is applicable to stable analytical measurement systems that produce results on a continuous numerical scale.
1.3 This practice is applicable to laboratory test methods.
1.4 This practice is applicable to validated process stream analyzers.
1.5 This practice is applicable to monitoring the differences between two analytical measurement systems that purport to measure the same property provided that both systems have been assessed in accordance with the statistical methodology in Practice D6708 and the appropriate bias applied.
Note 2: For validation of univariate process stream analyzers, see also Practice D3764.
Note 3: One or both of the analytical systems in 1.5 may be laboratory test methods or validated process stream analyzers.
1.6 This practice assumes that the normal (Gaussian) model is adequate for the description and prediction of measurement system behavior when it is in a state of statistical control.
Note 4: For non-Gaussian processes, transformations of test results may permit proper application of these tools. Consult a statistician for further guidance and information.
1.7 This international standard was developed in accordance with internationally recognized principles on standardization established in the Decision on Principles for the Development of International Standards, Guides and Recommendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee.
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This document describes process capability and performance measures when the specifications are given by geometrical product specifications e.g. maximum material requirements or linear size with a modifier. The purpose of this document of the international series of standards on capability calculation is to assist the organizations to calculate the PCIs (process capability index) when geometrical product specifications are used on drawings.
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ABSTRACT
This practice covers regression analysis of a set of data to define the statistical relationship between two numerical variables for use in predicting one variable from the other. This practice is restricted in scope to consider only a single numerical response variable and a single numerical predictor variable. The objective is to obtain a regression model for use in predicting the value of the response variable Y for given values of the predictor variable X.
SIGNIFICANCE AND USE
4.1 Regression analysis is a procedure that uses data to study the statistical relationships between two or more variables (1, 2).3 This practice is restricted in scope to consider only a single numerical response variable and a single numerical predictor variable. The objective is to obtain a regression model for use in predicting the value of the response variable Y for given values of the predictor variable X.
4.2 A regression model consists of: (1) a regression function that relates the mean values of the response variable distribution to fixed values of the predictor variable, and (2) a statistical distribution that describes the variability in the response variable values at a fixed value of the predictor variable.
4.2.1 The regression analysis utilizes either experimental or observational data to estimate the parameters defining a regression model and their precision. Diagnostic procedures are utilized to assess the resulting model fit and can suggest other models for improved prediction performance.
4.3 The information in this practice is arranged as follows.
4.3.1 Section 5 gives a general outline of the steps in the regression analysis procedure. The subsequent sections cover procedures for estimation of specific regression models.
4.3.2 Section 6 assumes a straight line relationship between the two variables. This is also known as the simple linear regression model or a first order model. This model should be used as a starting point for understanding the XY relationship and ultimately defining the best fitting model to the data.
4.3.3 Section 7 considers a proportional relationship between the variables, where the ratio of one variable to the other is constant. The intercept is constrained to be zero. This model is useful for single point calibration, where a reference material is run periodically as a standard during routine testing to correct for drift in instrument performance over a given range of test results.
4.3.4 Section 8 di...
SCOPE
1.1 This practice covers regression analysis of a set of data to define the statistical relationship between two numerical variables for use in predicting one variable from the other.
1.2 The regression analysis provides graphical and calculational procedures for selecting the best statistical model that describes the relationship and for evaluation of the fit of the data to the selected model.
1.3 The resulting regression model can be useful for developing process knowledge through description of the variable relationship, in making predictions of future values, in relating the precision of a test method to the value of the characteristic being measured, and in developing control methods for the process generating values of the variables.
1.4 The system of units for this practice is not specified. Dimensional quantities in the practice are presented only as illustrations of calculation methods. The examples are not binding on products or test methods treated.
1.5 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety, health, and environmental practices and determine the applicability of regulatory limitations prior to use.
1.6 This international standard was developed in accordance with internationally recognized principles on standardization established in the Decision on Principles for the Development ...
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ABSTRACT
This practice provides a general methodology for evaluating single-stage or multiple-stage acceptance procedures which involve a quality characteristic measured on a numerical scale. This methodology computes, at a prescribed confidence level, a lower bound on the probability of passing an acceptance procedure, using estimates of the parameters of the distribution of test results from a sampled population.
SIGNIFICANCE AND USE
4.1 This practice considers inspection procedures that may involve multiple-stage sampling, where at each stage one can decide to accept or to continue sampling, and the decision to reject is deferred until the last stage.
4.1.1 At each stage there are one or more acceptance criteria on the test results; for example, limits on each individual test result, or limits on statistics based on the sample of test results, such as the average, standard deviation, or coefficient of variation (relative standard deviation).
4.2 The methodology in this practice defines an acceptance region for a set of test results from the sampled population such that, at a prescribed confidence level, the probability that a sample from the population will pass the acceptance procedure is greater than or equal to a prespecified lower bound.
4.2.1 Having test results fall in the acceptance region is not equivalent to passing the acceptance procedure, but provides assurance that a sample would pass the acceptance procedure with a specified probability.
4.2.2 This information can be used for process demonstration, validation of test methods, and qualification of instruments, processes, and materials.
4.2.3 This information can be used for lot release (acceptance), but the lower bound may be conservative in some cases.
4.2.4 If the results are to be applied to future test results from the same process, then it is assumed that the process is stable and predictable. If this is not the case then there can be no guarantee that the probability estimates would be valid predictions of future process performance.
4.3 This methodology was originally developed (1-4)3 for use in two specific quality characteristics of drug products in the pharmaceutical industry but will be applicable for acceptance procedures in all industries.
4.4 Mathematical derivations would be required that are specific to the individual criteria of each test.
SCOPE
1.1 This practice provides a general methodology for evaluating single-stage or multiple-stage acceptance procedures which involve a quality characteristic measured on a numerical scale. This methodology computes, at a prescribed confidence level, a lower bound on the probability of passing an acceptance procedure, using estimates of the parameters of the distribution of test results from a sampled population.
1.2 For a prescribed lower probability bound, the methodology can also generate an acceptance limit table, which defines a set of test method outcomes (for example, sample averages and standard deviations) that would pass the acceptance procedure at a prescribed confidence level.
1.3 This approach may be used for demonstrating compliance with in-process, validation, or lot-release specifications.
1.4 The system of units for this practice is not specified.
1.5 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety, health, and environmental practices and determine the applicability of regulatory limitations prior to use.
1.6 This international standard was developed in accordance with internationally recognized principles on standardization established in the Decision on Principles for the Development of International Standards, Guides and Recommendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee.
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ABSTRACT
This practice presents rules for accepting or rejecting evidence based on a sample. Statistical evidence for this practice is in the form of an estimate of a proportion, an average, a total, or other numerical characteristic of a finite population or lot. This practice is an estimate of the result which would have been obtained by investigating the entire lot or population under the same rules and with the same care as was used for the sample. One purpose of this practice is to describe straightforward sample selection and data calculation procedures so that courts, commissions, etc. will be able to verify whether such procedures have been applied.
SIGNIFICANCE AND USE
4.1 This practice is designed to permit users of sample survey data to judge the trustworthiness of results from such surveys. Practice E105 provides a statement of principles for guidance of ASTM technical committees and others in the preparation of a sampling plan for a specific material. Guide E1402 describes the principal types of sampling designs. Practice E122 aids in deciding on the required sample size.
4.2 Section 5 gives extended definitions of the concepts basic to survey sampling and the user should verify that such concepts were indeed used and understood by those who conducted the survey. What was the frame? How large (exactly) was the quantity N? How was the parameter θ estimated and its standard error calculated? If replicate subsamples were not used, why not? Adequate answers should be given for all questions. There are many acceptable answers to the last question.
4.3 If the sample design was relatively simple, such as simple random or stratified, then fully valid estimates of sampling variance are easily available. If a more complex design was used then methods such as discussed in Ref (1)3 or in Guide E1402 may be acceptable. Use of replicate subsamples is the most straightforward way to estimate sampling variances when the survey design is complex.
4.4 Once the survey procedures that were used satisfy Section 5, see if any increase in sample size is needed. The calculations for making it objectively are described in Section 6.
4.5 Refer to Section 7 to guide in the interpretation of the uncertainty in the reported value of the parameter estimate, θ^, that is, the value of its standard error, se(θ^). The quantity se(θ^) should be reviewed to verify that the risks it entails are commensurate with the size of the sample.
4.6 When the audit subsample shows that there was reasonable conformity with prescribed procedures and when the known instances of departures from the survey plan can be shown to have no appreciable effect on the est...
SCOPE
1.1 This practice presents rules for accepting or rejecting evidence based on a sample. Statistical evidence for this practice is in the form of an estimate of a proportion, an average, a total, or other numerical characteristic of a finite population or lot. It is an estimate of the result which would have been obtained by investigating the entire lot or population under the same rules and with the same care as was used for the sample.
1.2 One purpose of this practice is to describe straightforward sample selection and data calculation procedures so that courts, commissions, etc. will be able to verify whether such procedures have been applied. The methods may not give least uncertainty at least cost, they should however furnish a reasonable estimate with calculable uncertainty.
1.3 This practice is primarily intended for one-of-a-kind studies. Repetitive surveys allow estimates of sampling uncertainties to be pooled; the emphasis of this practice is on estimation of sampling uncertainty from the sample itself. The parameter of interest for this practice is effectively a constant. Thus, the principal inference is a simple point estimate to be used as if it were the unknown constant, rather than, for example, a forecast or prediction interval or distribution...
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This document describes a data format for the exchange of quality information: - the data format is distinguished by a transparent structure that is easy to edit; - it is flexible, space saving and easily be copied and compacted; All files are language independent because of the allocation of an explicit key to a language independent field, the content of which can be translated into any language required.
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This document - introduces conditions, constraints and resources necessary to evaluate a measurement method or a result; - defines an organizational scheme for the acquisition of trueness and precision data by study; - provides the necessary definitions, statistical model and principles for ISO 5725 (all parts). - is not applicable to proficiency testing or production of the reference item that has their own standards (ISO 13528, respectively and ISO Guide 35). This document is concerned exclusively with measurement methods which yield results on a continuous scale and give a single value as the test result, although this single value may be the outcome of a calculation from a set of observations. It defines values which describe, in quantitative terms, the ability of a measurement method to give a true result (trueness) or to replicate a given result (precision). Thus, there is an implication that exactly the identical item is being measured, in exactly the same way, and that the measurement process is under control. This document may be applied to a very wide range of test items, including gas, liquids, powders and solid objects, manufactured or naturally occurring, provided that due consideration is given to any heterogeneity of the test item. This document does not include methods of calculation that are described in the other parts.
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SIGNIFICANCE AND USE
5.1 The POD analysis method described herein is based on a well-known and well established statistical regression method. It shall be used to quantify the demonstrated POD for a specific set of examination parameters and known range of discontinuity sizes under the following conditions.
5.1.1 The initial response from a nondestructive evaluation inspection system is ultimately binary in nature (that is, hit or miss).
5.1.2 Discontinuity size is the predictor variable and can be accurately quantified.
5.1.3 A relationship between discontinuity size and POD exists and is best described by a generalized linear model with the appropriate link function for binary outcomes.
5.2 This practice does not limit the use of a generalized linear model with more than one predictor variable or other types of statistical models if justified as more appropriate for the hit/miss data.
5.3 If the initial response from a nondestructive evaluation inspection system is measurable and can be classified as a continuous variable (for example, data collected from an Eddy Current inspection system), then Practice E3023 may be more appropriate.
5.4 Prior to performing the analysis it is assumed that the discontinuity of interest is clearly defined; the number and distribution of induced discontinuity sizes in the POD specimen set is known and well-documented; discontinuities in the POD specimen set are unobstructed; and the POD examination administration procedure (including data collection method) is well-designed, well-defined, under control, and unbiased. The analysis results are only valid if convergence is achieved and the model adequately represents the data.
5.5 The POD analysis method described herein is consistent with the analysis method for binary data described in MIL-HDBK-1823A, and is included in several widely utilized POD software packages to perform a POD analysis on hit/miss data. It is also found in statistical software packages that have generalized line...
SCOPE
1.1 This practice covers the procedure for performing a statistical analysis on nondestructive testing hit/miss data to determine the demonstrated probability of detection (POD) for a specific set of examination parameters. Topics covered include the standard hit/miss POD curve formulation, validation techniques, and correct interpretation of results.
1.2 The values stated in inch-pound units are to be regarded as standard. The values given in parentheses are mathematical conversions to SI units that are provided for information only and are not considered standard.
1.3 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety, health, and environmental practices and determine the applicability of regulatory limitations prior to use.
1.4 This international standard was developed in accordance with internationally recognized principles on standardization established in the Decision on Principles for the Development of International Standards, Guides and Recommendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee.
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- Standard14 pagesEnglish languagesale 15% off
This document provides a) a discussion of alternative experimental designs for the determination of trueness and precision measures including reproducibility, repeatability and selected measures of intermediate precision of a standard measurement method, including a review of the circumstances in which their use is necessary or beneficial, and guidance as to the interpretation and application of the resulting estimates, and b) worked examples including specific designs and computations. Each of the alternative designs discussed in this document is intended to address one (or several) of the following issues: a) a discussion of the implications of the definitions of intermediate precision measures; b) a guidance on the interpretation and application of the estimates of intermediate precision measures in practical situations; c) determining reproducibility, repeatability and selected measures of intermediate precision; d) improved determination of reproducibility and other measures of precision; e) improving the estimate of the sample mean; f) determining the range of in-house repeatability standard deviations; g) determining other precision components such as operator variability; h) determining the level of reliability of precision estimates; i) reducing the minimum number of participating laboratories by optimizing the reliability of precision estimates; j) avoiding distorted estimations of repeatability (split-level designs); k) avoiding distorted estimations of reproducibility (taking the heterogeneity of the material into consideration). Often, the performance of the method whose precision is being evaluated in a collaborative study will have previously been assessed in a single-laboratory validation study conducted by the laboratory which developed it. Relevant factors for the determination of intermediary precision will have been identified in this prior single-laboratory study.
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This document provides detailed descriptions of statistical methods for proficiency testing providers to use to design proficiency testing schemes and to analyse the data obtained from those schemes. This document provides recommendations on the interpretation of proficiency testing data by participants in such proficiency testing schemes and by accreditation bodies.
The procedures in this document can be applied to demonstrate that the measurement results obtained by laboratories, inspection bodies, and individuals meet specified criteria for acceptable performance.
This document is applicable to proficiency testing where the results reported are either quantitative measurements or qualitative observations on test items.
NOTE The procedures in this document can also be applied for the assessment of expert opinion where the opinions or judgments are reported in a form which can be compared objectively with an independent reference value or a consensus statistic. For example, when classifying proficiency test items into known categories by inspection - or in determining by inspection whether proficiency test items arise, or do not arise, from the same original source - and the classification results are compared objectively, the provisions of this document that relate to nominal (qualitative) properties can be applied.
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This International Standard gives a number of optimized test plans, the corresponding border lines and characteristics. In addition the algorithms for designing test plans using a spreadsheet program are also given, together with guidance on how to choose test plans.
This standard specifies procedures to test whether an observed value of
- failure rate,
- failure intensity,
- mean operating time to failure (MTTF),
- mean operating time between failures (MTBF),
conforms to a given requirement.
It is assumed, except where otherwise stated, that during the accumulated test time, the times to failure or the operating times between failures are independent and identically exponentially distributed. This assumption implies that the failure rate or failure intensity is assumed to be constant.
Four types of test plans are described as follows:
- truncated sequential probability ratio test (SPRT);
- fixed time/failure terminated test (FTFT);
- fixed calendar time terminated test without replacement;
- combined test.
This standard does not cover guidance on how to plan, perform, analyse and report a test. This information can be found in IEC 60300-3-5.
This standard does not describe test conditions. This information can be found in IEC 60605-2 and in IEC 60300-3-5.
- Standard92 pagesEnglish languagee-Library read for1 day
This International Standard gives a number of optimized test plans, the corresponding border lines and characteristics. In addition the algorithms for designing test plans using a spreadsheet program are also given, together with guidance on how to choose test plans. This standard specifies procedures to test whether an observed value of - failure rate, - failure intensity, - mean operating time to failure (MTTF), - mean operating time between failures (MTBF), conforms to a given requirement. It is assumed, except where otherwise stated, that during the accumulated test time, the times to failure or the operating times between failures are independent and identically exponentially distributed. This assumption implies that the failure rate or failure intensity is assumed to be constant. Four types of test plans are described as follows: - truncated sequential probability ratio test (SPRT); - fixed time/failure terminated test (FTFT); - fixed calendar time terminated test without replacement; - combined test. This standard does not cover guidance on how to plan, perform, analyse and report a test. This information can be found in IEC 60300-3-5. This standard does not describe test conditions. This information can be found in IEC 60605-2 and in IEC 60300-3-5.
- Standard92 pagesEnglish languagee-Library read for1 day
This document establishes a guide to the use and understanding of Shewhart control chart approach to the methods for statistical control of a process. This document is limited to the treatment of statistical process control methods using only Shewhart system of charts. Some supplementary material that is consistent with Shewhart approach, such as the use of warning limits, analysis of trend patterns and process capability is briefly introduced. However, there are several other types of control charts which can be used in different situations.
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This document specifies single sampling plans for lot-by-lot inspection under the following conditions:
a) where the inspection procedure is applied to a continuing series of lots of discrete products, all supplied by one producer using one production process;
b) where only a single quality characteristic, x, of these products is taken into consideration, which is measurable on a continuous scale;
c) where production is under statistical control and the quality characteristic, x, is distributed according to a normal distribution or a close approximation to the normal distribution;
d) where a contract or standard defines a lower specification limit, L, an upper specification limit, U, or both. An item is qualified as conforming if its measured quality characteristic, x, satisfies as appropriate one of the following inequalities:
1) x ≥ L (i.e. the lower specification limit is not violated);
2) x ≤ U (i.e. the upper specification limit is not violated);
3) x ≥ L and x ≤ U (i.e. neither the lower nor the upper specification limit is violated).
Inequalities 1) and 2) are cases with a single specification limit, and 3) is a case with double specification limits.
Where double specification limits apply, it is assumed in this document that conformity to both specification limits is equally important to the integrity of the product. In such cases, it is appropriate to apply a single AQL to the combined percentage of a product outside the two specification limits. This is referred to as combined control.
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IEC 61124:2023 gives a number of optimized test plans, the corresponding border lines and characteristics. In addition, the algorithms for designing test plans using a spreadsheet program are also given, together with guidance on how to choose test plans.
This document specifies procedures to test whether an observed value of
failure rate,
failure intensity,
mean operating time to failure (MTTF),
mean operating time between failures (MTBF), conforms to a given requirement.
It is assumed, except where otherwise stated, that during the accumulated test time, the times to failure or the operating times between failures are independent and identically exponentially distributed. This assumption implies that the failure rate or failure intensity is assumed to be constant.
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This document provides statistical techniques for the determination of the reproducibility of the level of detection for a) binary (qualitative) test methods for continuous measurands, e.g. the content of a chemical substance, and b) binary (qualitative) test methods for discrete measurands, e.g. the number of RNA copies in a sample. The reproducibility precision is determined according to ISO 5725 (all parts). Precision estimates are subject to random variability. Accordingly, it is important to determine the uncertainty associated with each estimate, and to understand the relationship between this uncertainty, the number of participants and the experimental design. This document thus provides not only a description of statistical tools for the calculation of the LOD reproducibility precision, but also for the standard error of the estimates.
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The current ISO 16355 series is written intentionally independent of industry because the principles of applying statistical methods for product and technology development are similar for all types of products. However, when applying the standard for the development of fully or partially digitized products in practice, specific characteristics of digital goods in product development (such as measurability, immateriality, economies of scale effects, etc.) are taken into account. This document gives guidelines for adapting the quality function deployment (QFD) process, its purpose, users, and tools as they are described in the ISO 16355 series that consider these specific characteristics for developing digitalized products and services. Table 1 illustrates the scope of this document by stating examples of the types of products the standard focuses on. Users of this document include all organization functions necessary to assure customer satisfaction, including business planning, marketing, sales, research and development (R&D), engineering, information technology (IT), manufacturing, procurement, quality, production, service, packaging and logistics, support, testing, regulatory, and other phases in hardware, software, service, and system organizations.
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ABSTRACT
This practice presents methodology for the setting of an upper confidence bound regarding an unknown fraction or quantity non-conforming, or a rate of occurrence for nonconformities, in cases where the method of attributes is used and there is a zero response in a sample. Three cases are considered. In Case 1, the sample is selected from a process or a very large population of interest. In Case 2, a sample of n items is selected at random from a finite lot of N items. In Case 3, there is a process, but the output is a continuum, such as area (for example, a roll of paper or other material, a field of crop), volume (for example, a volume of liquid or gas), or time (for example, hours, days, quarterly, etc.) The sample size is defined as that portion of the �continuum� sampled, and the defined attribute may occur any number of times over the sampled portion.
SIGNIFICANCE AND USE
4.1 In Case 1, the sample is selected from a process or a very large population of interest. The population is essentially unlimited, and each item either has or has not the defined attribute. The population (process) has an unknown fraction of items p (long run average process non-conforming) having the attribute. The sample is a group of n discrete items selected at random from the process or population under consideration, and the attribute is not exhibited in the sample. The objective is to determine an upper confidence bound, pu, for the unknown fraction p whereby one can claim that p ≤ pu with some confidence coefficient (probability) C. The binomial distribution is the sampling distribution in this case.
4.2 In Case 2, a sample of n items is selected at random from a finite lot of N items. Like Case 1, each item either has or has not the defined attribute, and the population has an unknown number, D, of items having the attribute. The sample does not exhibit the attribute. The objective is to determine an upper confidence bound, Du, for the unknown number D, whereby one can claim that D ≤ Du with some confidence coefficient (probability) C. The hypergeometric distribution is the sampling distribution in this case.
4.3 In Case 3, there is a process, but the output is a continuum, such as area (for example, a roll of paper or other material, a field of crop), volume (for example, a volume of liquid or gas), or time (for example, hours, days, quarterly, etc.) The sample size is defined as that portion of the “continuum” sampled, and the defined attribute may occur any number of times over the sampled portion. There is an unknown average rate of occurrence, λ, for the defined attribute over the sampled interval of the continuum that is of interest. The sample does not exhibit the attribute. For a roll of paper, this might be blemishes per 100 ft2; for a volume of liquid, microbes per cubic litre; for a field of crop, spores per acre; for a time interval, ca...
SCOPE
1.1 This practice presents methodology for the setting of an upper confidence bound regarding a unknown fraction or quantity non-conforming, or a rate of occurrence for nonconformities, in cases where the method of attributes is used and there is a zero response in a sample. Three cases are considered.
1.1.1 The sample is selected from a process or a very large population 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 finite lot of discrete items, and the number of non-conforming items in 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 the sample is zero.
1.2 Allowance is made for misclassification error in this practice, but only when misclassification rates are well understood or known and can be approximated numerically.
1.3 The values stated in inch-pound units are to be regarded as standard. No other units of measurement are included in this standard.
1.4 This ...
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SIGNIFICANCE AND USE
5.1 The standard deviation, or one of its derivatives, such as relative standard deviation or pooled standard deviation, derived from this practice, provides an estimate of precision in a measured value. Such results are ordinarily expressed as the mean value ± the standard deviation, that is, X ± s.
5.2 If the measured values are, in the statistical sense, “normally” distributed about their mean, then the meaning of the standard deviation is that there is a 67 % chance, that is 2 in 3, that a given value will lie within the range of ± one standard deviation of the mean value. Similarly, there is a 95 % chance, that is 19 in 20, that a given value will lie within the range of ± two standard deviations of the mean. The two standard deviation range is sometimes used as a test for outlying measurements.
5.3 The calculation of precision in the slope and intercept of a line, derived from experimental data, commonly is required in the determination of kinetic parameters, vapor pressure or enthalpy of vaporization. This practice describes how to obtain these and other statistically derived values associated with measurements by thermal analysis.
SCOPE
1.1 This practice details the statistical data treatment used in some thermal analysis methods.
1.2 The method describes the commonly encountered statistical tools of the mean, standard derivation, relative standard deviation, pooled standard deviation, pooled relative standard deviation, the best fit to a (linear regression of a) straight line (or plane), and propagation of uncertainties for all calculations encountered in thermal analysis methods (see Practice E2586).
1.3 Some thermal analysis methods derive the analytical value from the slope or intercept of a linear regression straight line (or plane) assigned to three or more sets of data pairs. Such methods may require an estimation of the precision in the determined slope or intercept. The determination of this precision is not a common statistical tool. This practice details the process for obtaining such information about precision.
1.4 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety, health, and environmental practices and determine the applicability of regulatory limitations prior to use.
1.5 This international standard was developed in accordance with internationally recognized principles on standardization established in the Decision on Principles for the Development of International Standards, Guides and Recommendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee.
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- Standard5 pagesEnglish languagesale 15% off
ABSTRACT
This practice is primarily a statement of principals for the guidance of ASTM technical committees and others in the use of average outgoing quality limit, AOQL, and lot tolerance percent defective, LTPD, sampling plans for determining acceptable of lots of product. Two general types of tables are given, one based on the concept of lot tolerance, LTPD, and the other on AOQL. For each of the types, tables are provided both for single sampling and for double sampling. Each of the individual tables constitutes a collection of solutions to the problem of minimizing the over-all amount of inspection.
SIGNIFICANCE AND USE
4.1 Two general types of tables (Note 1) are given, one based on the concept of lot tolerance, LTPD, and the other on AOQL. The broad conditions under which the different types have been found best adapted are indicated below.
4.1.1 For each of the types, tables are provided both for single sampling and for double sampling. Each of the individual tables constitutes a collection of solutions to the problem of minimizing the over-all amount of inspection. Because each line in the tables covers a range of lot sizes, the AOQL values in the LTPD tables and the LTPD values in the AOQL tables are often conservative.
Note 1: Tables in Annex A1 – Annex A4 and parts of the text are reproduced by permission of John R. Wiley and Sons. More extensive tables and discussion of the methods will be found in that text.
4.2 The sampling tables based on lot quality protection (LTPD) (the tables in Annex A1 and Annex A2) are perhaps best adapted to conditions where interest centers on each lot separately, for example, where the individual lot tends to retain its identity either from a shipment or a service standpoint. These tables have been found particularly useful in inspections made by the ultimate consumer or a purchasing agent for lots or shipments purchased more or less intermittently.
4.3 The sampling tables based on average quality protection (AOQL) (the tables in Annex A3 and Annex A4) are especially adapted for use where interest centers on the average quality of product after inspection rather than on the quality of each individual lot and where inspection is, therefore, intended to serve, if necessary, as a partial screen for defective pieces. The latter point of view has been found particularly helpful, for example, in consumer inspections of continuing purchases of large quantities of a product and in manufacturing process inspections of parts where the inspection lots tend to lose their identity by merger in a common storeroom from which quantities are withdraw...
SCOPE
1.1 This practice is primarily a statement of principals for the guidance of ASTM technical committees and others in the use of average outgoing quality limit, AOQL, and lot tolerance percent defective, LTPD, sampling plans for determining acceptable of lots of product.
1.2 This international standard was developed in accordance with internationally recognized principles on standardization established in the Decision on Principles for the Development of International Standards, Guides and Recommendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee.
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IEC 61123:2019 is intended to define a procedure to verify if a reliability of an item/system complies with the stated requirements. The requirement is assumed to be specified as the percentage of success (success ratio) or the percentage of failures (failure ratio). This document can be used where a number of items are tested (number of trials performed) and classified as passed or failed. It can also be used where one or a number of items are tested repeatedly. The procedures are based on the assumption that the probability of success or failure is the same from trial to trial (statistically independent events). Plans for fixed trial/failure terminated tests as well as truncated sequential probability ratio tests (SPRTs) are included. This document contains extensive tables with ready-to-use SPRT plans and their characteristics for equal and non-equal risks for supplier and customer. In the case of the reliability compliance tests for constant failure rate/intensity, IEC 61124 applies. This second edition cancels and replaces the first edition published in 1991. This edition constitutes a technical revision. This edition includes the following significant technical changes with respect to the previous edition: The sequential probability ratio test (SPRT) [1, 2][1] has been significantly developed in recent years [3, 4, 5]. This edition contains shorter and accurate tests, a wide range of test plans, and significant additional characteristic data, as follows: the tests are significantly truncated (the maximum trial numbers are low) without substantially increasing the expected number of trials to decision (ENT); the true producer’s and consumer’s risks (α', β') are given and very close to the nominal (α, β); the range of the test parameters is wide (failure ratio, risks and discrimination ratio); the test plans include various risk ratios (not restricted to equal risks only); the values of ENT are accurate and given in the relevant region (for practical use); guidelines for extension of the test sets (interpolation and extrapolation) are included. In Annex C, the use of the cumulative binomial distribution function of Excel that simplifies the procedure of designing has been added (Clause C.3). Keywords: verify if a reliability of an item/system complies with the stated requirements
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This document specifies experimental procedures and statistical analysis for the determination of measurement uncertainty in situations where the following conditions are fulfilled: Condition 1: The level of the measurand is non-negative, e.g. concentration level of a contaminant in a sample. Condition 2: Measurement error consists of two independent components: for one of these components the relative standard deviation is constant (that is, the absolute deviation is proportional to the level of the measurand), whereas for the other component the absolute standard deviation is constant (that is, independent of the level of the measurand). Condition 3: Samples for different levels of the measurand can be made available; if the level of the measurand is the concentration of a chemical substance, samples could be obtained e.g. by fortifying (spiking) blank samples. Conditions 1 and 2 are met for most applications of instrumental chemical analyses. Condition 3 can be met for chemical analyses if blank samples are available. This document can also be used to determine precision data for a particular laboratory for different technicians, different environmental conditions, the same or similar test items, with the same level of the measurand, over a certain period of time.
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This document specifies two-stage (double) sampling plans by attributes for inspection for a proportion of nonconforming items in a target population of discrete units, in particular: a) the proportion of nonconforming items in a lot of product items; b) the proportion of nonconforming function instances of an internal control system (ICS); c) the proportion of misstatements in a population of accounting entries or booking records; d) the proportion of nonconforming test characteristics of an entity subject to an acceptance test, e.g. in product and process audits. The plans are preferable to single sampling plans where the cost of inspection is high or where the delay and uncertainty caused by the possible requirement for second samples is inconsequential. The statistical theory underlying the plans, tables and figures are provided in Annexes A through K.
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This document provides detailed descriptions of statistical methods for proficiency testing providers to use to design proficiency testing schemes and to analyse the data obtained from those schemes. This document provides recommendations on the interpretation of proficiency testing data by participants in such proficiency testing schemes and by accreditation bodies. The procedures in this document can be applied to demonstrate that the measurement results obtained by laboratories, inspection bodies, and individuals meet specified criteria for acceptable performance. This document is applicable to proficiency testing where the results reported are either quantitative measurements or qualitative observations on test items. NOTE The procedures in this document can also be applied for the assessment of expert opinion where the opinions or judgments are reported in a form which can be compared objectively with an independent reference value or a consensus statistic. For example, when classifying proficiency test items into known categories by inspection - or in determining by inspection whether proficiency test items arise, or do not arise, from the same original source - and the classification results are compared objectively, the provisions of this document that relate to nominal (qualitative) properties can be applied.
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This document specifies single sampling plans for lot-by-lot inspection under the following conditions: a) where the inspection procedure is applied to a continuing series of lots of discrete products, all supplied by one producer using one production process; b) where only a single quality characteristic, x, of these products is taken into consideration, which is measurable on a continuous scale; c) where production is under statistical control and the quality characteristic, x, is distributed according to a normal distribution or a close approximation to the normal distribution; d) where a contract or standard defines a lower specification limit, L, an upper specification limit, U, or both. An item is qualified as conforming if its measured quality characteristic, x, satisfies as appropriate one of the following inequalities: 1) x ≥ L (i.e. the lower specification limit is not violated); 2) x ≤ U (i.e. the upper specification limit is not violated); 3) x ≥ L and x ≤ U (i.e. neither the lower nor the upper specification limit is violated). Inequalities 1) and 2) are cases with a single specification limit, and 3) is a case with double specification limits. Where double specification limits apply, it is assumed in this document that conformity to both specification limits is equally important to the integrity of the product. In such cases, it is appropriate to apply a single AQL to the combined percentage of a product outside the two specification limits. This is referred to as combined control.
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This document sets out guidelines for checking conformity with quantifiable characteristics using the test or measurement result and its associated measurement uncertainty. This document is applicable whenever the uncertainty may be quantified according to the principles laid down in ISO/IEC Guide‑98‑3 (GUM). The term uncertainty is thus a descriptor for all elements of variation in the measurement result, including uncertainty due to sampling. This document does not give rules for how to act when an inconclusive result of a conformity test has been obtained. NOTE There are not limitations on the nature of the entity subject to the requirements nor on the quantifiable characteristic. Examples of entities together with quantifiable characteristics are given in Table A.1.
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SIGNIFICANCE AND USE
3.1 The GESD procedure can be used to simultaneously identify up to a pre-determined number of outliers (r) in a data set, without having to pre-examine the data set and make a priori decisions as to the location and number of potential outliers.
3.2 The GESD procedure is robust to masking. Masking describes the phenomenon where the existence of multiple outliers can prevent an outlier identification procedure from declaring any of the observations in a data set to be outliers.
3.3 The GESD procedure is automation-friendly, and hence can easily be programmed as automated computer algorithms.
SCOPE
1.1 This practice provides a step by step procedure for the application of the Generalized Extreme Studentized Deviate (GESD) Many-Outlier Procedure to simultaneously identify multiple outliers in a data set. (See Bibliography.)
1.2 This practice is applicable to a data set comprising observations that is represented on a continuous numerical scale.
1.3 This practice is applicable to a data set comprising a minimum of six observations.
1.4 This practice is applicable to a data set where the normal (Gaussian) model is reasonably adequate for the distributional representation of the observations in the data set.
1.5 The probability of false identification of outliers associated with the decision criteria set by this practice is 0.01.
1.6 It is recommended that the execution of this practice be conducted under the guidance of personnel familiar with the statistical principles and assumptions associated with the GESD technique.
1.7 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety, health, and environmental practices and determine the applicability of regulatory limitations prior to use.
1.8 This international standard was developed in accordance with internationally recognized principles on standardization established in the Decision on Principles for the Development of International Standards, Guides and Recommendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee.
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- Standard6 pagesEnglish languagesale 15% off
ABSTRACT
This practice covers simple methods for calculating how many units to include in a random sample in order to estimate with a specified precision, a measure of quality for all the units of a lot of material or produced by a process. It also treats the common situation where the sampling units can be considered to exhibit a single source of variability; it does not treat multi-level sources of variability.
SIGNIFICANCE AND USE
4.1 This practice is intended for use in determining the sample size required to estimate, with specified precision, a measure of quality of a lot or process. The practice applies when quality is expressed as either the lot average for a given property, or as the lot fraction not conforming to prescribed standards. The level of a characteristic may often be taken as an indication of the quality of a material. If so, an estimate of the average value of that characteristic or of the fraction of the observed values that do not conform to a specification for that characteristic becomes a measure of quality with respect to that characteristic. This practice is intended for use in determining the sample size required to estimate, with specified precision, such a measure of the quality of a lot or process either as an average value or as a fraction not conforming to a specified value.
SCOPE
1.1 This practice covers simple methods for calculating how many units to include in a random sample in order to estimate with a specified precision, a measure of quality for all the units of a lot of material, or produced by a process. This practice will clearly indicate the sample size required to estimate the average value of some property or the fraction of nonconforming items produced by a production process during the time interval covered by the random sample. If the process is not in a state of statistical control, the result will not have predictive value for immediate (future) production. The practice treats the common situation where the sampling units can be considered to exhibit a single (overall) source of variability; it does not treat multi-level sources of variability.
1.2 The system of units for this standard is not specified. Dimensional quantities in the standard are presented only as illustrations of calculation methods. The examples are not binding on products or test methods treated.
1.3 This international standard was developed in accordance with internationally recognized principles on standardization established in the Decision on Principles for the Development of International Standards, Guides and Recommendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee.
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This document describes examples for software validation for software implementing the standards of ISO 22514‑7 on the capability of measurement processes. In detail, the following standards are covered: - ISO 22514‑7. It provides data sets and test results for testing the implementation of the evaluation methods described in these standards. This includes: a) the calculation of standard uncertainties from other sources (other than experiments – type B – ISO/IECGuide 98‑3); b) the estimation of uncertainty components using repeated measurements on reference parts; c) the estimation of uncertainty components using repeated measurements on multiple parts with different operators and their evaluation using the ANOVA method; d) the combination of uncertainty components using the Gaussian law of uncertainty propagation; e) the calculation of measurement process capability indices; f) the influence of operators on attributive measurements; g) the uncertainty range and capability indices for attributive measurements. The test examples are intended to cover the calculation of the measuring system capability and measurement process capability according to ISO 22514‑7.
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This document provides guidance for implementing the theories of the ISO 11843 series in various practical situation. As defined in this series, the term minimum detectable value corresponds to the limit of detection or detection limit defined by the IUPAC. The focus of interest is placed on the practical applications of statistics to quantitative analyses.
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This document introduces five statistical methods for evaluating the precision of binary measurement methods and their results. The five methods can be divided into two types. Both types are based on measured values provided by each laboratory participating in a collaborative study. In the first type, each laboratory repeatedly measures a single sample. The samples measured by the laboratories are nominally identical. The second type is an extension of the first type, where there are several levels of samples. For each statistical method, this document briefly summarizes its theory and explains how to estimate the proposed precision measures. Some real cases are illustrated to help the readers understand the evaluation procedures involved. For the first and second types of methods, five and three cases are presented, respectively. Finally, this document compares the five statistical methods.
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This document describes statistical procedures for setting up cumulative sum (CUSUM) schemes for process and quality control using variables (measured) and attribute data. It describes general‑purpose methods of decision-making using cumulative sum (CUSUM) techniques for monitoring, control and retrospective analysis.
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IEC 60812:2018 explains how failure modes and effects analysis (FMEA), including the failure modes, effects and criticality analysis (FMECA) variant, is planned, performed, documented and maintained. The purpose of failure modes and effects analysis (FMEA) is to establish how items or processes might fail to perform their function so that any required treatments could be identified. An FMEA provides a systematic method for identifying modes of failure together with their effects on the item or process, both locally and globally. It may also include identifying the causes of failure modes. Failure modes can be prioritized to support decisions about treatment. Where the ranking of criticality involves at least the severity of consequences, and often other measures of importance, the analysis is known as failure modes, effects and criticality analysis (FMECA). This document is applicable to hardware, software, processes including human action, and their interfaces, in any combination. An FMEA can be used in a safety analysis, for regulatory and other purposes, but this being a generic standard, does not give specific guidance for safety applications. This third edition cancels and replaces the second edition published in 2006. This edition constitutes a technical revision.This edition includes the following significant technical changes with respect to the previous edition: a) the normative text is generic and covers all applications; b) examples of applications for safety, automotive, software and (service) processes have been added as informative annexes; c) tailoring the FMEA for different applications is described; d) different reporting formats are described, including a database information system; e) alternative means of calculating risk priority numbers (RPN) have been added; f) a criticality matrix based method has been added; g) the relationship to other dependability analysis methods have been described. Keywords: failure modes and effects analysis (FMEA), failure modes effects and criticality analysis (FMECA)
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This document establishes single sampling plans for conformance testing, i.e., for assessing whether the quality level of a relevant audit population (lot, process, inventory, file etc) conforms to a declared value. Sampling plans are provided corresponding to four levels of discriminatory ability. The limiting quality ratio (LQR) (see Clause 4) of each sampling plan is given for reference. For levels I-III, the sampling plans have been devised so as to obtain a risk no more than 5 % of contradicting a correct declared quality level. The risk of failing to contradict an incorrectly declared quality level which is related to the LQR is no more than 10 %. The sample sizes for level 0 are designed in a way that the LQR factors of the sampling plans are compatible with the LQR factors for level I.
In contrast to the procedures in the other parts of the ISO 2859 series, the procedures in this document are not applicable to acceptance assessment of lots. Generally, this document mainly focuses on controlling type I error, which differs from the balancing of the risks in the procedures for acceptance sampling.
This document can be used for various forms of quality inspection in situations where objective evidence of conformity to some declared quality level is to be provided by means of inspection of a sample. The procedures are applicable to entities such as lots, process output, etc. that allow random samples of individual items to be taken from the entity.
The sampling plans provided in this document are applicable, but not limited, to the inspection of a variety of targets such as:
— end items;
— components and raw materials;
— operations;
— materials in process;
— supplies in storage;
— maintenance operations;
— data or records;
— administrative procedures;
— accounting procedures or accounting entries;
— internal control procedures.
This document considers two types of quality models for discrete items and populations, as follows.
i) The conforming-nonconforming model, where each item is classified as conforming or nonconforming, and where the quality indicator of a population of items is the proportion p of nonconforming items, or, equivalently, the percentage 100 p of nonconforming items.
ii) The nonconformities model, where the number of nonconformities is counted on each item, and where the quality indicator of a population of items is the average number λ of nonconformities found on items in the population, or, equivalently, the percentage 100 λ of nonconformities on items in the population.
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This document describes the steps for conducting short-term performance studies that are typically performed on machines (including devices, appliances, apparatuses) where parts produced consecutively under repeatability conditions are considered. The number of observations to be analysed vary according to the patterns the data produce, or if the runs (the rate at which items are produced) on the machine are low in quantity. The methods are not considered suitable where the sample size produced is less than 30 observations. Methods for handling the data and carrying out the calculations are described. In addition, machine performance indices and the actions required at the conclusion of a machine performance study are described.
This document is not applicable when tool wear patterns are expected to be present during the duration of the study, nor if autocorrelation between observations is present. The situation where a machine has captured the data, sometimes thousands of data points collected in a minute, is not considered suitable for the application of this document.
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