ImageVerifierCode 换一换
格式:PDF , 页数:6 ,大小:78.42KB ,
资源ID:173813      下载积分:11 积分
快捷下载
登录下载
邮箱/手机:
温馨提示:
快捷下载时,用户名和密码都是您填写的邮箱或者手机号,方便查询和重复下载(系统自动生成)。 如填写123,账号就是123,密码也是123。
特别说明:
请自助下载,系统不会自动发送文件的哦; 如果您已付费,想二次下载,请登录后访问:我的下载记录
支付方式: 支付宝扫码支付 微信扫码支付   
验证码:   换一换

加入VIP,免费下载
 

温馨提示:由于个人手机设置不同,如果发现不能下载,请复制以下地址【https://www.wnwk.com/docdown/173813.html】到电脑端继续下载(重复下载不扣费)。

已注册用户请登录:
账号:
密码:
验证码:   换一换
  忘记密码?
三方登录: QQ登录  

下载须知

1: 本站所有资源如无特殊说明,都需要本地电脑安装OFFICE2007和PDF阅读器。
2: 试题试卷类文档,如果标题没有明确说明有答案则都视为没有答案,请知晓。
3: 文件的所有权益归上传用户所有。
4. 未经权益所有人同意不得将文件中的内容挪作商业或盈利用途。
5. 本站仅提供交流平台,并不能对任何下载内容负责。
6. 下载文件中如有侵权或不适当内容,请与我们联系,我们立即纠正。
7. 本站不保证下载资源的准确性、安全性和完整性, 同时也不承担用户因使用这些下载资源对自己和他人造成任何形式的伤害或损失。

版权提示 | 免责声明

本文(ASTM_D_7915_-_22.pdf)为本站会员(益****师)主动上传,蜗牛文库仅提供信息存储空间,仅对用户上传内容的表现方式做保护处理,对上载内容本身不做任何修改或编辑。 若此文所含内容侵犯了您的版权或隐私,请立即通知蜗牛文库(发送邮件至admin@wnwk.com或直接QQ联系客服),我们立即给予删除!

ASTM_D_7915_-_22.pdf

1、Designation:D791522An American National StandardStandard Practice forApplication of Generalized Extreme Studentized Deviate(GESD)Technique to Simultaneously Identify MultipleOutliers in a Data Set1This standard is issued under the fixed designation D7915;the number immediately following the designat

2、ion 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 change since the last revision or reapproval.1.Scope*1.1 This practice provides a step by step pr

3、ocedure for theapplication of the Generalized Extreme Studentized Deviate(GESD)Many-Outlier Procedure to simultaneously identifymultiple outliers in a data set.(See Bibliography.)1.2 This practice is applicable to a data set comprisingobservations that is represented on a continuous numericalscale.1

4、.3 This practice is applicable to a data set comprising aminimum of six observations.1.4 This practice is applicable to a data set where the normal(Gaussian)model is reasonably adequate for the distributionalrepresentation of the observations in the data set.1.5 The probability of false identificati

5、on of outliers asso-ciated with the decision criteria set by this practice is 0.01.1.6 It is recommended that the execution of this practice beconducted under the guidance of personnel familiar with thestatistical principles and assumptions associated with theGESD technique.1.7 This standard does no

6、t purport to address all of thesafety concerns,if any,associated with its use.It is theresponsibility of the user of this standard to establish appro-priate safety,health,and environmental practices and deter-mine the applicability of regulatory limitations prior to use.1.8 This international standa

7、rd was developed in accor-dance with internationally recognized principles on standard-ization established in the Decision on Principles for theDevelopment of International Standards,Guides and Recom-mendations issued by the World Trade Organization TechnicalBarriers to Trade(TBT)Committee.2.Termino

8、logy2.1 Definitions of Terms Specific to This Standard:2.1.1 outlier,nan observation(or a subset of observations)which appears to be inconsistent with the remainder of the dataset.3.Significance and Use3.1 The GESD procedure can be used to simultaneouslyidentify up to a pre-determined number of outl

9、iers(r)in a dataset,without having to pre-examine the data set and make apriori decisions as to the location and number of potentialoutliers.3.2 The GESD procedure is robust to masking.Maskingdescribes the phenomenon where the existence of multipleoutliers can prevent an outlier identification proce

10、dure fromdeclaring any of the observations in a data set to be outliers.3.3 The GESD procedure is automation-friendly,and hencecan easily be programmed as automated computer algorithms.4.Procedure4.1 Specify the maximum number of outliers(r)in a data setto be identified.This is the number of cycles

11、required to beexecuted(see 4.2)for the identification of up to r outliers.4.1.1 The recommended maximum number of outliers(r)by this practice is two(2)for data sets with six to twelveobservations.4.1.2 For data sets with more than twelve observations,therecommended maximum number of outliers(r)is th

12、e lesser often(10)or 20%.4.1.3 The recommended values for r in 4.1.1 and 4.1.2 arenot intended to be mandatory.Users can specify other valuesbased on their specific needs.4.2 Set the current cycle number c to 1(c=1).4.2.1 Assign the original data set to be assessed(in 4.1)asthe data set for the curr

13、ent cycle 1 and label it as DTS1.4.3 Compute test statistic T for each observation in the dataset assigned to the current cycle(DTSc)as follows:T 5|x 2 x|s(1)where:x=an observation in the data set,1This practice is under the jurisdiction of ASTM Committee D02 on PetroleumProducts,Liquid Fuels,and Lu

14、bricants and is the direct responsibility of Subcom-mittee D02.94 on Coordinating Subcommittee on Quality Assurance and Statistics.Current edition approved May 1,2022.Published May 2022.Originallyapproved in 1988.Last previous edition approved in 2018 as D7915 18.DOI:10.1520/D7915-22.*A Summary of C

15、hanges section appears at the end of this standardCopyright ASTM International,100 Barr Harbor Drive,PO Box C700,West Conshohocken,PA 19428-2959.United StatesThis international standard was developed in accordance with internationally recognized principles on standardization established in the Decis

16、ion on Principles for theDevelopment of International Standards,Guides and Recommendations issued by the World Trade Organization Technical Barriers to Trade(TBT)Committee.1x=average calculated using all observations in the data set,ands=sample standard deviation calculated using all observa-tions in the data set.4.4 Identify the observation associated with the largestabsolute magnitude of the test statistic T in the data set of thecurrent cycle.4.5 If current cycle c is less than r,execute 4.5.

copyright@ 2008-2023 wnwk.com网站版权所有

经营许可证编号:浙ICP备2024059924号-2