ISO 16269-4 PDF

This part of ISO provides detailed descriptions of sound statistical testing procedures and graphical data analysis methods for detecting outliers in data. Statistical interpretation of data — Part 4: Detection and treatment of outliers التفسير الإحصائي للبيانات — الجزء4: كشف ومعالجة القيم الشاذة. ISO (E). Statistical interpretation of data – Part 4: Detection and treatment of outliers. Contents. Page. Foreword.

Author: Najinn Zululabar
Country: Mali
Language: English (Spanish)
Genre: Video
Published (Last): 6 January 2012
Pages: 269
PDF File Size: 5.86 Mb
ePub File Size: 16.28 Mb
ISBN: 635-3-20449-881-9
Downloads: 18123
Price: Free* [*Free Regsitration Required]
Uploader: Shakagul

Statistical outlier identification and remediation is a topic that has caused issues in almost every laboratory. There are many causes of outliers, including measurement error, sampling error, incorrect recording, or misspecification of the distributional assumptions.

When the root cause is not known or cannot be identified easily, statistical methods are employed to identify potential outliers for remediation.

Outliers are defined as observations that appear 116269-4 be inconsistent with the rest of the data set. A given data set can have more than one outlier, though it is rare in the laboratory setting. Prior to doing any statistical analysis, data should be isk and checked for assumptions.

Graphical methods can be used to oso accept assumptions such as normality and the lack of outliers. Some methods include the box plot, histogram, and normal probability plot. Other graphical techniques can be utilized as necessary or appropriate. The problem is that outliers can distort and reduce the information contained in the data source or generating mechanism. In the laboratory environment, the existence of outliers will undermine the effectiveness and accuracy of any result generated.

  LAELIA SPECIOSA PDF

Possible outliers are not necessarily bad or erroneous; they just do not reflect the expected outcome of the method.

BS ISO 16269-4:2010

In some situations, an outlier may carry essential information and thus it should be identified for further study. Often they contain valuable information about the process under investigation or the data-gathering and recording process.

Before considering the possible elimination of these points from the data, one should try to understand why they appeared and whether it is likely similar values could be seen in uso future. In other words, are these values within the precision and accuracy of the method? Once an observation is identified either by graphical or visual inspection as a potential outlier, root cause analysis should begin to determine whether an assignable cause can be found for the spurious result.

If no root cause can be determined, and a retest can be justified, the potential outlier should be recorded for future evaluation as more data become available. Removing data points on the basis of statistical analysis without an assignable cause is not sufficient. Statistical significance does not imply causation. Robust or nonparametric statistical methods are alternative methods for analysis. Robust statistical methods such as weighted least-squares regression minimize the effect of an outlier observation.

In practice, the number of outliers in the sample should be small. If there are many outliers in the data set, iwo ceases to be an outlier detection problem and different approaches are needed.

GSO ISO – Standards Store – GCC Standardization Organization

One or more outliers on either side of a normal data set can be detected by using a procedure known as the generalized extreme studentized deviate 61269-4. Compare the computed value R i to the table value see Table 2. Dixon-type tests are based on the ratio of the ranges. These tests are flexible enough to allow for specific observations to be tested.

  GANTTPROJECT TUTORIAL ESPAOL PDF

They also perform well with small sample sizes.

Because they are based on ordered statistics, there is no need to assume normality of the data. The following equation is for the largest or smallest observation being an outlier:. If the distance between the potential outlier to its nearest neighbor is large enough, it would be considered an outlier. See Table 3 for the critical values for r 10 ratio.

The value of r 10 is more than the critical value 0.

ISO 16269-4:2010

12669-4 It is of great importance to identify a sound subset of methods used in the identification and treatment of outliers. The ISO standard Scientists are eligible for a full print or digital subscription. Keep up with our latest articles, news and events.

Plus, get special offers and more delivered to your inbox. Statistical Outliers in the Laboratory Setting. Monday, March 3, Table 1 — Relative potency.

Table 3 — Critical values for Dixon test. Claim your Complimentary Subscription. Subscribe to eNewsletters and Email Alerts. Monday, December 31, Friday, December 28, Thursday, December 27, Wednesday, December 19, Tuesday, December 18, Monday, December 17,