Efficient sampling by artificial attributes

Avraham Beja; Shaul P. Ladany

doi:10.1080/00401706.1974.10489243

Efficient sampling by artificial attributes

Avraham Beja, Shaul P. Ladany

Research output: Contribution to journal › Article › peer-review

16 Scopus citations

Abstract

Hypotheses about the fraction of items in a lot possessing a “specification attribute” X < L can be tested by generally sampling the variable X or directly sampling the attribute of interest. When the process variance is known, it is often more efficient to test against “compressed limits” for one or more “artificial” attributes X < L_a, X < L_b etc. This study discusses the efficient choice of one or two compressed limits. General guidelines for this choice are suggested, and then evaluated under many hypothetical test specifications. One compressed limit offered ˜40% – 97% savings over direct attribute sampling; two limits allowed about 20% further savings.

Original language	English
Pages (from-to)	601-611
Number of pages	11
Journal	Technometrics
Volume	16
Issue number	4
DOIs	https://doi.org/10.1080/00401706.1974.10489243
State	Published - 1 Jan 1974
Externally published	Yes

Keywords

Artificial Attributes
Compressed Limit Sampling Plans
Sample Size
Sampling
Sequential Sampling

ASJC Scopus subject areas

Statistics and Probability
Modeling and Simulation
Applied Mathematics

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10.1080/00401706.1974.10489243

Cite this

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KW - Compressed Limit Sampling Plans

KW - Sample Size

KW - Sampling

KW - Sequential Sampling

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Efficient sampling by artificial attributes

Abstract

Keywords

ASJC Scopus subject areas

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