Design of Sampling Plan for Exponential Distribution Under Neutrosophic Statistical Interval Method

The sampling plan using the classical statistics under the exponential distribution can be applied only when there are certainty and clearness and in observations and parameters. But, in practice, it is not necessary that under some circumstances all the observations/parameters are determined. So, w...

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Veröffentlicht in:IEEE access Jg. 6; S. 64153 - 64158
1. Verfasser: Aslam, Muhammad
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Automobiles
consumer’s risk
Exponential distribution
Fuzzy environment
Inspection
Manufacturing industries
neutrosophic method
neutrosophic parameters
Optimization
Parameters
Probability distribution functions
producer’s risk
Sampling
Statistical analysis
Statistical methods
Statistics
Testing
ISSN:2169-3536, 2169-3536
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Zusammenfassung:The sampling plan using the classical statistics under the exponential distribution can be applied only when there are certainty and clearness and in observations and parameters. But, in practice, it is not necessary that under some circumstances all the observations/parameters are determined. So, we cannot analyze them using the classical statistics which provides results in the determined values. The neutrosophic statistics which is the generation of classical statistics can be applied to the analysis when parameters/observations are incomplete, indeterminate, and vague imprecise. In this paper, we will design originally a sampling plan for the exponential distribution under the neutrosophic interval statistical method. The neutrosophic plan parameters of the proposed plan will be determined through the neutrosophic non-linear problem. The tables for various values of risk are presented for the use in the industrial. An example from the automobiles manufacturing industry is given to explain for the exponential distribution under the neutrosophic interval statistical method.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2018.2877923