scholarly journals THE ACCEPTANCE CONTROL COSTS FOR VARIABLE SAMPLING IN CASE OF DISTRIBUTION OF CHARACTERISTIC INCOMPATIBLE WITH ASSUMPTIONS

2015 ◽  
Vol 3 (314) ◽  
Author(s):  
Magdalena Chmielińska
Keyword(s):  
Sample Size ◽  
Normal Distribution ◽  
Classical Method ◽  
Quality Characteristic ◽  
Acceptance Sampling ◽  
Variable Sampling ◽  
Acceptance Control

The acceptance sampling is the settlement procedure based on a sample randomly selected from a larger batch of quality in the controlled batch. The inspection can be run in case of variable assessment and attribute assessment. Variable sampling assumes that the parameter of quality characteristic follows the normal distribution. In paper will be presented the procedure of determining the acceptance constant k of acceptance sampling by set sample size and risk of the producer, in the case of distribution of a controlled characteristics significantly different from the normal distribution. In the article the proposed method with the classical method in terms of the generated costs, is compared. It was assumed that in the case of distributions significantly different from normal distribution, the proposed method proves to be cheaper in the application.

scholarly journals Design of a New Synthetic Acceptance Sampling Plan

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 653 ◽  
Author(s):  
Saeed Dobbah ◽  
Muhammad Aslam ◽  
Khushnoor Khan
Keyword(s):  
Standard Deviation ◽  
Normal Distribution ◽  
Operating Characteristic ◽  
Linear Optimization ◽  
Sampling Plan ◽  
Quality Characteristic ◽  
Acceptance Sampling ◽  
Acceptance Sampling Plan ◽  
Non Linear

In this paper, we propose a new synthetic sampling plan assuming that the quality characteristic follows the normal distribution with known and unknown standard deviation. The proposed plan is given and the operating characteristic (OC) function is derived to measure the performance of the proposed sampling plan for some fixed parameters. The parameters of the proposed sampling plan are determined using non-linear optimization solution. A real example is added to explain the use of the proposed plan by industry.

Characterizations of the normal distribution by suitable transformations

1973 ◽  
Vol 10 (1) ◽  
pp. 100-108 ◽  
Author(s):  
S. Beer ◽  
E. Lukacs
Keyword(s):  
Sample Size ◽  
Normal Distribution ◽  
Functional Equations ◽  
Normal Sample ◽  
Population Mean ◽  
Converse Theorems

Yu. V. Linnik showed that certain transformations, given by Formulae (1.1), (1.6) and (1.7) transform a normal sample into itself. The transformations (1.1) and (1.7) apply to samples of size 2 while (1.6) admits an arbitrary sample size. It is also assumed that the population mean is zero.In the present paper the converse theorems are proven so that characterizations of the normal distribution are obtained. The problem leads to the functional equations (2.3) and (2.13) whose solution yields the desired results.

scholarly journals Tamaño óptimo de la muestra

2017 ◽  
Vol 5 (9) ◽  
Author(s):  
M. H. Badii ◽  
J. Castillo ◽  
A. Guillen
Keyword(s):  
Sample Size ◽  
Normal Distribution ◽  
Key Words ◽  
Sample Size Estimation ◽  
Size Estimation ◽  
Bias Estimation ◽  
Population Variance ◽  
Population Proportion ◽  
Estimation Process ◽  
The Mean

Key words: Bias, estimation, population, sampleAbstract. The basics of sample size estimation process are described. Assuming the normal distribution, the procedures for estimation of sample size for the mean; with and without knowledge of the population variance, and population proportion are noted. Sample size for more than one population feature is also given.Palabras clave: Estimación, muestra, población, sesgoResumen. Se describen los fundamentos del proceso de la estimación del tamaño óptimo de la muestra. Suponiendo una distribución normal para una población, se notan los procedimientos de la estimación del tamaño óptimo de la muestra para la media muestral con y sin el conocimiento de la varianza poblacional. Se presenta el tamaño óptimo de la muestra con más de una característica poblacional.

The Application of the Smirnov Test in the Homogeneity Test for Non-Normal Distribution Temperature Series

2012 ◽  
Vol 516-517 ◽  
pp. 530-535
Author(s):  
Xin Jie Deng ◽  
Yang Sheng You ◽  
Yan Ying Chen ◽  
Xue Mei Yang
Keyword(s):  
Normal Distribution ◽  
Classical Method ◽  
Temperature Series ◽  
Homogeneity Test ◽  
Test Results ◽  
Standard Normal Homogeneity Test ◽  
Standard Normal ◽  
Better Than ◽  
Smirnov Test

The homogeneity test is the first stage to revise the climate records. Its accuracy will directly affect the follow-up work. The classic method SNHT (Standard Normal Homogeneity Test) can only be applied in climatic sequences obey normal distribution, but lots of non-normality climate sequences need to be examined. In this paper, the Smirnov Test was introduced to test the homogeneity of the temperature series, which is a classical method for distribution test, and it can apply for the temperature sequences obey any distribution. The homogeneity test results by testing Chongqing Municipality's temperature sequences show that: the Smirnov Test is better than SNHT

Variable sampling plans for normal distribution indexed by Taguchi's loss function

1997 ◽  
Vol 44 (6) ◽  
pp. 591-603 ◽  
Author(s):  
Ikuo Arizono ◽  
Akihiro Kanagawa ◽  
Hiroshi Ohta ◽  
Kyouko Watakabe ◽  
Kouji Tateishi
Keyword(s):  
Normal Distribution ◽  
Loss Function ◽  
Sampling Plans ◽  
Variable Sampling ◽  
Taguchi’S Loss Function
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