~196~ International Journal of Statistics and Applied Mathematics 2020; 5(4): 196-200 ISSN: 2456-1452 Maths 2020; 5(4): 196-200 © 2020 Stats & Maths www.mathsjournal.com Received: 12-05-2020 Accepted: 16-06-2020 S Jayalakshmi Assistant Professor, Department of Statistics, Bharathiar University, Coimbatore, Tamil Nadu, India Neena Krishna PK Ph.D., Research Scholar, Department of Statistics, Bharathiar University, Coimbatore, Tamil Nadu, India Corresponding Author: S Jayalakshmi Assistant Professor, Department of Statistics, Bharathiar University, Coimbatore, Tamil Nadu, India Selection of Bayesian skip lot sampling plan-3 with conditional repetitive group sampling plan using Trignometric Ratio Method S Jayalakshmi and Neena Krishna PK Abstract Acceptance sampling plans on the basis of Bayesian methodology can be taken when the innovator has prior knowledge on the process fluctuation to take a opinion about the disposition of the lot. Bayesian sampling plan has a clear picture on the influences associated with the selection of appropriate prior distribution. This paper describes new method for Bayesian Skip Lot Sampling Plan-3 with Conditional Repetitive Group Sampling Plan using Trigonometric Ratio Methods. Tables and numerical examples are also provided. Keywords: Bayesian skip lot sampling plan-3, conditional repetitive group sampling plan, Trignometric ratio method 1. Introduction Acceptance Sampling is undoubtedly a opposing part, intiated as defensive accessory against the intimidation of depreciation in quality. Review of lot and ultimate product is fundamental to establish acceptable quality. Literally the concept of sampling inspection pays extra consideration to revamp the quality and lessen cost in terms of minimal sample size. Under this system manufactures are capable to take inventive production with the use of Bayesian Sampling Plan that is more powerful than current sampling plan, taking lesser surveillance cost and effectiveness over sample size. Bayesian Acceptance Sampling distribution access is combined with the usage of the prior process history for the choice of diffusion (Gamma- Poisson, Beta-Binomial) to explain the random variations involved in acceptance sampling. Bayesian Sampling Plans requires the customer to indicate exceptionally the distribution of the defectives from lot to lot. The skip-lot Sampling methodology was advanced from the approach of continuous sampling plan, that is the quality authority of a product can be revised over the review of limited proportion of very large-conforming lots. The reference plan advanced for commerce purpose to overcome the inspected items of production towards producer risks and consumer risks, alpha and beta risks continued on lot-by-lot inspection. Dodge and Perry (1971) [2] establish the operation of Skip lot Sampling to the condition in one by one lot to be inspected is sampled confer to a few lot inspection plan is nominated as Skip lot Sampling Plan-2. Parker and Kessler (1981) [7] have altered the extant form of Skip lot Sampling Plan-2 under the principles in which at least one lot is consistently sampled from a construction of lot. Vijayaraghavan (1990) [11] described the Skip Lot Sampling Plan-3 which is also an extension of Skip Lot Sampling Plan -2 uses certain elementary methods of continuous sampling. According to Hald (1981) [3] contributed an admirable relation and classical Bayesian theory and procedure for attributes of acceptance sampling. Calvin (1984) [1] has examined clearly and concisely the meaning of ‘how and when to perform Bayesian Acceptance Sampling’. Shankar and Mohapatra (1984) [9] advanced a modern sampling plan, it is an development of classical Repetitive Group Sampling Plan and nominated as Conditional Repetitive Group Sampling (CRGS) Plan. Kuralmani (1992) [5] made some improvement on Repetitive Group and certain relevant Conditional Sampling Plans. Latha (2002) [6] has explained the probability of acceptance for the Bayesian Single Sampling plan.