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Standardizing an Assessment
Published in Lucy Jane Miller, Developing Norm-Referenced Standardized Tests, 2020
James Gyurke, Aurelio Prifitera
The degree of coordination depends upon the scope of the sampling plan. Obviously, a large number and wide geographical distribution of testing sites require much more logistic planning than few sites confined to a few locations. It is therefore important when deciding upon the sampling plan to consider how many and which sites are important for obtaining the information necessary for the test’s intended purpose.
Drug Products with Multiple Components—Development of TCM
Published in Shein-Chung Chow, Innovative Statistics in Regulatory Science, 2019
Tse et al. (2006) proposed a statistical quality control (QC) method to assess a proposed consistency index of raw materials, which are from different resources and/or final product, and may be manufactured at different sites. The idea is to construct a 95% confidence interval for a proposed consistency index under a sampling plan. If the constructed 95% confidence lower limit is greater than a pre-specified QC lower limit, then we claim that the raw materials or final product has passed the QC and hence can be released for further process or use. Otherwise, the raw materials and/or final product should be rejected. For a given component (the most active component if possible), sampling plan is derived to ensure that there is a desired probability for establishing consistency between sites when truly there is no difference in raw materials or final products between sites. The statistical quality control method for assessment of consistency proposed by Tse et al. (2006) is described below.
Packaging of Cosmetic and Personal Care Products
Published in Heather A.E. Benson, Michael S. Roberts, Vânia Rodrigues Leite-Silva, Kenneth A. Walters, Cosmetic Formulation, 2019
Antônio Celso da Silva, Celio Takashi Higuchi, Heather A.E. Benson, Vânia Rodrigues Leite-Silva
The sampling plan defines how many units should be analyzed in each batch received. Sampling can never be targeted, that is, samples should not be taken from a single box or package. Packages must be picked at random and be representative of the full batch.
Estimation of the probability content in a specified interval using fiducial approach
Published in Journal of Applied Statistics, 2021
Ngan Hoang-Nguyen-Thuy, K. Krishnamoorthy
Mechanical parts are manufactured to meet some tolerance specification limits so that they can be used for their intended purpose. For example, if a shaft is designed to have a ‘sliding fit’ in a hole, the shaft should be little smaller than the hole. Specifically, if a shaft with a nominal diameter of 10 mm is to have a sliding fit within a hole, the shaft might be specified with a tolerance range from 9.964 to 10 mm, and the hole might be specified with a tolerance range from 10.04 to 10.076 mm. Both the shaft and hole sizes will usually form normal distributions.1 In electrical components production, an electrical specification might call for a resistor with a nominal value of 100 ohms, but will also state a tolerance such as ±1%. Thus, in many applications, one needs to assess the percentage of parts that meet the specifications. For example, the acceptance sampling plan, an important statistical method, is commonly used in quality control. In particular, the plan is used to accept/reject a shipment of a product based on some quality characteristics of the parts in a sample from the shipment. Such methods are also used in different stages of production by a manufacturer. If an acceptance sampling plan is based on a continuous variable type data, and it is designed to accept/reject the shipment or a production process on the basis of the percentage of parts satisfy the tolerance specifications, then a confidence interval (CI) or hypothesis test for the true percentage of parts that meet specification is required to implement the acceptance sampling plan.
Variables acceptance reliability sampling plan for items subject to inverse Gaussian degradation process
Published in Journal of Applied Statistics, 2021
The paper is organized as follows. In Section 2, we develop a new reliability sampling plan for items subject to degradation phenomena. A reliability degradation testing procedure is defined and the rule for acceptance and rejection is specified. The parameter values of the sampling plan which takes into account the two types of risks are determined, given the producer’s risk and consumer’s risk. A procedure which is efficient and convenient in determining the parameters of the sampling plan is suggested. A study to compare the proposed sampling plan with the conventional sampling plan is also performed. In Section 3, we study the effect of the sampling plan developed in Section 2 on the reliability characteristic of items in the population which has passed the testing procedure. To this end, we compare the populations before and after the degradation testing procedure under some stochastic sense. In Section 4, some concluding remarks are given.
Acceptance sampling plans from truncated life test based on Tsallis q-exponential distribution
Published in Journal of Applied Statistics, 2020
Amjad D. Al-Nasser, Mohammed Obeidat
In this section, the advantages of the proposed plan (TQED) are compared with other ASP under various types of distributions. The comparison criterion will be the cost of inspection based on the sample size of the ASP. We said that a sampling plan with a smaller sample size is more efficient in reducing the cost of inspection compared to other sampling plans. The proposed ASP will be compared with ASP proposed by Aslam et al. [15], for the generalized exponential distribution (GED), Balakrishnan et al. [18] for the generalized Birnbaum–Saunders distribution (GBSD) and Sampath and Lalitha [29] for the hybrid exponential distribution (HED). The comparisons were made at confidence level n, c, c = 2, 4, 6, 8; and under the initial values of 4.