China
Africa
Explore chapters and articles related to this topic
Artificial Intelligence Basics
Published in Subasish Das, Artificial Intelligence in Highway Safety, 2023
Systematic Sampling: A systematic sampling design selects a single starting unit at random and determines a fixed interval for all other units in the population. This method is also called quasi-random sampling, as random sampling is usually conducted as the first choice. For example, if a population has N = 2,000 units and n is taken as 200 units, then the sampling fraction would be 10. A random sample would be selected between 1 and 10, and it would be systemically continued 200 times. Figure 21 illustrates systematic sampling by proving an example. The population has 25 units. To get a sample of eight units, the first unit is randomly selected between 1 and 3. This is systematically continued eight times to complete the sample.
Data collection, processing, and database management
Published in Zongzhi Li, Transportation Asset Management, 2018
Equation 3.11 indicates the optimal sampling fraction that minimizes the cost for a fixed variance or minimizes the variance for a fixed cost nh/n=Nhσh/ch∑h=1HNhσh/chwhere σh = The known SD in stratum h of the population
Continuous Sampling Plans
Published in Edward G. Schilling, Dean V. Neubauer, Acceptance Sampling in Quality Control, 2017
Edward G. Schilling, Dean V. Neubauer
The most celebrated continuous sampling plan and the plan that undoubtedly has received the most application is also the original—the Dodge CSP-1 plan. It is carried out on a stream of product, with items inspected in order of production. The procedure is as follows: Specify sampling fraction (f) and clearing interval (i).Begin 100% inspection.After i units in succession have been found without a defective, start sampling inspection.Randomly inspect a fraction f of the units.When a defective is found, revert to 100% inspection (step 2). A diagrammatic representation of CSP-1 will be found in Figure 15.1.
Optimal CSP-1 boundary scheme based on the estimator of the proportion of conformance for specified in-control process
Published in Quality Technology & Quantitative Management, 2020
Chunzhi Li, Shurong Tong, Keqin Wang, Xinwei Zhang
where is the clearance number, is the sampling fraction, , , , , and only takes a positive integer. The inspection scheme is constructed in the contour and is selected according to the production lot size. However, it is difficult in CSP-1 to identify the boundary of feasible inspection schemes for specified in-control process with constant . Thus, it is imperative to build up the boundary of feasible inspection schemes which can achieve the conforming outgoing quality with and provide all feasible inspection schemes for specified in-control process.
A variable-type skip-lot sampling plan for products with a unilateral specification limit
Published in International Journal of Production Research, 2021
Chien-Wei Wu, Amy H. I. Lee, Yi-San Huang
In the skip-lot sampling process, the sampling fraction f can be determined by both the producer and the consumer based on past transaction records. If the quality of previous products is good and the consumer has confidence on the producer, can be set. If previous quality is above average, or can be set. By referring to the tables provided, plan parameters can be obtained, and the operating procedure stated in section 3.1 can be adopted to implement the sampling plan. For example, if and are given, and is selected, plan parameters can be obtained from Table 2. This indicates that 137 samples will be inspected from the selected lot, and will be calculated. If , the lot will be accepted. If , the lot will be rejected. Once three consecutive lots are accepted, we can switch to the skipping inspection stage. Then, randomly select a fraction of 0.05 from all submitted lots for inspection and inspect 137 samples from the selected lot. If , accept the lots and stay in the skipping inspection stage. If , reject the lots and return to normal inspection.
Water quality and Human Health Risk Assessment: a case study of the Czarna Przemsza River source in Zawiercie, Poland
Published in Human and Ecological Risk Assessment: An International Journal, 2020
Olga Janoska, Agnieszka Gruszecka-Kosowska
While water from the unofficial source of the Czarna Przemsza River is being consumed by inhabitants of Zawiercie city, specifically Bzów district, accurate data concerning water consumption was needed to determine estimated daily intakes (EDI) of analyzed parameters in water. For this purpose, questionnaire surveys were carried out. The district was inhabited by 900 people, thus determining the sampling fraction as 0.5, confidence level of 95%, and maximum error of 5% the questionnaire had to be filled in by 269 people. The questionnaire results concerning water consumption, i.e. daily and non-daily water ingestion rate, sex and age of respondents were used for calculating EDI values.