997 resultados para sequential sampling


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Researchers typically tackle questions by constructing powerful, highlyreplicated sampling protocols or experimental designs. Such approaches often demand large samples sizes and are usually only conducted on a once-off basis. In contrast, many industries need to continually monitor phenomena such as equipment reliability, water quality, or the abundance of a pest. In such instances, costs and time inherent in sampling preclude the use of highlyintensive methods. Ideally, one wants to collect the absolute minimum number of samples needed to make an appropriate decision. Sequential sampling, wherein the sample size is a function of the results of the sampling process itself, offers a practicable solution. But smaller sample sizes equate to less knowledge about the population, and thus an increased risk of making an incorrect management decision. There are various statistical techniques to account for and measure risk in sequential sampling plans. We illustrate these methods and assess them using examples relating to the management of arthropod pests in commercial crops, but they can be applied to any situation where sequential sampling is used.

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Construction of a confidence interval for process capability index CPM is often based on a normal approximation with fixed sample size. In this article, we describe a different approach in constructing a fixed-width confidence interval for process capability index CPM with a preassigned accuracy by using a combination of bootstrap and sequential sampling schemes. The optimal sample size required to achieve a preassigned confidence level is obtained using both two-stage and modified two-stage sequential procedures. The procedure developed is also validated using an extensive simulation study.

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Capability indices in both univariate and multivariate processes are extensively employed in quality control to assess the quality status of production batches before their release for operational use. It is traditionally a measure of the ratio of the allowable process spread and the actual spread. In this paper, we will adopt a bootstrap and sequential sampling procedures to determine the optimal sample size for estimating a multivariate capability index introduced by Pearns et. al. [12]. Bootstrap techniques have the distinct advantage of placing very minimum requirement on the distributions of the underlying quality characteristics, thereby rendering them more relevant under a wide variety of situations. Finally, we provide several numerical examples where the sequential sampling procedures are evaluated and compared.

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The objective of the present study was to develop a sequential sampling plan for the decision-making process to control Tenuipalpus heveae Baker (Acari: Tenuipalpidae), an important pest of the rubber tree crop. The experimental area was represented by 1,000 plants of the RRIM 600 clone divided in 100 plots with 10 plants each. Leaves were collected and the number of mites determined under laboratory conditions. The sequential sampling plan was developed in accordance with the Sequential Test Likelihood Ratio. The value 0.10 was pre-established for α and β representing type I and type II errors, respectively. The level of control adopted was six mites per 12 cm2. The operating characteristic curve and the curve of maximum expected sample were determined. Two lines were generated: the upper one, when the condition for chemical control is recommended (S1 = 23.3080 + 2.1972); and the lower, when chemical control is not recommended (S0 = -23.3080 + 2.1972). Sample size for the decision-making process to control T. heveae requires 6 to 18 plants. © 2013 Sociedade Entomológica do Brasil.

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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In the seed production system, genetic purity is one of the fundamental requirements for its commercialization. The present work had the goal of determined the sample size for genetic purity evaluation, in order to protect the seed consumer and the producer and to evaluate the sensitivity of microsatellite technique for discriminating hybrids from their respective relatives and for detecting mixtures when they are present in small amounts in the samples. For the sequential sampling, hybrid seeds were marked and mixed in with the seed lots, simulating the following levels of contamination: 0.25, 0.5, 1.0, 2.0, 4.0, and 6.0%. After this, groups of 40 seeds were taken in sequence, up to a maximum of 400 seeds, with the objective of determining the quantity of seeds necessary to detect the percentage of mixture mentioned above. The sensitivity of microsatellite technique was evaluated by mixing different proportions of DNA from the hybrids with their respective seed lines. For the level of mixture was higher than 1:8 (1P1:8P2; 8P1:1P2), the sensitivity of the marker in detecting different proportions of the mixture varied according to the primer used. In terms of the sequential sampling, it was verified that in order to detect mixture levels higher than 1% within the seed lot- with a risk level for both the producer and the consumer of 0.05- the size of the necessary sample was smaller than the size needed for the fixed sample size. This also made it possible to reduce costs, making it possible to use microsatellites to certify the genetic purity of corn seeds lots.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Prepared in cooperation with South Carolina Agricultural Experiment Station.

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Helicoverpa spp. is the primary pest in the Australian fresh-market tomato industry. We describe the spatial distribution of Helicoverpa spp. eggs on fresh-market tomato crops in the Goulburn Valley region of Victoria, and present a sequential sampling plan for monitoring population densities. The distribution of Helicoverpa spp. eggs was highly contagious, as indicated by a Taylor's b-value of 1.59. This high level of contagion meant that relatively large sample sizes would need to be collected to obtain an estimate of population density. High-precision sampling plans generally necessitated impractical sample sizes, and thus the plan we present is a relatively low-precision level plan (SE/mean = 0.3). Nonetheless, this level of precision is considered adequate for most agronomic scenarios. The plan was validated using a statistical re-sampling approach. The level of precision achieved was generally close to the nominal level. Likewise, the number of samples collected generally showed little departure from the theoretically calculated minimum sample size.

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Objective: Diarrhoea in the enterally tube fed (ETF) intensive care unit (ICU) patient is a multifactorial problem. Diarrhoeal aetiologies in this patient cohort remain debatable; however, the consequences of diarrhoea have been well established and include electrolyte imbalance, dehydration, bacterial translocation, peri anal wound contamination and sleep deprivation. This study examined the incidence of diarrhoea and explored factors contributing to the development of diarrhoea in the ETF, critically ill, adult patient. ---------- Method: After institutional ethical review and approval, a single centre medical chart audit was undertaken to examine the incidence of diarrhoea in ETF, critically ill patients. Retrospective, non-probability sequential sampling was used of all emergency admission adult ICU patients who met the inclusion/exclusion criteria. ---------- Results: Fifty patients were audited. Faecal frequency, consistency and quantity were considered important criteria in defining ETF diarrhoea. The incidence of diarrhoea was 78%. Total patient diarrhoea days (r = 0.422; p = 0.02) and total diarrhoea frequency (r = 0.313; p = 0.027) increased when the patient was ETF for longer periods of time. Increased severity of illness, peripheral oxygen saturation (Sp02), glucose control, albumin and white cell count were found to be statistically significant factors for the development of diarrhoea. ---------- Conclusion: Diarrhoea in ETF critically ill patients is multi-factorial. The early identification of diarrhoea risk factors and the development of a diarrhoea risk management algorithm is recommended.

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La prise de décision est un processus computationnel fondamental dans de nombreux aspects du comportement animal. Le modèle le plus souvent rencontré dans les études portant sur la prise de décision est appelé modèle de diffusion. Depuis longtemps, il explique une grande variété de données comportementales et neurophysiologiques dans ce domaine. Cependant, un autre modèle, le modèle d’urgence, explique tout aussi bien ces mêmes données et ce de façon parcimonieuse et davantage encrée sur la théorie. Dans ce travail, nous aborderons tout d’abord les origines et le développement du modèle de diffusion et nous verrons comment il a été établi en tant que cadre de travail pour l’interprétation de la plupart des données expérimentales liées à la prise de décision. Ce faisant, nous relèveront ses points forts afin de le comparer ensuite de manière objective et rigoureuse à des modèles alternatifs. Nous réexaminerons un nombre d’assomptions implicites et explicites faites par ce modèle et nous mettrons alors l’accent sur certains de ses défauts. Cette analyse servira de cadre à notre introduction et notre discussion du modèle d’urgence. Enfin, nous présenterons une expérience dont la méthodologie permet de dissocier les deux modèles, et dont les résultats illustrent les limites empiriques et théoriques du modèle de diffusion et démontrent en revanche clairement la validité du modèle d'urgence. Nous terminerons en discutant l'apport potentiel du modèle d'urgence pour l'étude de certaines pathologies cérébrales, en mettant l'accent sur de nouvelles perspectives de recherche.

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Introducción: La bronquiolitis se ha convertido en una patología de alta relevancia clínica y de salud pública, de la cual se han realizado múltiples estudios en cuanto a tratamiento y diagnóstico; Identificar el perfil de los pacientes que presentan esta patología en nuestra población justifica el profundizar en su conocimiento y contexto a nivel local. Metodología: Se realizó un estudio observacional descriptivo de serie de casos. Muestreo consecutivo o secuencial de pacientes con bronquiolitis que cumplieron los criterios de selección, durante el 2011. La información se analizó en SPSS. Se realizó un análisis descriptivo y análisis para determinar la posible asociación entre las variables. Resultados: El total de pacientes en el estudio fue 92. Se encontraron una serie de características comunes, discriminadas en dos grupos, características sociodemográficas de los pacientes y sus padres y características o manifestaciones clínicas de los pacientes, al ingreso, durante y al egreso de su hospitalización. Discusión: Las características sociodemográficas que identifican a los pacientes que presentan bronquiolitis pueden ser determinantes, como pertenecer a población vulnerable, como los pacientes recién nacidos, o lactantes menores; pertenecer a una comunidad en la cual haya presencia de niños en edad escolar. Conclusiones: Los pacientes con riesgo de presentar bronquiolitis, para este estudio, son lactantes menores y recién nacidos; hijos de padres profesionales, y bachilleres, y provenientes de la ciudad de Bogotá. A nivel socio demográfico se encontró que convivir con personas fumadoras y niños en edad escolar no mostró una diferencia en la distribución porcentual de estas variables.