scholarly journals Sample size for estimating mean and coefficient of variation in species of crotalarias

2018 ◽  
Vol 90 (2) ◽  
pp. 1705-1715 ◽  
Author(s):  
MARCOS TOEBE ◽  
LETÍCIA N. MACHADO ◽  
FRANCIELI L. TARTAGLIA ◽  
JULIANA O. DE CARVALHO ◽  
CIRINEU T. BANDEIRA ◽  
...  
Keyword(s):  
Sample Size ◽  
Coefficient Of Variation

A variable sample size run sum coefficient of variation chart

2022 ◽  
Author(s):  
Wai Chung Yeong ◽  
Yen Yoon Tan ◽  
Sok Li Lim ◽  
Khai Wah Khaw ◽  
Michael Boon Chong Khoo
Keyword(s):  
Sample Size ◽  
Coefficient Of Variation ◽  
Variable Sample Size

Modified Automated Determination of 2,3-Diphosphoglycerate in Whole Blood

1972 ◽  
Vol 18 (9) ◽  
pp. 1001-1004 ◽  
Author(s):  
Kenneth F Atkinson
Keyword(s):  
Sample Size ◽  
Coefficient Of Variation ◽  
Whole Blood ◽  
Normal Range ◽  
Blood Samples ◽  
Automated Determination

Abstract A modification is described of the automated determination of 2,3-diphosphoglycerate (DPG) in blood [Grisolia, S., et al., Anal. Biochem. 31, 235 (1969)]. Modifications in the manifold result in a sensitive, noise-free, rapid system and the modifications in the preparations of the reagents ensure stability of the diluted standards and blood samples for at least three weeks. Samples are run at the rate of 60/h and sample size can be as small as 5 µl of whole blood. The coefficient of variation of the overall determination of automated DPG and manual hemoglobin is 3.6% and the SD is ±0.77 µmol/g Hb. The normal range is 14.6 ± 2.2 (SD) µmol/g hemoglobin.

scholarly journals Inspection Strategy under Indeterminacy Based on Neutrosophic Coefficient of Variation

Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 193 ◽  
Author(s):  
Muhammad Aslam ◽  
Mansour Sattam Aldosari
Keyword(s):  
Sample Size ◽  
Coefficient Of Variation ◽  
Operating Characteristic ◽  
Uncertain Data ◽  
Sampling Plan ◽  
Sampling Plans ◽  
Interval Method ◽  
Inspection Strategy ◽  
Classical Statistics

The existing sampling plans which use the coefficient of variation (CV) are designed under classical statistics. These available sampling plans cannot be used for sentencing if the sample or the population has indeterminate, imprecise, unknown, incomplete or uncertain data. In this paper, we introduce the neutrosophic coefficient of variation (NCV) first. We design a sampling plan based on the NCV. The neutrosophic operating characteristic (NOC) function is then given and used to determine the neutrosophic plan parameters under some constraints. The neutrosophic plan parameters such as neutrosophic sample size and neutrosophic acceptance number are determined through the neutrosophic optimization solution. We compare the efficiency of the proposed plan under the neutrosophic statistical interval method with the sampling plan under classical statistics. A real example which has indeterminate data is given to illustrate the proposed plan.

A Method for the Analysis of Total Nitrogen in Natural Waters

1984 ◽  
Vol 41 (5) ◽  
pp. 815-819 ◽  
Author(s):  
J. Kalff ◽  
E. Bentzen
Keyword(s):  
Sample Size ◽  
Total Nitrogen ◽  
Coefficient Of Variation ◽  
Natural Waters ◽  
Sodium Salicylate ◽  
Persulfate Oxidation ◽  
Dissolved Nitrogen

This method for analyzing total nitrogen (TN) in freshwaters is based on the persulfate oxidation of nitrogen to nitrate, followed by the analysis of this nitrate by a modified version of the sodium salicylate method. The method is simpler than other reported techniques for TN in oligotrophy and mesotrophic waters and requires equipment readily available in most laboratories. The method is linear to 1000 μg N/L, with the range extendable by changing the sample size. The variability is lowest (coefficient of variation 6.6%) between 100 and 1000 μg N/L. We have successfully used the method for the determination of TN, as well as dissolved nitrogen (DN) on filtered samples and nitrate on nonoxidized samples.

Monitoring the coefficient of variation using a variable sample size control chart

2015 ◽  
Vol 80 (9-12) ◽  
pp. 1561-1576 ◽  
Author(s):  
Philippe Castagliola ◽  
Ali Achouri ◽  
Hassen Taleb ◽  
Giovanni Celano ◽  
Stelios Psarakis
Keyword(s):  
Sample Size ◽  
Control Chart ◽  
Coefficient Of Variation ◽  
Size Control ◽  
Variable Sample Size

Use of Loose Shelled Kernels to Estimate Aflatoxin in Farmers Stock Peanut Lots1

1999 ◽  
Vol 26 (1) ◽  
pp. 39-44 ◽  
Author(s):  
T. B. Whitaker ◽  
F. G. Giesbrecht ◽  
W. M. Hagler
Keyword(s):  
Sample Size ◽  
Coefficient Of Variation ◽  
Functional Relationship ◽  
Test Sample ◽  
Sampling Plan ◽  
Sample Sizes ◽  
Total Aflatoxin ◽  
Aflatoxin Concentration

Abstract Loose shelled kernels (LSK) are a defined grade component of farmers stock peanuts and represented, on the average, 33.3% of the total aflatoxin mass and 7.7% of the kernel mass among the 120 farmers stock peanut lots studied. The functional relationship between aflatoxin in LSK taken from 2-kg test samples and the aflatoxin in farmers stock peanut lots was determined to be linear with zero intercept and a slope of 0.297. The correlation between aflatoxin in LSK and aflatoxin in the lot was 0.844 which suggests that LSK taken from large test samples can be used to estimate the aflatoxin concentration in a farmer's lot. Using only LSK allows large test samples to be used to estimate the lot concentration since LSK can be easily screened from a large test sample. If LSK accounts for 7.7% of the lot kernel mass, a 50-kg sample will yield about 3.9 kg of LSK which can be easily prepared for aflatoxin analysis. Increasing the test sample size from 2 to 50 kg reduced the coefficient of variation associated with estimating a lot with 100 parts per billion (ppb) aflatoxin from 114 to 23%, respectively. As an example, a farmers stock aflatoxin sampling plan with dual tolerances (10 and 100 ppb) that classified lots into three categories was evaluated for two test sample sizes (2 and 50 kg). The effect of increasing test sample size from 2 to 50 kg on the number of lots classified into each of the three categories was demonstrated when measuring aflatoxin only in LSK.

scholarly journals Understanding between-cluster variation in prevalence and limits for how much variation is plausible

2020 ◽  
pp. 096228022095183
Author(s):  
Mark D Chatfield ◽  
Daniel M Farewell
Keyword(s):  
Standard Deviation ◽  
Sample Size ◽  
Correlation Coefficient ◽  
Coefficient Of Variation ◽  
Binary Data ◽  
True Cluster ◽  
Power Calculations ◽  
Log Odds ◽  
Cluster Variation ◽  
Cluster Correlation

In clinical trials and observational studies of clustered binary data, understanding between-cluster variation is essential: in sample size and power calculations of cluster randomised trials, for example, the intra-cluster correlation coefficient is often specified. However, quantifications of between-cluster variation can be unintuitive, and an intra-cluster correlation coefficient as low as 0.04 may correspond to surprisingly large between-cluster differences. We suggest that understanding is improved through visualising the implied distribution of true cluster prevalences – possibly by assuming they follow a beta distribution – or by calculating their standard deviation, which is more readily interpretable than the intra-cluster correlation coefficient. Even so, the bounded nature of binary data complicates the interpretation of variances as primary measures of uncertainty, and entropy offers an attractive alternative. Appealing to maximum entropy theory, we propose the following rule of thumb: that plausible intra-cluster correlation coefficients and standard deviations of true cluster prevalences are both bounded above by the overall prevalence, its complement, and one third. We also provide corresponding bounds for the coefficient of variation, and for a different standard deviation and intra-cluster correlation defined on the log odds scale. Using previously published data, we observe the quantities defined on the log odds scale to be more transportable between studies with different outcomes with different prevalences than the intra-cluster correlation and coefficient of variation. The latter increase and decrease, respectively, as prevalence increases from 0% to 50%, and the same is true for our bounds. Our work will help clinical trialists better understand between-cluster variation and avoid specifying implausibly high values for the intra-cluster correlation in sample size and power calculations.

scholarly journals Estimating Deoxynivalenol in Shelled Corn Barge Lots by Measuring Deoxynivalenol in Corn Screenings

2003 ◽  
Vol 86 (6) ◽  
pp. 1187-1192 ◽  
Author(s):  
Thomas B Whitaker ◽  
John L Richard ◽  
Francis G Giesbrecht ◽  
Andrew B Slate ◽  
Nelson Ruiz
Keyword(s):  
Sample Size ◽  
Coefficient Of Variation ◽  
Test Sample ◽  
Regression Equations

Abstract To determine if deoxynivalenol (DON) is concentrated in small corn screenings, fourteen to twenty-three 1.1 kg test samples were taken from each of 10 barges of shelled corn. Each of the 181 test samples was divided into 2 components (fines and clean) using a 5 mm screen. The clean component sample rode the 5 mm screen and the fines component sample passed through the 5 mm screen. The DON concentration in fines component sample was about 3 times the DON concentration in the clean component sample. The DON in the 181 fines and clean component samples averaged 689.0 and 206.1 ng/g, respectively. Regression equations were developed to predict the DON in the barge based upon measurements of DON in the fines component sample. The ratio of DON in the lot to DON in the fines component sample was 0.359. The coefficient of variation (CV) associated with predicting the DON concentration in a lot with 359 ng/g using a 1.1 kg test sample was 47.0%. Increasing sample size to 4.4 kg reduced the CV to 23%.

scholarly journals Sampling plan in health surveys, city of São Paulo, Brazil, 2015

2018 ◽  
Vol 52 ◽  
pp. 81 ◽  
Author(s):  
Maria Cecilia Goi Porto Alves ◽  
Maria Mercedes Loureiro Escuder ◽  
Moises Goldbaum ◽  
Marilisa Berti de Azevedo Barros ◽  
Regina Mara Fisberg
Keyword(s):  
Sample Size ◽  
Health Survey ◽  
Coefficient Of Variation ◽  
Sample Size Calculation ◽  
Sao Paulo ◽  
Sampling Plan ◽  
São Paulo ◽  
Design Effect ◽  
Sampling Procedures ◽  
The City

OBJECTIVE: To evaluate the sampling plan of the Health Survey of the City of São Paulo (ISA-Capital 2015) regarding the accuracy of estimates and the conformation of domains of study by the Health Coordinations of the city of São Paulo, Brazil. METHODS: We have described the population, domains of study, and sampling procedures, including stratification, calculation of sample size, and random selection of sample units, of the Health Survey of the City of São Paulo, 2015. The estimates of proportions were analyzed in relation to precision using the coefficient of variation and the design effect. We considered suitable the coefficients below 30% at the regional level and 20% at the city level and the estimates of the design effect below 1.5. We considered suitable the strategy of establishing the Health Coordinations as domains after verifying that, within the coordinations, the estimates of proportions for the age and sex groups had the minimum acceptable precision. The estimated parameters were related to the subjects of use of services, morbidity, and self-assessment of health. RESULTS: A total of 150 census tracts were randomly selected, 30 in each Health Coordination, 5,469 households were randomly selected and visited, and 4,043 interviews were conducted. Of the 115 estimates made for the domains of study, 97.4% presented coefficients of variation below 30%, and 82.6% were below 20%. Of the 24 estimates made for the total of the city, 23 presented coefficient of variation below 20%. More than two-thirds of the estimates of the design effect were below 1.5, which was estimated in the sample size calculation, and the design effect was below 2.0 for 88%. CONCLUSIONS: The ISA-Capital 2015 sample generated estimates at the predicted levels of precision at both the city and regional levels. The decision to establish the regional health coordinations of the city of São Paulo as domains of study was adequate.

Sample Size as a Function of Coefficient of Variation and Ratio of Means

1993 ◽  
Vol 47 (3) ◽  
pp. 165-167 ◽  
Author(s):  
Gerald Van Belle ◽  
Donald C. Martin
Keyword(s):  
Sample Size ◽  
Coefficient Of Variation
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