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Population proportion
About: Population proportion is a research topic. Over the lifetime, 247 publications have been published within this topic receiving 4099 citations.
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TL;DR: A series of six articles as discussed by the authors gives readers an understanding of the concepts of inferential statistics, as well as the specific tools for calculating confidence intervals and tests of statistical significance for samples of data.
Abstract: Healthcare quality professionals need to understand and use inferential statistics to interpret sample data from their organizations. Since in quality improvement and healthcare research studies, all the data from a population often are not available, investigators take samples and make inferences about that population using inferential statistics. This series of six articles will give readers an understanding of the concepts of inferential statistics, as well as the specific tools for calculating confidence intervals and tests of statistical significance for samples of data. The statistical principles are equally applicable to quality improvement and healthcare research studies. This article, Part 4, starts with a review of the information contained in Parts 1, 2, and 3, which appeared in the July/August 2003 issue of the Journal for Healthcare Quality. This article describes t distributions and how these are used to calculate confidence intervals for estimating a population mean based on a sample mean of a continuous variable. Part 4 concludes with a discussion of standard error, margin of error, and confidence intervals for estimating a population proportion based on a sample proportion from a binomial variable.
2 citations
30 Nov 2013
TL;DR: In this article, a Bayesian nonignorable selection model was used to accommodate the selection mechanism and compared four possible estimators of the finite population proportions based on data analysis as well as Monte Carlo simulation.
Abstract: In this paper, we study Bayesian estimation for the finite population proportion in binary data under selection bias. We use a Bayesian nonignorable selection model to accommodate the selection mechanism. We compare four possible estimators of the finite population proportions based on data analysis as well as Monte Carlo simulation. It turns out that nonignorable selection model might be useful for weekly biased samples.
2 citations
Journal Article•
TL;DR: The factors affecting the interprovincial transmission and development of coronavirus disease 2019 in China are explored with a view to providing recommendations for the formulation of preventive and control measures according to the actual conditions in different regions during the outbreak of the severe infectious disease.
Abstract: Objective: To explore the factors affecting the interprovincial transmission and development of coronavirus disease 2019 (COVID-19) in China, with a view to providing recommendations for the formulation of preventive and control measures according to the actual conditions in different regions during the outbreak of the severe infectious disease Methods: We collected the total number of confirmed cases of COVID-19 in 30 provinces and cities in China by the end of 24:00 February 25, 2020 Then we also collected the distance from each region to Hubei province, the proportion of population moving out from Wuhan city from January 1 to January 23, population density, urban population, traffic passenger volume, passenger turnover volume and other relevant data of each region The cumulative confirmed cases including the most of imported cases by the end of 24:00 January 29, 2020 were taken as the first-stage cases cluster, and the cumulative newly confirmed cases including the most of secondary cases from 0:00 January 30 to 24:00 February 25, 2020 were taken as the second-stage cases cluster Pearson bivariate correlation and linear fitting regression method were adopted to analyze the effects of population migration, transportation, economy and other factors on the transmission and development of COVID-19 in different regions In the linear fitting regression, the multi-factor optimal subset model was used to screen the factors most closely related to COVID-19 Results: The distance from each region to Hubei province was negatively correlated with the first-stage cases cluster with the most of imported cases and the second-stage cases cluster with the most of secondary cases(t=-3 654, t=-3 679, both P2 760, all P<0 05) GDP and the proportion of population moving out from Wuhan were most closely related to the first-stage cases cluster with the most of imported cases (t=4 173, t=7 851, all P<0 05) The first-stage cases cluster, the proportion of population moving out from Wuhan, and urban population were most closely related to the second-stage cases cluster with the most of secondary cases (t=4 734, t=3 491, t=2 855, all P<0 05) Results: GDP and the proportion of population moving out from Wuhan city had the greatest impact on the stage with the most of imported cases The imported cases, the proportion of population moving out from Wuhan and the urban population had the greatest impact on the stage with the most of secondary cases In the early stage of epidemic outbreak with the most of imported cases,we should consider strengthening the prevention and control of the epidemic in areas with high level of GDP and high proportion of population moving out from the epidemic area The flow of population should be restricted more strictly as soon as possible in order to effectively curb the outbreak of the epidemic In the later-stage of epidemic with the most of secondary cases, regionalized control policies should be formulated mainly according to the indicators of imported cases, the population proportion fromtheepidemic area, and the urban population Finally, the contact of population should be restricted reasonably to prevent further development of the epidemic
2 citations
TL;DR: This paper has corrected a major mistake in the research paper of Singh and Mathur and proposed the corresponding corrected estimator of sensitive population proportion, and obtained the variance of the proposed estimator.
Abstract: In this paper, we have pointed out a major mistake in the research paper of Singh and Mathur [(2004). Unknown repeated trials in the unrelated question randomized response model, Biometrical Journal, 46:375-378]. We have corrected this mistake and proposed the corresponding corrected estimator of sensitive population proportion. Furthermore, we have obtained the variance of our proposed estimator. Likewise, Singh and Mathur, we have also compared the variance of our proposed estimator with that of the Greenberg et al.'s estimator theoretically as well as numerically.
2 citations
TL;DR: This article proposed a randomized response (RR) model that is more efficient than the one envisaged by Gjestvang and Singh ([2006], A new randomized response model, Journal of Royal Statistcal Socity, B, (3), 523-530).
Abstract: This paper addresses the problem of estimating the population proportion π of a sensitive group. We have suggested a randomized response (RR) model that is more efficient than the one envisaged by Gjestvang and Singh ([2006), A new randomized response model, Journal of Royal Statistcal Socity, B, (3), 523–530].
2 citations