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相关概念视频

Stratified Sampling Method01:16

Stratified Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Sample Proportion and Population Proportion01:20

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Collecting samples or responses from an entire population takes significant time and effort, so a researcher collects responses from only a sample of that population. Suppose a study needs to collect information about a specific mobile application. After sample collection, the researcher analyzes the data and discovers that most individuals in the sample use that specific mobile application. The sample proportion measures the number of individuals in a sample who either use or don't use the...
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Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

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The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
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Weighted Mean00:57

Weighted Mean

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While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
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One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
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When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
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Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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用夏威夷人口普查数据进行单种族人口估计的逐步比例权重算法.

Masako Matsunaga1, Kyle M Ishikawa1, Chathura Siriwardhana1

  • 1Department of Quantitative Health Sciences, John A. Burns School of Medicine, University of Hawai'i, Honolulu, HI.

Hawai'i journal of health & social welfare
|October 30, 2023
PubMed
概括

从美国人口普查数据中估计单种族人口是具有挑战性的,因为多种族的分类. 逐步比例权重算法 (SPWA) 为健康差异研究提供了一个解决方案,特别是在夏威夷.

关键词:
人口普查是指进行人口普查.哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈原住民夏威夷人是夏威夷的原住民.公共卫生 公共卫生算法算法是一种算法.人口估计 人口估计种族群体是一种种族群体.

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科学领域:

  • 人口统计学 人口统计学
  • 公共卫生 公共卫生
  • 社会学 社会学 社会学

背景情况:

  • 健康差异研究依赖于各种种族群体的准确人口流行数据.
  • 由于多种族报告类别,美国人口普查数据在估计单种族人口方面存在挑战.
  • 分类多种族个体至关重要,特别是在像夏威夷这样的多元人口中.

研究的目的:

  • 利用美国人口普查数据开发和描述一种用于估计单种族人口规模的新算法.
  • 解决在人口研究中分类多种族个体的复杂性.
  • 具体适应该方法论的夏威夷独特的种族人口统计,包括部分的夏威夷原住民分类.

主要方法:

  • 开发了逐步比例权重算法 (SPWA) 用于单个种族的人口估计.
  • 将算法应用于美国人口普查数据的主要种族群体及其嵌套的详细种族.
  • 纳入针对夏威夷普遍存在的特定"部分原住民夏威夷人"分类的调整.

主要成果:

  • SPWA成功地估计了主要种族群体的单种族人口.
  • 证明了算法的能力,为夏威夷5个最常见的种族群体提供准确的单一种族估计.
  • 该方法提供了一个强大的方法来分类多种族的个人.

结论:

  • 逐步比例权重算法 (SPWA) 提高了从美国人口普查数据中单个种族人口估计的准确性.
  • 这种方法对于加强健康和健康差异研究至关重要,特别是在像夏威夷这样的人口复杂地区.
  • 准确的多种族个人种族分类对于有效的公共卫生研究至关重要.