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相关概念视频

Stratified Sampling Method01:16

Stratified Sampling Method

11.6K
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.
To choose a stratified sample, divide the population into groups called strata and then take a...
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Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

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To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
8.2K
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

7.5K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
7.5K
Cluster Sampling Method01:20

Cluster Sampling Method

11.5K
Appropriate sampling methods ensure 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.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
11.5K
What are Estimates?01:06

What are Estimates?

4.9K
It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such...
4.9K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

25
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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相关实验视频

Updated: May 7, 2025

Sampling Soils in a Heterogeneous Research Plot
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Sampling Soils in a Heterogeneous Research Plot

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优化人口平均值估计在分层采样使用线性成本:一个模拟研究研究.

Poonam Singh1, Prayas Sharma2, Rajesh Singh1

  • 1Department of Statistics, Banaras Hindu University, Varanasi, India.

Heliyon
|December 30, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了针对分层采样的新的概括指数估计器,提高了效率并降低了调查成本. 与现有技术相比,这些新的方法在现实应用中提供了更高的性能.

关键词:
辅助信息 辅助信息 辅助信息费用 费用 费用 费用 费用 费用 费用整数编程问题 整数编程问题拉格朗奇的乘数是什么平均平方误差 (MSE) 是指优化优化 优化优化相对效率的百分比 (PRE)模拟模拟是为了模拟.分层采样分层采样

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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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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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相关实验视频

Last Updated: May 7, 2025

Sampling Soils in a Heterogeneous Research Plot
07:11

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

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Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

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科学领域:

  • 统计 统计 统计 统计
  • 调查方法 调查方法
  • 应用数学 应用数学 应用数学

背景情况:

  • 提高采样效率是一个持续的挑战.
  • 同时提高估计器效率和优化调查成本在医学,农业和运输等领域至关重要.

研究的目的:

  • 在分层抽样中开发一个对人口平均值估计的概括指数估计器家族.
  • 在固定预算内,使用整数编程和拉格朗奇乘法来优化调查成本.

主要方法:

  • 对于拟议的估计器的平均平方误差 (MSE) 的推导.
  • 制定一个优化问题,以在成本约束下完善估计器性能.
  • 利用整数编程和拉格朗奇乘法来优化成本.

主要成果:

  • 拟议的概括指数估计器显著优于现有的替代方案.
  • 理论和经验评估证实了新估计者的优势.
  • 通过真实世界的数据集验证,证明了实际相关性和理论稳定性.

结论:

  • 开发的估计器为分层采样提供了强大而高效的解决方案.
  • 该方法有效地平衡了估计器性能与调查成本优化.
  • 结果在各种数据收集领域具有广泛的适用性.