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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.
To choose a stratified sample, divide the population into groups called strata and then take a...
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Cluster Sampling Method01:20

Cluster Sampling Method

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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...
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Sampling Plans01:23

Sampling Plans

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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
169
Randomized Experiments01:13

Randomized Experiments

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
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What are Estimates?01:06

What are Estimates?

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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...
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Random Sampling Method01:09

Random 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. Data are the result of sampling from a 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. Among the various sampling methods used by...
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相关实验视频

Updated: Jun 12, 2025

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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在分层随机抽样中探索混合物估计器.

Kanwal Iqbal1,2, Syed Muhammad Muslim Raza1,3, Tahir Mahmood4

  • 1Department of Economics and Statistics, Dr Hasan Murad School of Management (HSM), University of Management and Technology, Lahore, Pakistan.

PloS one
|September 17, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新的混合估计器,用于估计人口平均值,使用分层采样下的辅助变量. 拟议的方法提高了各种分布和样本大小的精度,超过了现有的估计器.

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科学领域:

  • 统计 统计 统计 统计
  • 调查方法 调查方法
  • 数据科学数据科学数据科学

背景情况:

  • 现代传感器技术能够产生大量的数据,需要高效的统计方法.
  • 辅助变量 (定量和质量) 经常与研究变量一起记录,以获得成本效益.
  • 混合估计器对于利用辅助信息来估计人口平均值是有价值的.

研究的目的:

  • 为分层采样提出一种混合物估计器的一般化家族.
  • 为了提高人口平均值估计的精度,使用辅助变量.
  • 分析不同样本大小和分布的拟议估计者的行为.

主要方法:

  • 在分层采样下开发通用混合物估计器.
  • 对对称和不对称分布的估计器效率的研究.
  • 对不同样本大小的正常分布的估计器趋同的分析.

主要成果:

  • 拟议的通用混合物估计器与现有方法相比,显示出更高的精度.
  • 估计器的性能在正常,均,韦布尔和马分布中得到验证.
  • 估计器遵循Cauchy分布的样本大小<35,然后趋于正常.

结论:

  • 拟议的通用混合估计器在估计人口平均值方面提供了显著的改进.
  • 这些发现得到了健康和金融领域的现实应用的支持.
  • 该研究强调了考虑样本大小和数据分布在估计器选择中的重要性.