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相关概念视频

Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

7.8K
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...
7.8K
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

8.4K
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 +...
8.4K
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

3.0K
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...
3.0K
Sample Proportion and Population Proportion01:20

Sample Proportion and Population Proportion

5.3K
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...
5.3K
What are Estimates?01:06

What are Estimates?

5.1K
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...
5.1K
Random Sampling Method01:09

Random Sampling Method

11.2K
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...
11.2K

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相关实验视频

Updated: Jul 11, 2025

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems
07:41

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems

Published on: July 30, 2019

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用模型平均方法对志愿者网络调查样本的估计.

Zhan Liu1, Junbo Zheng1, Chaofeng Tu1

  • 1Hubei Key Laboratory of Applied Mathematics, School of Mathematics and Statistics, Hubei University, Wuhan, People's Republic of China.

Journal of applied statistics
|November 16, 2023
PubMed
概括

这项研究引入了一种新型的模型平均方法,以从网络调查中获得更准确的人口估计. 将后勤回归和增强模型结合起来,可以改善志愿者样本的倾向性得分估计.

科学领域:

  • 统计 统计 统计 统计
  • 调查方法 调查方法
  • 计算统计学 计算统计学

背景情况:

  • 志愿者网络调查样本通常需要进行统计调整以准确估计人口.
  • 倾向性评分方法被广泛使用,但对模型选择敏感.
  • 由于不同的倾向得分模型,现有的方法产生了不同的人口估计.

研究的目的:

  • 为志愿者网络调查样本开发更准确的人口估计方法.
  • 通过组合参数和非参数模型来改进倾向得分估计.
  • 确定拟议方法的理论特性和实际实施.

主要方法:

  • 提出了一种对倾向性得分的模型平均估计方法.
  • 使用了参数逻辑回归模型和非参数通用增强模型的估计值.
  • 建立了拟议估计器的一致性和异常正常性.
  • 开发了一个用于实现的计算算法.

主要成果:

  • 拟议的模型平均方法在模拟研究中显示出更好的准确性.
  • 该方法提供了一种结合不同倾向得分模型的可靠方法.
  • 确定了估计器的理论性质 (一致性,非对称的正常性).
关键词:
志愿者网络调查样本调查一般化增强型的模型.逻辑回归模型的逻辑回归模型.模型-平均方法的方法.倾向性得分是指倾向性得分.

更多相关视频

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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Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates
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Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates

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相关实验视频

Last Updated: Jul 11, 2025

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems
07:41

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems

Published on: July 30, 2019

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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

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

Published on: July 3, 2020

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Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates
08:56

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates

Published on: January 13, 2023

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结论:

  • 模型平均化倾向得分估计为志愿者网络调查提供了更可靠的方法.
  • 与单一模型方法相比,拟议的方法提高了人口估计的准确性.
  • 该方法通过模拟和现实世界调查数据集 (NSAS) 得到验证.