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相关概念视频

Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.0K
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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What are Estimates?01:06

What are Estimates?

5.0K
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...
5.0K
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 +...
8.3K
Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

7.2K
A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...
7.2K
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
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

7.6K
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...
7.6K

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相关实验视频

Updated: Jun 10, 2025

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

Published on: January 8, 2020

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使用公共数据来改进人口估计在一致的边界内.

John R Logan1, Wenquan Zhang2, Zengwang Xu3

  • 1Brown University.

The Professional geographer : the journal of the Association of American Geographers
|October 14, 2024
PubMed
概括
此摘要是机器生成的。

这项研究比较了邻里数据方法来跟踪随时间的变化. 使用小面积数据的基于特征 (TB) 方法在详细的人口普查数据可用时,比标准方法提高了准确性.

关键词:
邻里地区的变化.人口普查片段的人口普查片段.插值的插值是指一个插值.纵向数据 纵向数据 纵向数据

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相关实验视频

Last Updated: Jun 10, 2025

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

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科学领域:

  • 人口统计学 人口统计学
  • 城市研究 城市研究
  • 地理信息系统 地理信息系统

背景情况:

  • 由于地理边界的变化,邻里变化研究需要互插数据.
  • 标准的插值方法假定人口分布均,引入不准确性.
  • 现有的方法在数据异质性和不同空间细粒度方面扎.

研究的目的:

  • 为了评估标准纵向流域数据库 (LTDB) 估计与基于特征 (TB) 方法对邻近特征的准确性.
  • 评估数据细节性和来源 (全计数与样本) 对估计准确性的影响.
  • 确定基于特征的方法优于标准方法的条件.

主要方法:

  • 通过使用2010年LTDB (标准) 和TB方法的边界,比较了2000年的社区特征.
  • 利用小面积数据用于TB方法,以考虑空间异质性.
  • 经过验证的估计与机密的,区块级原始人口普查数据相比.

主要成果:

  • 基于特征的 (TB) 估计显著超过了区块层面 (例如种族,年龄,住房) 可用的变量LTDB估计.
  • 当小面积数据具有采样可变性或空间细节较少时,TB方法的有效性会下降.
  • 标准的LTDB方法存在局限性,因为它们假定人口分布均.

结论:

  • 基于特征的方法在高分辨率,全数普查数据可访问时,为邻里变化估计提供了更高的准确性.
  • 先进方法的有效性取决于可用的小面积数据的质量和细节性.
  • 未来的研究应该专注于精炼方法,用于有限或以样本为基础的小区域数据的区域.