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Related Concept Videos

Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

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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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Distributions to Estimate Population Parameter01:26

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

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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.
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Population Growth00:57

Population Growth

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Population size is dynamic, increasing with birth rates and immigration, and decreasing with death rates and emigration. In ideal conditions with unlimited resources, populations can increase exponentially, which plots as a J-shaped growth rate curve of population size against time. This type of curve is characteristic of newly-introduced invasive species, or populations that have suffered catastrophic declines and are rebounding.
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Applications of Normal Distribution

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The normal distribution is a useful statistical tool. One of its practical applications is determining the door height after considering the normal distribution of heights of persons, such that many can pass through it easily without striking their heads. The normal distribution can also determine the probability of a person having a height less than a specific height.
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Spatial Human Development Index in China: Measurement and Interpretation Based on Bayesian Estimation.

Xiang Luo1, Jingjing Qin1, Qing Wan2

  • 1College of Public Administration, Central China Normal University, Wuhan 430079, China.

International Journal of Environmental Research and Public Health
|January 8, 2023
PubMed
Summary

This study reveals that high population density and regional integration enhance China's Human Development Index (HDI). Incorporating spatial factors improves HDI measurement, highlighting the importance of spatial analysis for sustainable economic growth.

Keywords:
Bayesian estimationHDIspatial spillover

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Area of Science:

  • Economics
  • Geography
  • Sociology

Background:

  • China's economic growth is increasingly driven by service-industry-dominated urban agglomerations.
  • Traditional Human Development Index (HDI) measurements often overlook spatial factors and their varying influence.
  • Understanding spatial dynamics is crucial for accurately assessing human development in China.

Purpose of the Study:

  • To investigate the impact of spatial factors on China's Human Development Index (HDI).
  • To develop a more accurate HDI measurement by incorporating spatial considerations.
  • To analyze the influence of population density and regional integration on HDI.

Main Methods:

  • Utilized Bayesian estimation and a spatial hierarchical factor model.
  • Applied Sen Capability Approach theory to framework the analysis.
  • Measured HDI using panel data from 2000 to 2018, including spatial factors.

Main Results:

  • Provinces with high population density and regional integration show higher HDI rankings and lower uncertainty, linked to improved education weights.
  • A significant spatial spillover effect on HDI was observed, with spatial associations strengthening annually.
  • Robust testing using nighttime lighting data confirmed a positive relationship between spatial correlation and HDI ranking.

Conclusions:

  • Spatial factors, particularly population density and regional integration, are critical determinants of HDI in China.
  • Policy recommendations include facilitating cross-regional population mobility and optimizing public expenditure.
  • The study underscores the need for spatial analysis in measuring and enhancing human development.