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Related Concept Videos

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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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...
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A complete procedure for testing a claim about a population proportion is provided here.
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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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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.
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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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Probabilistic County-Level Population Projections.

Crystal Cy Yu1, Hana Ševčíková2, Adrian E Raftery3

  • 1Department of Sociology, University of Washington, Seattle, WA, USA.

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|May 22, 2023
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Summary
This summary is machine-generated.

This study introduces a new Bayesian method for subnational population projections, improving accuracy and uncertainty assessment for local population forecasts. The approach accounts for migration and special populations, offering narrower forecast intervals than traditional methods.

Keywords:
Bayesian modelCohort-component methodProbabilistic population projectionsSubnational projectionsUncertainty

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Area of Science:

  • Demography
  • Statistical Modeling
  • Population Studies

Background:

  • Traditional population projections often lack uncertainty assessment.
  • National probabilistic methods (e.g., UN) are not directly applicable to subnational levels due to unique data characteristics.
  • Subnational projections require accounting for internal migration and specific populations (e.g., college students).

Purpose of the Study:

  • To develop a novel Bayesian method for subnational population projections.
  • To incorporate migration dynamics and special populations into subnational forecasts.
  • To assess the accuracy and calibration of the proposed method against existing deterministic projections.

Main Methods:

  • A modified Bayesian approach building upon the United Nations' national probabilistic method.
  • Application to Washington State counties, including migration and college populations.
  • Comparison with deterministic projections and out-of-sample validation.

Main Results:

  • The proposed Bayesian method produces accurate and well-calibrated subnational population forecasts.
  • Forecast intervals generated by the new method are generally narrower than traditional growth-based intervals.
  • Improved uncertainty quantification for population predictions at the county level.

Conclusions:

  • The developed Bayesian method offers a robust framework for subnational probabilistic population projections.
  • This approach enhances the reliability of local population forecasts by quantifying uncertainty.
  • The method provides valuable insights for regional planning and policy-making.