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Related Concept Videos

Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

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 + error bound)
The...
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Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

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 Guinness...
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

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...
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

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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Watershed Planning within a Quantitative Scenario Analysis Framework
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Published on: July 24, 2016

Can knowledge improve population forecasts at subcounty levels?

Guangqing Chi1

  • 1Department of Sociology and Social Science Research Center, Mississippi State University, Mississippi State, MS 39762, USA. gchi@soc.msstate.edu

Demography
|February 10, 2011
PubMed
Summary
This summary is machine-generated.

Knowledge-based regression models for subcounty population forecasting did not outperform traditional extrapolation methods. Temporal instability and scale effects at finer geographic levels limited regression accuracy, despite producing more precise projections.

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Area of Science:

  • Urban and Regional Planning
  • Demography
  • Geographic Information Systems

Background:

  • Accurate subcounty population forecasts are crucial for urban and regional planning.
  • Existing extrapolation methods are insufficient for capturing complex interactions like land use and traffic flow.

Purpose of the Study:

  • To evaluate the effectiveness of knowledge-based regression models for subcounty population forecasting.
  • To compare regression models with traditional extrapolation techniques for demographic forecasting.

Main Methods:

  • Applied four regression models incorporating demographic, socioeconomic, transportation, and land development factors.
  • Examined population change from 1970 and forecasted year 2000 population at the minor civil division level in Wisconsin.
  • Assessed temporal instability and scale effects on model performance.

Main Results:

  • Regression methods produced more precise population projections but were often more biased than extrapolation.
  • Temporal instability in regression coefficients was significant across estimation and projection periods.
  • Finer geographic scales amplified the instability and scale effect issues for regression models.

Conclusions:

  • Knowledge-based regression is not superior to extrapolation for subcounty population forecasting.
  • Temporal instability and scale effects hinder the performance of regression models at subcounty levels.
  • Further research is needed to refine regression techniques for localized demographic predictions.