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Related Concept Videos

Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

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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.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
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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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Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

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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...
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Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

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Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
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Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

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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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Estimating county-level vaccination coverage using small area estimation with the National Immunization Survey-Child.

Zachary H Seeskin1, Nadarajasundaram Ganesh1, Poulami Maitra2

  • 1NORC at the University of Chicago, 55 E. Monroe Street, 31(st) Floor, Chicago, IL 60603, USA.

Vaccine
|December 24, 2023
PubMed
Summary

Small area estimation methods provide county-level vaccination coverage estimates for children. These methods utilize National Immunization Survey-Child data and demographic predictors to identify areas needing intervention.

Keywords:
Fay-Herriot modelJames-Stein estimationRandom digit dialing

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Area of Science:

  • Public Health
  • Biostatistics
  • Epidemiology

Background:

  • The National Immunization Survey-Child (NIS-Child) offers vaccination coverage estimates for children aged 19-35 months at national and state levels.
  • There is a critical need for granular, county-level vaccination coverage data to support local public health planning and targeted interventions.
  • Identifying geographic areas with potentially low vaccination coverage is essential for effective public health strategies.

Approach:

  • This study employed small area estimation methods using 2008-2018 NIS-Child data.
  • An empirical best linear unbiased prediction (EBLUP) approach was used, combining direct survey estimates with model-based predictions.
  • County-level health and demographic characteristics were utilized as predictors in the statistical models.

Key Points:

  • The methods generated county-level vaccination coverage estimates for children born between 2007-2011 and 2012-2016.
  • Analysis identified common predictors for small area models, many of which relate to known barriers to vaccination.
  • This approach allows for more precise identification of areas with suboptimal vaccination rates.

Conclusions:

  • Small area estimation provides valuable county-level vaccination coverage data, enhancing public health surveillance.
  • The findings support the use of demographic and health predictors to model vaccination coverage at a granular level.
  • This methodology can aid local authorities in planning interventions and improving childhood immunization rates in underserved areas.