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Related Concept Videos

Sample Proportion and Population Proportion01:20

Sample Proportion and Population Proportion

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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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Partial Fractions01:28

Partial Fractions

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A partial fraction is a component of a rational expression represented as the sum of simpler fractions. When a rational function is expressed as a ratio of two polynomials, it can often be decomposed into a sum of fractions whose denominators are simpler polynomials, typically linear or irreducible quadratic factors. This process is called partial fraction decomposition, and it is used to simplify complex expressions for integration, solving equations, or analysis.Partial fraction decomposition...
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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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Margin of Error01:27

Margin of Error

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The margin of error is also called the maximum error of an estimate. The margin of error is the maximum possible or expected difference between the observed sample parameter value and the actual population parameter value. For proportion, it is the maximum difference between the value of sample proportion obtained from the data and the true value of population proportion. As the true value of the population parameter is not known, the margin of error is calculated using the sample statistic.
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Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

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A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
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Confidence Intervals01:21

Confidence Intervals

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An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a sample proportion. However, unlike the point estimate which is a single value, the confidence interval contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A confidence...
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The Optical Fractionator Technique to Estimate Cell Numbers in a Rat Model of Electroconvulsive Therapy
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Attributable fraction estimation from complex sample survey data.

Steven G Heeringa1, Patricia A Berglund1, Brady T West1

  • 1Survey Methodology Program, Institute for Social Research, University of Michigan, Ann Arbor, MI.

Annals of Epidemiology
|December 17, 2014
PubMed
Summary
This summary is machine-generated.

Estimating attributable fraction (AF) from complex survey data is feasible using weighted sum estimators and resampling methods like Jackknife and Bootstrap. These statistical approaches provide stable results for public health research.

Keywords:
Attributable fractionBootstrapCluster samplingComplex sample designJackknife repeated replicationPopulation attributable riskStratified samplingWeighting

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Survey Methodology

Background:

  • Attributable fraction (AF) estimation is crucial for public health research.
  • Complex sample designs are common in epidemiological studies.
  • Accurate AF estimation requires specialized statistical methods for complex survey data.

Purpose of the Study:

  • To review methods for estimating AF from complex sample data.
  • To provide practical guidance on AF estimation and inference.
  • To demonstrate the use of contemporary software tools for complex sample AF analysis.

Main Methods:

  • Literature review of AF estimation methods (1980-2014).
  • Development of a general approach using weighted sum estimators.
  • Application of Jackknife and Bootstrap resampling for variance estimation.
  • Example analysis of risk factors for alcohol dependency.

Main Results:

  • Theoretical foundation for AF estimation from complex samples is established.
  • Weighted sum estimation and resampling methods yield stable and consistent AF estimates.
  • Implementation is feasible using major statistical software packages.

Conclusions:

  • Resampling variance estimation methods show promise for AF estimation in complex samples.
  • Further simulation studies are needed to evaluate these methods across diverse complex designs.
  • Empirical testing is recommended to assess performance in various populations.