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Related Concept Videos

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.
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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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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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Sample Proportion and Population Proportion01:20

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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 probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
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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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bayesPop: Probabilistic Population Projections.

Hana Ševčíková1, Adrian E Raftery2

  • 1Center for Statistics and the Social Sciences, University of Washington, Box 354322, Seattle, WA 98195-4322, USA.

Journal of Statistical Software
|January 13, 2017
PubMed
Summary
This summary is machine-generated.

The bayesPop R package generates probabilistic population projections globally using Bayesian models for fertility and life expectancy. This tool aids in understanding future demographic trends and population dynamics for various countries and regions.

Keywords:
Bayesian hierarchical modelUnited NationsWorld Population Prospectsexpression languagepopulation projectionspopulation pyramid

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Area of Science:

  • Demography
  • Computational Statistics
  • Population Health

Background:

  • Accurate population projections are crucial for policy-making and resource allocation.
  • Existing methods often lack probabilistic outputs, limiting uncertainty assessment.
  • The United Nations utilizes advanced demographic modeling for global population estimates.

Purpose of the Study:

  • To introduce bayesPop, an R package for comprehensive probabilistic population projections.
  • To provide tools for visualizing and analyzing future demographic trends.
  • To enable the calculation of derived population quantities for policy-relevant indicators.

Main Methods:

  • Utilizes Bayesian hierarchical models for probabilistic total fertility and life expectancy projections.
  • Generates samples from the joint posterior predictive distribution of demographic variables.
  • Incorporates an expression language for calculating derived population quantities.

Main Results:

  • bayesPop produces probabilistic projections of age- and sex-specific population counts, fertility, and mortality rates.
  • The package offers graphical summaries like trajectory plots and probabilistic population pyramids.
  • It allows for aggregated projections across regions and trading blocs.

Conclusions:

  • bayesPop offers a robust framework for probabilistic population forecasting.
  • The package supports data-driven policy decisions by quantifying demographic uncertainty.
  • Its methodology is validated by its use in official United Nations population projections.