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Related Concept Videos

Probability Histograms01:17

Probability Histograms

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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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Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
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 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
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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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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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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
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Spatial auto-correlation and auto-regressive models estimation from sample survey data.

Biometrical journal. Biometrische Zeitschrift·2020
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A spatially balanced design with probability function proportional to the within sample distance.

Roberto Benedetti1, Federica Piersimoni2

  • 1Department of Economic Studies (DEc), "G. d'Annunzio" University, Viale Pindaro 42, Pescara, IT-65127, Italy.

Biometrical Journal. Biometrische Zeitschrift
|May 17, 2017
PubMed
Summary

A novel sampling design using within-sample distance improves spatial balance in environmental surveys. This method enhances sample distribution, reducing sampling error compared to traditional techniques like Generalized Random Tessellation Stratified (GRTS).

Keywords:
Correlated Poisson samplingGRTS designMCMCPivotal methodSpatial stratification

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Area of Science:

  • Spatial statistics
  • Survey methodology
  • Environmental science

Background:

  • Georeferenced data are common in biological, agricultural, and environmental surveys.
  • Effective sampling requires utilizing the spatial distribution of the population.
  • Spatially balanced samples ensure uniform coverage across all dimensions.

Purpose of the Study:

  • To introduce a new probability sampling design for geo-referenced populations.
  • To evaluate the spatial balance and efficiency of the proposed design.
  • To compare the new method against established spatial sampling techniques.

Main Methods:

  • Utilized within-sample distance as a summary index for spatial distribution.
  • Compared the proposed design with Generalized Random Tessellation Stratified (GRTS), Spatially Correlated Poisson Sampling (SCPS), CUBE, and Local Pivotal Method (LPM).
  • Conducted experiments on real and simulated datasets.

Main Results:

  • The within-sample distance design achieved superior spatial balance.
  • The proposed method resulted in lower sampling error than compared methods.
  • The design demonstrated flexibility with stratification and coordination.

Conclusions:

  • The within-sample distance method offers a more effective approach to spatial sampling.
  • This design is suitable for large populations and high sampling rates.
  • It provides a valuable alternative for environmental and agricultural surveys.