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Distributions to Estimate Population Parameter

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...

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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Estimating Population Mean with Unknown Standard Deviation

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 Mean with Known Standard Deviation

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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.
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Stratified Sampling Method

Stratified Sampling Method

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Related Experiment Video

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Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Published on: February 2, 2019

A less field-intensive robust design for estimating demographic parameters with mark-resight data.

Brett T McClintock¹, Gary C White

¹Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, Colorado 80523, USA. bmcclintock@usgs.gov

|March 28, 2009

Summary

A new mark-resight model offers a less invasive and cost-effective alternative to traditional mark-recapture for estimating animal population abundance and survival. This method is ideal for long-term monitoring, especially for endangered species.

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Area of Science:

Ecology
Wildlife Biology
Conservation Science

Background:

Mark-recapture methods are widely used for estimating animal population parameters but are costly and disruptive.
Existing mark-resight models primarily focus on abundance estimation, limiting their application in demographic studies.
There is a need for less invasive and more economical alternatives for long-term wildlife monitoring.

Purpose of the Study:

To introduce a novel mark-resight model for simultaneously estimating abundance, apparent survival, and state transition probabilities.
To provide a viable and less disruptive alternative to traditional mark-recapture techniques.
To assess the applicability of this new model for monitoring endangered populations.

Main Methods:

Developed a mark-resight model analogous to established mark-recapture models.
The model estimates abundance, apparent survival, and transition probabilities between observable and unobservable states.
Implemented the model using statistical software and the freeware package Program MARK.
Applied the model to data from mainland New Zealand Robins (Petroica australis).

Main Results:

The developed mark-resight model successfully estimated abundance, apparent survival, and transition probabilities.
The methodology proved to be a reliable alternative to mark-recapture for population monitoring.
The New Zealand Robin data demonstrated the model's effectiveness in a real-world scenario.

Conclusions:

The new mark-resight model provides a cost-effective and less invasive approach for ecological studies.
This methodology is particularly valuable for long-term monitoring of endangered species where disturbance is a concern.
The model offers a robust alternative for comprehensive demographic parameter estimation in wildlife research.