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Related Concept Videos

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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Estimating Population Mean with Known Standard Deviation01:16

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.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
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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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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 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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Lung Capacity01:47

Lung Capacity

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The air in the lungs is measured in volumes and capacities. Lung volume measures reflect the amount of air taken in, released, or left over after a lung function, like a single inhalation. Lung capacity measures are sums of two or more lung volume measures.
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Topographical Estimation of Visual Population Receptive Fields by fMRI
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Estimation of Lung Volume in Normal Population Using MSCT.

Y J Yang1, M Shang2, Y W Li2

  • 1School of Forensic Medicine, Henan University of Science and Technology, Luoyang 471023, Henan Province, China.

Fa Yi Xue Za Zhi
|November 24, 2018
PubMed
Summary

This study developed new equations using multi-slice spiral CT and Pulmo software to accurately estimate normal lung volume. These models can help assess lung compression by providing a baseline for comparison.

Keywords:
forensic medicinelungmodelstomography, spiral computed

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Area of Science:

  • Radiology and Medical Imaging
  • Pulmonary Medicine
  • Biomedical Engineering

Background:

  • Accurate estimation of normal lung volume is crucial for diagnosing and managing various respiratory conditions.
  • Existing methods for lung volume measurement may have limitations in accessibility or precision.

Purpose of the Study:

  • To establish reliable curvilinear equations for estimating normal lung volume in a general population.
  • To validate these equations using multi-slice spiral CT and specialized software.

Main Methods:

  • 45 healthy individuals underwent lung CT scans using a 16-slice spiral CT scanner.
  • Pulmo software and workstation instruments were used to measure lung dimensions and volume.
  • Regression analysis was performed to develop and select the optimal predictive models.

Main Results:

  • The developed curvilinear equations demonstrated a high goodness of fit (R² values up to 0.981) for estimating bilateral lung volumes.
  • Models incorporating the product of vertical diameter and transverse diameter at the diaphragmatic dome showed strong correlations (R² 0.977 and 0.972).
  • Retrospective testing confirmed no significant difference between estimated and software-measured lung volumes.

Conclusions:

  • The established curvilinear equations provide a valid method for estimating pre-injury normal lung volume.
  • This estimation serves as a valuable reference for quantifying lung compression severity.