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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

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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

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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

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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

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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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One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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The Use of Chemostats in Microbial Systems Biology
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Estimation methods for heterogeneous cell population models in systems biology.

Steffen Waldherr1

  • 1Department of Chemical Engineering, KU Leuven, Leuven, Belgium steffen.waldherr@kuleuven.be.

Journal of the Royal Society, Interface
|November 2, 2018
PubMed
Summary

This review covers estimation methods for cell population models, focusing on population balance equations. It discusses experimental data, modeling frameworks, and computational techniques for analyzing cellular heterogeneity.

Keywords:
cell ensemble modelsdensity functionsparameter identificationpopulation balance equationsstate estimationstructured population models

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Biophysics

Background:

  • Cellular heterogeneity is a fundamental aspect of biological systems.
  • Diverse experimental techniques and computational models exist to study cell populations.
  • Parameter and state estimation are crucial for refining computational models with measurement data.

Purpose of the Study:

  • To provide a comprehensive overview of current estimation methods for heterogeneous cell population data and models.
  • To focus on population balance equation models while also addressing stochastic and individual-based models.
  • To guide researchers in selecting and applying appropriate estimation techniques.

Main Methods:

  • Discussion of experimental approaches and data types for cellular heterogeneity.
  • Description of computational modeling frameworks for heterogeneous populations.
  • Analysis of observability and identifiability properties for estimation problems.
  • Overview of computational methods for solving parameter and state estimation problems.

Main Results:

  • Key estimation challenges and solutions for population balance models are detailed.
  • Insights into stochastic and individual-based modeling estimation are provided.
  • The review synthesizes current knowledge on computational methods for biological system modeling.

Conclusions:

  • Effective estimation methods are vital for accurate modeling of cellular heterogeneity.
  • Understanding model properties like observability and identifiability is critical.
  • This review serves as a valuable resource for computational biologists and systems scientists.