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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

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

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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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Functions of Connective Tissues01:17

Functions of Connective Tissues

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Connective tissues perform a broad range of functions in the body. Their primary function is to connect and link different tissues in the body and act as packaging material between tissues. The areolar tissue, a connective tissue prototype, commonly cements various tissue types in diverse body organs. In contrast, adipose tissue cushions internal organs while insulating the body from heat loss.
Hard connective tissues, such as bones and cartilage, provide structure and support to the body.
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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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Semiparametric Estimation of Task-Based Dynamic Functional Connectivity on the Population Level.

Maria A Kudela1, Mario Dzemidzic2,3, Brandon G Oberlin2,4

  • 1Safety and Observational Statistics, Takeda R&D Data Science Institute, Takeda Pharmaceuticals, Cambridge, MA, United States.

Frontiers in Neuroscience
|July 12, 2019
PubMed
Summary

This study introduces a novel dynamic functional connectivity (dFC) method to analyze brain activity during fMRI tasks. The approach reveals new insights into brain networks, particularly during flavor perception, uncovering associations missed by traditional static analyses.

Keywords:
addictiondynamic functional connectivityfunctional MRIgustatory tasksemiparametric mixed modelsstatistical methods

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Area of Science:

  • Neuroimaging
  • Cognitive Neuroscience
  • Statistical Modeling

Background:

  • Dynamic functional connectivity (dFC) measures time-varying brain region associations using fMRI.
  • Traditional methods often use simple sliding window correlations, which may lack robustness.
  • Understanding task-based brain network dynamics is crucial for cognitive neuroscience.

Purpose of the Study:

  • To apply a novel bootstrap-based technique for robust estimation of subject-level dFC and confidence intervals.
  • To combine subject-level dFC estimates using semiparametric mixed models for group-level analysis.
  • To identify condition-specific dFC patterns and differences between flavors (beer vs. Gatorade) in a task-based fMRI study.

Main Methods:

  • Utilized a bootstrap-based technique to estimate dynamic functional connectivity (dFC) and confidence intervals.
  • Employed semiparametric mixed models to integrate subject-level dFC data for group-level analysis.
  • Quantified dFC using the proportion of time with significant positive or negative associations, analyzing task-based fMRI data from 24 subjects.

Main Results:

  • The novel dFC method revealed biologically meaningful brain organization, mirroring resting-state networks (RSNs).
  • Beer flavor consumption potentiated associations between reward-related regions, including the ventral striatum (VST) and ventral anterior insular cortex (vAIC).
  • The approach identified numerous significant dFC associations not detectable by traditional static functional connectivity (FC) analysis.

Conclusions:

  • The developed data-driven dFC methodology provides robust group-level and individual-level connectivity estimates for task-based fMRI.
  • This novel approach enhances the understanding of brain network dynamics during specific tasks and conditions.
  • The findings highlight the potential of advanced dFC methods to uncover complex neural interactions beyond static FC analysis.