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Related Concept Videos

Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
Confidence Intervals01:21

Confidence Intervals

An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a sample proportion. However, unlike the point estimate which is a single value, the confidence interval contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A confidence...
Confidence Coefficient01:24

Confidence Coefficient

The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under both the...
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor 't,' or...
Prediction Intervals01:03

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
The...
Time-Series Graph00:54

Time-Series Graph

A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...

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Related Experiment Video

Updated: Jun 21, 2026

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

Network inference with confidence from multivariate time series.

Mark A Kramer1, Uri T Eden, Sydney S Cash

  • 1Department of Mathematics and Statistics, Boston University, Boston, Massachusetts 02215, USA. mak@bu.edu

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|August 8, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for inferring functional connectivity networks from time series data, accurately quantifying uncertainty in the number of network edges. The approach is validated on simulated and real-world epileptic seizure data.

Related Experiment Videos

Last Updated: Jun 21, 2026

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

Area of Science:

  • Network science
  • Time series analysis
  • Statistical inference

Background:

  • Networks are ubiquitous in nature and technology, with dynamics often driven by unknown physical interactions.
  • Inferring network structures from time series data commonly relies on functional connectivity thresholds.
  • Measurement uncertainty necessitates robust methods for error propagation in network inference.

Purpose of the Study:

  • To develop a systematic procedure for inferring functional connectivity networks from multivariate time series data.
  • To provide a quantification of uncertainty, specifically in the number of inferred network edges.
  • To demonstrate the accuracy and robustness of the proposed method.

Main Methods:

  • Utilized a principled and systematic procedure for network inference.
  • Applied a measure of linear coupling to analyze time series data.
  • Incorporated statistical error propagation to account for measurement uncertainty.

Main Results:

  • The procedure successfully infers functional connectivity networks.
  • Quantification of uncertainty in the number of edges was achieved.
  • Demonstrated accuracy and robustness in edge determination and uncertainty reporting.

Conclusions:

  • The developed procedure offers a reliable approach for network inference from time series data.
  • Accurate quantification of uncertainty in network structure is crucial and achievable.
  • The method shows promise for applications in various scientific domains, including neuroscience.