Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Statistical Analysis: Overview01:11

Statistical Analysis: Overview

16.5K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
16.5K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

502
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
502
Transient and Steady-state Response01:24

Transient and Steady-state Response

559
In control systems, test signals are essential for evaluating performance under various conditions. The ramp function is effective for systems undergoing gradual changes, while the step function is suitable for assessing systems facing sudden disturbances. For systems subjected to shock inputs, the impulse function is the most appropriate test signal.
These test signals are integral in designing control systems to exhibit two key performance aspects: transient response and steady-state...
559
Statistical Analysis System (SAS)01:14

Statistical Analysis System (SAS)

896
SAS, short for Statistical Analysis System, is a powerful data analysis, management, and visualization tool. Developed by the SAS Institute in the early 1970s, SAS has evolved into a comprehensive software suite used across various industries for statistical analysis, business intelligence, and predictive modeling.
Applications: SAS finds applications in numerous fields, including healthcare for clinical trial analysis, finance for risk assessment, marketing for customer data analysis, and...
896
Variance01:15

Variance

12.2K
The deviations show how spread out the data are about the mean. A positive deviation occurs when the data value exceeds the mean, whereas a negative deviation occurs when the data value is less than the mean. If the deviations are added, the sum is always zero. So one cannot simply add the deviations to get the data spread. By squaring the deviations, the numbers are made positive; thus, their sum will also be positive.
The standard deviation measures the spread in the same units as the data....
12.2K
Statistical Significance01:50

Statistical Significance

21.3K
Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
21.3K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Sebum levels are associated with the relationship between skin properties and microbiota in Japanese women.

Scientific reports·2026
Same author

Real-Time Visualization of Turbulent Micromixing in a T-Mixer: Submillisecond Homogenization Revealed by Chemiluminescence.

Analytical chemistry·2026
Same author

Pyrroloquinoline Quinone Protects Against Light-Induced Retinal Damage in Association with the Suppression of c-Fos Signalling.

International journal of molecular sciences·2026
Same author

Effects of Reinforcement Materials and Mechanical Retention on the Flexural Strength and Deflection of Repaired Denture Base Resin.

Cureus·2025
Same author

Endothelial dysfunction in plaque rupture and plaque erosion.

Heart and vessels·2025
Same author

Three-year clinical outcomes of a fractional flow reserve-guided percutaneous coronary intervention (PCI) strategy: A comparison of nicorandil and ATP.

International journal of cardiology. Heart & vasculature·2025

Related Experiment Video

Updated: Jan 28, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.7K

Response variance prediction using transient statistical energy analysis.

Robin S Langley1, David H Hawes1, Tore Butlin1

  • 1Department of Engineering, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ, United Kingdom.

The Journal of the Acoustical Society of America
|March 3, 2019
PubMed
Summary

This study introduces a novel method for predicting the variance in transient Statistical Energy Analysis (SEA) responses. The approach accounts for system variability, offering a more complete understanding of structural dynamics under time-varying loads.

More Related Videos

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

6.0K
Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding
06:33

Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding

Published on: October 11, 2018

7.2K

Related Experiment Videos

Last Updated: Jan 28, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.7K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

6.0K
Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding
06:33

Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding

Published on: October 11, 2018

7.2K

Area of Science:

  • Mechanical Engineering
  • Structural Dynamics
  • Acoustics

Background:

  • Statistical Energy Analysis (SEA) is a key method for predicting high-frequency structural responses.
  • Existing SEA methods primarily focus on mean energy responses, neglecting response variability.
  • Transient SEA has been limited to predicting only the mean response to time-varying inputs.

Purpose of the Study:

  • To develop a novel method for predicting the variance of transient SEA responses.
  • To extend transient SEA to account for the statistical variation in structural dynamics.

Main Methods:

  • Derived a matrix differential equation for the covariance of subsystem energies.
  • Incorporated system variability terms driven by natural frequency distributions (Gaussian orthogonal ensemble).
  • Validated the method through numerical simulations and experimental tests on coupled plate systems.

Main Results:

  • Successfully derived a method to predict the variance of transient SEA.
  • The method quantifies deviations from the mean response due to system variability.
  • Numerical and experimental results demonstrate the accuracy of the proposed variance prediction.

Conclusions:

  • The developed method provides a significant advancement in transient SEA by including response variance.
  • This enables a more comprehensive prediction of structural dynamic behavior under transient loads.
  • The study discusses the limitations and applicability of the variance prediction method.