Jove
Visualize
Contact Us

Related Concept Videos

Wave Parameters01:10

Wave Parameters

9.7K
The simplest mechanical waves are associated with simple harmonic motion and repeat themselves for several cycles. These simple harmonic waves can be modeled using a combination of sine and cosine functions. Consider a simplified surface water wave that moves across the water's surface. Unlike complex ocean waves, in surface water waves, water moves vertically, oscillating up and down, whereas the disturbance of the wave moves horizontally through the medium. If a seagull is floating on the...
9.7K
One-Way ANOVA01:18

One-Way ANOVA

14.7K
One-way ANOVA analyzes more than three samples categorized by one factor. For example, it can compare the average mileage of sports bikes. Here, the data is categorized by one factor - the company. However, one-way ANOVA cannot be used to simultaneously compare the sample mean of three or more samples categorized by two factors. An example of two factors would be sports bikes from different companies driven in different terrains, such as a desert or snowy landscape. Here, two-way ANOVA is used...
14.7K
Variance01:15

Variance

13.2K
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....
13.2K
Variability: Analysis01:11

Variability: Analysis

645
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
645
What is an ANOVA?01:16

What is an ANOVA?

11.4K
The Analysis of Variance or ANOVA is a statistical test developed by Ronald Fisher in 1918. It is performed on three or more samples to check for equality between their means.
Before performing ANOVA, one must ensure that the samples used for this analysis have three crucial characteristics or statistical assumptions. The first assumption states that the samples should be drawn from normally distributed samples, while the second requires that all the drawn samples should be randomly and...
11.4K
What is ANOVA?01:13

What is ANOVA?

6.9K
The Analysis of Variance or ANOVA is a statistical test developed by Ronald Fisher in 1918. It is performed on three or more samples to check for equality between their means.
Before performing ANOVA, one must ensure that the samples used for this analysis have three crucial characteristics or statistical assumptions. The first assumption states that the samples should be drawn from normally distributed samples, while the second requires that all the drawn samples be randomly and independently...
6.9K

You might also read

Related Articles

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

Sort by
Same author

A multiscale wavelet-based test for isotropy of random fields on a regular lattice.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2015
Same author

A wavelet-based multiscale ensemble time-scale algorithm.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control·2012
Same author

Evaluating scaled windowed variance methods for estimating the Hurst coefficient of time series.

Physica A·2011
Same author

Wavelet variance analysis for random fields on a regular lattice.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2011
Same author

Depressed mood during the menopausal transition and early postmenopause: observations from the Seattle Midlife Women's Health Study.

Menopause (New York, N.Y.)·2008
Same author

Hot flash severity in hormone therapy users/nonusers across the menopausal transition.

Maturitas·2007
Same journal

Theoretical Foundations of the Echo Envelope Statistical Modeling: A Tutorial.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control·2025
Same journal

Practical Demonstrations of FR3-Band Thin-Film Lithium Niobate Acoustic Filter Design.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control·2025
Same journal

Real-Time Heterogeneous Helical Wave Spectrum Method for Transabdominal Passive Acoustic Mapping.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control·2025
Same journal

Cascaded Plane Wave Ultrasound Velocity Vector Imaging: In Vivo Feasibility in Carotid Arteries.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control·2025
Same journal

Quantitative Acoustic Attenuation Scanning Using a Phase-Insensitive Ultrasound Computed Tomography System.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control·2025
Same journal

FPGA-Accelerated CNN Reconstruction for Low-Power Sparse-Array Ultrasound Imaging.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control·2025
See all related articles
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 Experiment Video

Updated: Mar 30, 2026

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities
08:08

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

Published on: May 10, 2017

15.4K

A Wavelet Perspective on the Allan Variance.

Donald B Percival

    IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
    |November 4, 2015
    PubMed
    Summary
    This summary is machine-generated.

    This study reveals how wavelet variance theory, particularly with Haar wavelets, deepens understanding of Allan variance for time and frequency standards. It enables accurate confidence intervals without assuming noise types, even with gappy or contaminated data.

    More Related Videos

    Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section
    11:00

    Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

    Published on: July 19, 2016

    12.0K
    Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method
    08:42

    Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

    Published on: September 3, 2021

    3.7K

    Related Experiment Videos

    Last Updated: Mar 30, 2026

    Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities
    08:08

    Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

    Published on: May 10, 2017

    15.4K
    Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section
    11:00

    Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

    Published on: July 19, 2016

    12.0K
    Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method
    08:42

    Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

    Published on: September 3, 2021

    3.7K

    Area of Science:

    • Signal Processing
    • Time and Frequency Standards
    • Geophysics

    Background:

    • Allan variance is crucial for characterizing high-performance time and frequency standards.
    • Wavelets and discrete wavelet transform (DWT) emerged in geophysical and signal processing literature.
    • A connection between Allan variance and Haar wavelet transform was previously noted.

    Purpose of the Study:

    • To review wavelet variance theory, focusing on Haar wavelets and their link to Allan variance.
    • To explore wavelet variance estimation for constructing Allan variance confidence intervals without a priori noise assumptions.
    • To discuss specialized wavelet variance estimators for gappy or contaminated data and their adaptation for Allan variance.

    Main Methods:

    • Review of basic wavelet variance theory, emphasizing Haar-based approaches.
    • Interpretation of Allan variance estimators using the maximal overlap discrete wavelet transform (MODWT).
    • Adaptation of specialized wavelet variance estimators for gappy and contaminated data.

    Main Results:

    • Wavelet variance using Haar wavelets is directly related to Allan variance.
    • Wavelet variance estimation provides a method for confidence intervals of Allan variance.
    • Estimators for gappy/contaminated data can be adapted for Allan variance.

    Conclusions:

    • Wavelet variance theory offers a deeper understanding and improved estimation for Allan variance.
    • This approach bypasses the need for pre-specifying noise models for confidence intervals.
    • Generalizations of Allan variance are possible using wavelets beyond the Haar.