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

Introduction to Test of Independence01:21

Introduction to Test of Independence

2.4K
In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
The test statistic for a test of independence is similar to that of a goodness-of-fit test:
2.4K
Hypothesis Test for Test of Independence01:16

Hypothesis Test for Test of Independence

3.7K
The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
H0: The two variables (factors)...
3.7K
Chi-square Distribution01:10

Chi-square Distribution

4.5K
How does one determine if bingo numbers are evenly distributed or if some numbers occurred with a greater frequency? Or if the types of movies people preferred were different across different age groups or if a coffee machine dispensed approximately the same amount of coffee each time. These questions can be addressed by conducting a hypothesis test. One distribution that can be used to find answers to such questions is known as the chi-square distribution. The chi-square distribution has...
4.5K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

287
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...
287
Determination of Expected Frequency01:08

Determination of Expected Frequency

2.2K
Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
2.2K
Ordinal Level of Measurement00:55

Ordinal Level of Measurement

25.6K
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using an ordinal scale are similar to nominal scale data, but there is one major difference. The ordinal scale data can be ordered. An example of ordinal scale data is a list of the top five national parks...
25.6K

You might also read

Related Articles

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

Sort by
Same author

Resting-state EEG alpha-BOLD coupling spatially follows cortical cell-type and receptor gradients.

bioRxiv : the preprint server for biology·2026
Same author

Ion dynamics underlying the seizure delay effect of low-frequency electrical stimulation.

PLoS computational biology·2025
Same author

Deviation From Typical Brain Activity During Naturalistic Stimulation Is Related to Personality Traits.

Psychophysiology·2025
Same author

Intrinsic ion dynamics underlies the temporal nature of resting-state functional connectivity.

bioRxiv : the preprint server for biology·2025
Same author

Learning Latent Profiles via Cognitive Growth Charting in Psychosis: Design and Rationale for the PRECOGNITION Project.

Schizophrenia bulletin open·2025
Same author

Using normative models pre-trained on cross-sectional data to evaluate intra-individual longitudinal changes in neuroimaging data.

eLife·2025

Related Experiment Video

Updated: Sep 3, 2025

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

33.9K

Assessing serial dependence in ordinal patterns processes using chi-squared tests with application to EEG data

Arthur Matsuo Yamashita Rios de Sousa1, Jaroslav Hlinka1

  • 1Institute of Computer Science, Czech Academy of Sciences, Prague 182 07, Czech Republic.

Chaos (Woodbury, N.Y.)
|July 30, 2022
PubMed
Summary

This study introduces a new chi-squared test for analyzing m-dependent ordinal patterns processes. The test quantifies serial dependence, revealing decreased dependence during epilepsy seizures in EEG data.

More Related Videos

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
09:32

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

12.5K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.4K

Related Experiment Videos

Last Updated: Sep 3, 2025

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

33.9K
Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
09:32

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

12.5K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.4K

Area of Science:

  • Statistics
  • Time Series Analysis
  • Signal Processing

Background:

  • Ordinal patterns analysis extends chi-squared tests for independence.
  • m-dependent ordinal patterns processes include those from random walks, white noise, and moving averages.

Purpose of the Study:

  • To develop chi-squared asymptotically distributed statistics for m-dependent ordinal patterns processes.
  • To propose a test for quantifying serial dependence (m-dependence) in such processes.

Main Methods:

  • Extending Elsinger's work on chi-squared tests for independence.
  • Developing asymptotically chi-squared distributed statistics accounting for probability constraints.
  • Proposing and applying a novel m-dependence test.

Main Results:

  • The proposed statistics are chi-squared asymptotically distributed under constraints.
  • The m-dependence test effectively quantifies serial dependence.
  • Epilepsy EEG data showed shorter m-dependence during seizures.

Conclusions:

  • The developed test provides a method to quantify serial dependence in m-dependent ordinal patterns processes.
  • Reduced serial dependence in EEG signals during seizures suggests a potential biomarker.