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

Correlation01:09

Correlation

11.7K
In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
11.7K
Calculating and Interpreting the Linear Correlation Coefficient01:11

Calculating and Interpreting the Linear Correlation Coefficient

5.9K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable, x, and the dependent variable, y. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
5.9K
Correlation of Experimental Data01:23

Correlation of Experimental Data

230
Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity,...
230
Correlations02:20

Correlations

32.8K
Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
32.8K
Global Climate Change01:50

Global Climate Change

24.3K
Throughout its ~4.5 billion year history, the Earth has experienced periods of warming and cooling. However, the current drastic increase in global temperatures is well outside of the Earth’s cyclic norms, and evidence for human-caused global climate change is compelling. Paleoclimatology, the study of ancient climate conditions, provides ample evidence for human-caused global climate change by comparing recent conditions with those in the past.
24.3K
Coefficient of Correlation01:12

Coefficient of Correlation

6.1K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
6.1K

You might also read

Related Articles

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

Sort by
Same author

Income Differences in the Association Between Medical-Preventive Integration KAP and Depressive Symptoms: A Cross-Sectional Study Among Chinese Healthcare Workers.

Healthcare (Basel, Switzerland)·2026
Same author

Association between dietary inflammatory index, empirical dietary inflammatory patterns and the risk of Long COVID: a prospective cohort study.

BMC public health·2026
Same author

Clinical factors of prolonged treatment duration in patients with non-puerperal mastitis.

Gland surgery·2026
Same author

Adolescents' Daily Disclosure and Concealment and Their Associations with Functional Autonomy and Detachment from Mothers.

Journal of youth and adolescence·2026
Same author

Prokaryotic community diversity, assembly processes, and potential metabolic pathways of a hexanoic acid-producing microbiota constructed from Chinese baijiu fermentation pit mud.

Bioresource technology·2026
Same author

What drives generational differences in subjective well-being? A machine learning study of Chinese migrant workers in the Yangtze River Delta.

Acta psychologica·2026

Related Experiment Video

Updated: Jun 21, 2025

Investigating the Relationship between Sea Surface Chlorophyll and Major Features of the South China Sea with Satellite Information
10:28

Investigating the Relationship between Sea Surface Chlorophyll and Major Features of the South China Sea with Satellite Information

Published on: June 13, 2020

5.8K

Multiple serial correlations in global air temperature anomaly time series.

Meng Gao1, Xiaoyu Fang1, Ruijun Ge1

  • 1School of Mathematics and Information Sciences, Yantai University, Yantai, China.

Plos One
|July 9, 2024
PubMed
Summary

Serial correlations are universal in global surface air temperature (SAT) time series. Understanding these temporal patterns, including short-term, long-term, and nonlinear types, is crucial for climate science research.

More Related Videos

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.7K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.3K

Related Experiment Videos

Last Updated: Jun 21, 2025

Investigating the Relationship between Sea Surface Chlorophyll and Major Features of the South China Sea with Satellite Information
10:28

Investigating the Relationship between Sea Surface Chlorophyll and Major Features of the South China Sea with Satellite Information

Published on: June 13, 2020

5.8K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.7K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.3K

Area of Science:

  • Climate Science
  • Time Series Analysis
  • Statistical Physics

Background:

  • Serial correlations in temperature data indicate temporal consistency of climate events.
  • Global surface air temperature (SAT) time series contain inherent variability crucial for climate system understanding.

Purpose of the Study:

  • To investigate serial correlations within global surface air temperature (SAT) anomaly time series.
  • To identify and characterize short-term, long-term, and nonlinear serial correlations in climate data.

Main Methods:

  • Preprocessing SAT time series to obtain anomaly data.
  • Utilizing the first-order autoregressive model (AR(1)) for short-term correlations.
  • Applying detrended fluctuation analysis (DFA) for long-term correlations.
  • Employing the horizontal visibility graph (HVG) algorithm and a novel parameter (Δσ) for nonlinear correlations.
  • Statistical significance testing using Monte Carlo simulations.

Main Results:

  • Identified global patterns of short-term correlations linked to atmospheric phenomena like Rossby waves.
  • Detected long-term correlations consistent with climate variability, such as the El Niño-Southern Oscillation (ENSO).
  • Demonstrated the effectiveness of HVG topological parameters and Δσ in capturing and detecting temporal correlations, respectively.

Conclusions:

  • Serial correlations are a universal feature of global SAT time series.
  • The identified correlation types provide insights into climate dynamics and variability.
  • These findings underscore the importance of considering serial correlations in climate science analyses.