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Time-Series Graph00:54

Time-Series Graph

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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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Cause and Effect01:53

Cause and Effect

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While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
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Correlation01:09

Correlation

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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:
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Scatter Plot01:15

Scatter Plot

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The most common and easiest way to display the relationship between two variables, x and y, is a scatter plot. A scatter plot shows the direction of a relationship between the variables. A clear direction happens when there is either:
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Time Course of Drug Effect01:14

Time Course of Drug Effect

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The progression of a drug's impact can be analyzed by examining both the concentration-time course and the effect-time course. The concentration-time course is determined by the drug's half-life and is influenced by factors such as its pharmacokinetics, including absorption, distribution, metabolism, and elimination. The effect of the drug is often related to its concentration in the plasma and is calculated using the maximum drug effect and the plasma concentration that generates 50...
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Correlation and Causation01:27

Correlation and Causation

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Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
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Related Experiment Video

Updated: Apr 19, 2026

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
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Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

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Unraveling the cause-effect relation between time series.

X San Liang1

  • 1School of Marine Sciences, Nanjing University of Information Science and Technology (Nanjing Institute of Meteorology), Nanjing 210044 and China Institute for Advanced Study, Central University of Finance and Economics, Beijing 100081, China.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|December 11, 2014
PubMed
Summary

This study introduces a quantitative method to determine causality between two time series using information flow. It confirms that while causation implies correlation, the reverse is not always true, offering a new tool for scientific discovery.

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Area of Science:

  • Complex Systems
  • Time Series Analysis
  • Causality Studies

Background:

  • Determining cause and effect between time series is a fundamental challenge across many scientific disciplines.
  • Traditional methods often struggle with complex, nonlinear relationships and can be misled by correlation alone.

Purpose of the Study:

  • To develop a rigorous, quantitative method for identifying causality between two time series.
  • To establish a new framework for measuring causality based on the physical concept of information flow.

Main Methods:

  • Utilized a recently rigorized physical notion of information flow to solve an inverse problem.
  • Developed a formula for causality measurement based on the time rate of information flow between series.
  • Employed commonly used statistics, such as sample covariances, in the causality formula.

Main Results:

  • Successfully validated the method with linear and nonlinear time series exhibiting one-way causality.
  • Demonstrated that causation implies correlation, but correlation does not imply causation.
  • Applied the method to analyze the asymmetric causal relationship between El Niño and the Indian Ocean Dipole (IOD).

Conclusions:

  • The information flow method provides a robust and quantitative approach to causality detection in time series.
  • The findings highlight the limitations of correlation-based analyses and underscore the importance of directional causality.
  • The study offers a valuable tool for investigating complex interactions in fields ranging from climate science to economics.