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Related Concept Videos

Coefficient of Correlation01:12

Coefficient of Correlation

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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:
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In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
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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.
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Spearman's rank correlation test, also known as Spearman's rho, is a nonparametric method for assessing the strength and direction of association between two variables. This test is particularly valuable when the data distribution is unknown or when the assumption of normality does not hold. Named after the English psychologist and statistician Dr. Charles Edward Spearman, it serves as the nonparametric counterpart to Pearson's correlation coefficient.
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Correlation between flu and Wikipedia's pages visualization.

Vincenza Gianfredi1, Omar Enzo Santangelo2, Sandro Provenzano3

  • 1. gianfredi.vincenza@hsr.it.

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Italian public searches for flu, fever, and cough on Wikipedia align with official health data. This correlation suggests Wikipedia Trends can monitor public health trends, aiding in public health interventions.

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Area of Science:

  • Epidemiology
  • Public Health Surveillance
  • Digital Epidemiology

Background:

  • The Istituto Superiore di Sanità (ISS) monitors influenza cases in Italy.
  • Online search trends may reflect public interest and concern regarding influenza.
  • Assessing the correlation between online search data and official health data can validate digital tools for public health.

Purpose of the Study:

  • To determine if Italian general public's Wikipedia search frequency for influenza correlates with official ISS influenza case data.
  • To explore the potential of using Wikipedia Trends as a public health surveillance tool.

Main Methods:

  • Collected weekly influenza case data from ISS (October 2015 - May 2019).
  • Extracted weekly Wikipedia page view data for "Influenza," "Febbre," and "Tosse" (Flu, Fever, Cough) using Wikipedia Trends.
  • Performed cross-correlation analysis (product-moment correlation) between ISS data and Wikipedia search trends at various lags.

Main Results:

  • A temporal correlation was observed between ISS influenza data and Wikipedia search trends.
  • Strongest correlations were found at lag 0 and lag 1 for "Flu," "Fever," and "Cough" page views compared to ISS case numbers (r values ranging from 0.7501 to 0.7571).
  • Significant correlations indicate that Wikipedia search volumes closely mirror reported influenza cases.

Conclusions:

  • Wikipedia search trends demonstrate a strong alignment with official influenza case data in Italy.
  • Wikipedia Trends can serve as a valuable, real-time tool for public health surveillance.
  • This approach offers potential for future programming and management of public health interventions.