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Related Concept Videos

Correlation01:09

Correlation

14.1K
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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Calculating and Interpreting the Linear Correlation Coefficient01:11

Calculating and Interpreting the Linear Correlation Coefficient

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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. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
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Correlations02:20

Correlations

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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...
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What is Weather?01:07

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Overview
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Precipitation and Co-precipitation01:17

Precipitation and Co-precipitation

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Precipitation and coprecipitation methods can be used to separate a mixture of ions in a solution. In qualitative inorganic analysis, ions that form sparingly soluble precipitates with the same reagent are separated based on the differences in solubility products. For example, consider the separation of Cu(II) and Fe(II) ions by precipitation as insoluble sulfides. First, copper(II) sulfide is precipitated by the addition of acidic H2S, where the dissociation of H2S is suppressed. Adding H2S...
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Coefficient of Correlation01:12

Coefficient of Correlation

7.7K
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...
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Related Experiment Video

Updated: Nov 30, 2025

Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses
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Weather Parameters and COVID-19: A Correlational Analysis.

Sourabh Pahuja1, Manu Madan, Saurabh Mittal

  • 1Department of Pulmonary, Critical Care and Sleep Medicine and Biostatistics, All India Institute of Medical Sciences (AIIMS), New Delhi, India.

Journal of Occupational and Environmental Medicine
|November 12, 2020
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Higher temperatures correlate with slower COVID-19 spread in Delhi, India. While temperature impacts disease dynamics, the broader influence of environmental factors on the pandemic requires further global research.

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Area of Science:

  • Environmental Science
  • Epidemiology
  • Public Health

Background:

  • The COVID-19 pandemic caused by the SARS-CoV-2 virus has had a profound global impact.
  • Understanding environmental factors influencing disease transmission is crucial for public health strategies.

Purpose of the Study:

  • To investigate the correlation between ambient temperature, humidity, and wind speed and the occurrence of COVID-19 cases in Delhi, India.

Main Methods:

  • Daily data on COVID-19 cases, temperature, humidity, and wind speed were collected from online sources.
  • Pearson's correlation coefficient was employed to analyze the relationship between daily/weekly cases and environmental variables.

Main Results:

  • A significant positive correlation was observed between temperature and COVID-19 doubling time (r=0.814).
  • A significant negative correlation was found between temperature and the basic reproduction number (R0) (r=-0.78).
  • No significant correlation was found between COVID-19 cases and humidity or wind speed.

Conclusions:

  • Increasing ambient temperature appears to decrease COVID-19 infectivity.
  • The role of environmental factors in the global expansion of the COVID-19 pandemic warrants further investigation.