Difference between revisions of "Pearson Correlation Coefficient"
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Pearson’s correlation coefficient is the test statistics that measures the statistical relationship, or association, between two continuous variables. It is known as the best method of measuring the association between variables of interest because it is based on the method of covariance. It gives information about the magnitude of the association, or correlation, as well as the direction of the relationship.<ref>Definition - What Does Pearson Correlation Coefficient Mean? [https://www.statisticssolutions.com/pearsons-correlation-coefficient/ Statistics Solutions]</ref> | Pearson’s correlation coefficient is the test statistics that measures the statistical relationship, or association, between two continuous variables. It is known as the best method of measuring the association between variables of interest because it is based on the method of covariance. It gives information about the magnitude of the association, or correlation, as well as the direction of the relationship.<ref>Definition - What Does Pearson Correlation Coefficient Mean? [https://www.statisticssolutions.com/pearsons-correlation-coefficient/ Statistics Solutions]</ref> | ||
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| + | == Pearson Correlation Coefficient Calculator<ref>Pearson Correlation Coefficient Calculator [https://www.socscistatistics.com/tests/pearson/ Social Science Statitics]</ref> == | ||
| + | The Pearson correlation coefficient is used to measure the strength of a linear association between two variables, where the value r = 1 means a perfect positive correlation and the value r = -1 means a perfect negataive correlation. So, for example, you could use this test to find out whether people's height and weight are correlated (they will be - the taller people are, the heavier they're likely to be). | ||
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| + | Requirements for Pearson's correlation coefficient | ||
| + | *Scale of measurement should be interval or ratio | ||
| + | *Variables should be approximately normally distributed | ||
| + | *The association should be linear | ||
| + | *There should be no outliers in the data | ||
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| + | Equation | ||
| + | [[File:Pearson CorrelationCoefficent.png|200px|Pearson Correlation Coefficent Equation]]<br /> | ||
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| + | == References == | ||
| + | <references/> | ||
Revision as of 02:16, 16 July 2019
Pearson’s correlation coefficient is the test statistics that measures the statistical relationship, or association, between two continuous variables. It is known as the best method of measuring the association between variables of interest because it is based on the method of covariance. It gives information about the magnitude of the association, or correlation, as well as the direction of the relationship.[1]
Pearson Correlation Coefficient Calculator[2]
The Pearson correlation coefficient is used to measure the strength of a linear association between two variables, where the value r = 1 means a perfect positive correlation and the value r = -1 means a perfect negataive correlation. So, for example, you could use this test to find out whether people's height and weight are correlated (they will be - the taller people are, the heavier they're likely to be).
Requirements for Pearson's correlation coefficient
- Scale of measurement should be interval or ratio
- Variables should be approximately normally distributed
- The association should be linear
- There should be no outliers in the data
Equation
References
- ↑ Definition - What Does Pearson Correlation Coefficient Mean? Statistics Solutions
- ↑ Pearson Correlation Coefficient Calculator Social Science Statitics