The Pearsons Correlation Coefficient r A quick way to calculate r r S xy S xxS

The pearsons correlation coefficient r a quick way to

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The Pearson’s Correlation Coefficient ( r ) A quick way to calculate r
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r = S xy S xx S yy WHERE: S xx = ∑ x 2 ( ∑ x ) 2 n S xy = ∑ xy ( ∑ x )( ∑ y ) n S yy = ∑ y 2 ( ∑ y ) 2 n
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Example of the correlation coefficient - This indicates that there is a very strong linear and positive association between the height and weight of the people in our sample. That means that as the height of people increases, the weight increases as well almost in a linear way. - In simple terms it means if we meet a tall and a shorter person, it is likely that the taller person will weigh more than the shorter person. - However, it does not mean that being taller causes someone to weigh more as well. As association exists, however in order to investigate whether a causality exists, further analysis has to be carried out. ( regression analysis). Causality vs Correlation. Due to the presence of spurious correlations it is easy to mix causality with correlation. Correlations does not always imply causality.
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Spurious Correlation: Two variables may have no direct causal connection but many be wrongly inferred that they have due to the presence of another variable that causes both.
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  • Summer '20
  • Correlation does not imply causation, Statistical dependence

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