Correlation

Need for correlation

Intuition behind correlation

  1. It cancels the effect of units of each column in the bivariate data series and hence makes correlation independent of units so that we can compare any series
  2. It brings the correlation to the range of -1 to 1 which provides a universal scale for comparison of bivariate data series
  3. Since standard deviation is always positive, it does not change the sign of any term in the formula of covariance

Properties of correlation

  1. It is symmetric which means that correlation between x and y is same as the correlation between y and x
  2. It is always between -1 and 1
  3. Correlation coefficient is independent of scale change and origin change which means that correlation does not change when you multiply or divide each element of the series with a particular number or you add or subtract each element of the series by a particular number

Inference of correlation

  1. If the correlation coefficient is close to -1 it means that the value of x decreases with increase in value of y. This implies strong negative correlation
  2. If the correlation coefficient is close to 0 it means that the value of x is independent of value of y. This implies no correlation
  3. If the correlation coefficient is close to 1 it means that the value of x increases with increase in value of y. This implies strong positive correlation

Demerits of correlation

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A data science enthusiast currently doing bachelor's degree in data science

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Aayushmaan Jain

Aayushmaan Jain

A data science enthusiast currently doing bachelor's degree in data science

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