Pearson Correlation

• Correlation tells us how strongly two random variables are related to each other.

• Pearson Co-relation assume that both the datasets are from normal distribution, so it can't be used with discrete variables.

• Pearson Co-relation is not affected by scale and so easy to interpret

• Ranges from $[-1, 1]$

• We can interpret as,

  • $-1$ as totally Negative relationship
  • $+1$ as totally Positive relationship
  • $\approx 0$ as no relationship

• Pearson-correlation depends on Variance and Co-Variance

[!def] Formula of Pearson Co-relation
$$
Co-relation(x, y) = \frac{Co-Variance(x, y)}{\sqrt{variance(x)} \sqrt{variance(y)}}
$$

• As we can see co-relation is dependent on Co-Variance and Variance

TODO:

1. Cosine Similarity vs Pearson Correlation