Table of Contents (click to expand)
The aim of finding correlation is to find out the degree of closeness of scattered points to a straight line (linear association). This helps to indicate a relationship between two random events. Correlation may not always imply causation, as there may be a third variable that is causing both of the events.
Have you ever come across weird statistics about two events that are seemingly unrelated? For example, if one were asked to predict the sales of Air Conditioner (AC) units based solely on the knowledge of sales of frozen yogurts, the prediction may very well seem ridiculous. ACs and yogurts are, after all, two very different consumer goods produced by unrelated industries. One may argue that yogurt has as much in common with an AC as planet Earth has with Haley’s comet.
Here, the relation between (Z, X) and (Z, Y) is a causal relationship. We can predict that an increase in X would be associated with an increase in Y. We can make this prediction due to our knowledge of the common variable Z (temperature). What correlation did, in this instance, was help us find the causal factor behind those two events.
References (click to expand)
- Thangariyal, S., Rastogi, A., Tomar, A., Bhadoria, A., & Baweja, S. (2020, June 16). Impact Of Temperature and Sunshine Duration on Daily New Cases and Death due to COVID-19. []. Cold Spring Harbor Laboratory.
- Jamil, T., Alam, I., Gojobori, T., & Duarte, C. M. (2020, March 31). No Evidence for Temperature-Dependence of the COVID-19 Epidemic. []. Cold Spring Harbor Laboratory.
- The Correlation Coefficient (r) - SPH. Boston University
- D Joyce. Covariance and Correlation Math 217 Probability and Statistics. Clark University
- Textbooks PDF (I-XII) - NCERT. The National Council of Educational Research and Training
