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Correlation and Causation
Correlation
Correlation - is a statistical measure to quantify the strength of the relationship between two quantitative and continuous variables. The relationship can be one of the following
Positive - increasing one variable would increase the other
Negative - increasing one variable would decrease the other
No Correlation - increasing one variable has no impact on the other
Correlation is usually denoted by Pearson's Correlation Coefficient (r) and it ranges from -1 to 1.
-1: Perfect negative correlation
0: No correlation
1: Perfect positive correlation
Causation / Causality
Causation/Causality is the relationship between an outcome or an event and a potential reason or the cause. Two variables are said to be in a causal relationship when one variable (input variable) leads to or affects the second variable (output variable)
An application-oriented question on the topic along with responses can be seen below. The best answer was provided by
Natwar Lal on 15th June 2019.
Applause for all the respondents- Amlan Dutta, Natwar Lal.
Also review the answer provided by Mr Venugopal R, Benchmark Six Sigma's in-house expert.
Question
Vishwadeep Khatri
Q. 168 Correlation does not prove causation. Assuming continuous data for both, is it safe to say that proven cause effect relationship certainly results in strong correlation between the cause and effect variables? Explain with examples.
Note for website visitors - Two questions are asked every week on this platform. One on Tuesday and the other on Friday.
4 answers to this question
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