What is meant by, ″correlation does not imply causation″?


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Directions:
Based on your number, you will only complete the question corresponding to that number. For your assigned question, post both, the question, and your response. Include your assigned question number on the post′s title. Comment on whether you agree or disagree with their answers. If you disagree, explain why.
Group 1 Question:
1. Give a real-life example of a pair variables which have an obvious correlation coefficient of 1 (or close to it), expand on and explain these variables and their relationship. Describe what the scatter plot of these variables would look like?
Just as an example:
Since my last name begins with M, I would belong to Group 3. So, I would be tasked with answering question #3 only but I would need to reply to (at least) two students whose question was not question #3. ]
Here is what I would answer (notice how I include the question too! also, anything in blue must be replaced by your own responses; do not use the example!):
What is meant by, ″correlation does not imply causation″? Give an example of two variables for which there is correlation but not causation, expand and explain.
The phrase ″correlation does not imply causation″ is used to emphasize that a strong correlation value does not automatically mean that one variable ″causes″ or ″influences″ another variable. Often there is a third hidden variable that actually influences both but it could also be that random chance makes two variables strongly correlated. For example, according to data compiled in a famous website (this one: https://www.tylervigen.com/spurious-correlations), the divorce rate in the US state of Maine is highly correlated with the per capita consumption of margarine (r=0.99). It would be extremely surprising if eating margarine causes couples′ divorces in that particular US state, so very likely this example of a high correlation occurs just by random chance (without a third variable influencing both).
Hope this makes sense!
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