Correlation, despite its bad reputation, remains a vital tool in statistics for measuring the relationship between two variables. However, it has a significant flaw: it doesn’t account for the influence of other variables, leading to the common saying, “Correlation is not causation.” Enter partial correlation, a lesser-known but powerful statistical method that addresses this issue. Partial correlation retains the simplicity and effectiveness of traditional correlation while considering the effects of additional variables. Surprisingly, this method is not widely recognized, evidenced by its implementation in only one Python library, Pingouin, which isn’t a primary choice for most data scientists.
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