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Correlation describes the statistical relationship between two or more variables, quantifying the direction and strength of association. It is used for exploratory analysis, hypothesis generation and feature selection, but it does not establish causation and requires attention to sample size, outliers and non-linearity. Different measures (e.g., Pearson, Spe…
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Correlation describes whether and how strongly two variables vary together; it does not prove a cause.
Francis Galton developed the statistical idea of correlation in the late nineteenth century; Karl Pearson then formalized the Pearson correlation coefficient. This made relationships between two variables quantitatively describable.
Picture a scatter plot: joint rises are positive, opposite movement negative, and a tight cloud indicates strength. Outliers and curves can mislead.
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Its value depends on applying it to an observable problem and checking the result.
Correlation supports exploratory analysis and forecasting. Confounders, outliers, and the chosen coefficient limit interpretation.
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