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Calculate Pearson correlation coefficient (r) between two variables
Karl Pearson developed the correlation coefficient in the 1880s while studying genetics and heredity patterns.
A perfect correlation of +1 or -1 almost never occurs in real-world data due to natural variability and measurement error.
The famous phrase "correlation does not imply causation" reminds us that related variables may not actually cause each other.
Ice cream sales and crime rates are positively correlated, but both are actually caused by hot weather, not each other!
Pearson r only measures linear relationships. Two variables with a strong curved relationship might show r near zero!
Correlation studies on identical twins raised apart helped prove that many traits have genetic components with r values around 0.5-0.8.
Portfolio diversification relies on finding assets with low or negative correlations to reduce overall investment risk.
The number of Nicholas Cage films correlates (r=0.67) with swimming pool drownings! A perfect example of spurious correlation.
The Pearson correlation coefficient (r) measures the linear relationship between two variables. Values range from -1 (perfect negative correlation) to +1 (perfect positive correlation), with 0 indicating no linear relationship. R-squared (r²) represents the proportion of variance in one variable explained by the other.