5.1 Correlation notes

1. 5.1.1Correlation: types, scatter diagram and Karl Pearson's coefficient

   Correlation measures how closely two variables move together in a straight-line pattern; Karl Pearson's coefficient r = Cov(x, y) ÷ (σx σy) gives this as a pure number between −1 and +1.

   Advanced · 9 min · Read here
2. 5.1.2Spearman's rank correlation (with tied ranks) and probable error

   Spearman's rank correlation R = 1 − 6Σd² ÷ n(n² − 1) measures agreement between two rankings; the probable error 0.6745 (1 − r²)/√n tells whether a sample r is large enough to be taken seriously.

   Advanced · 7 min · Read here
3. 5.1.3Regression: lines of regression, regression coefficients and their properties; correlation versus regression

   Regression gives the average relationship between two variables as a line, so that one variable can be estimated from the other; there are two lines (Y on X and X on Y), their slopes are the regression coefficients byx and bxy, and r is their geometric mean.

   Advanced · 9 min · Read here

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