beta

beta(Y, X)

Return the coefficient estimate of an ordinary-least-squares regression of Y on X (with intercept).

Difference from Python’s scipy.stats.beta: scipy.stats.beta is a beta continuous random variable object for calculating probability density, distribution functions, random variates, and related statistics. DolphinDB’s beta is a statistical regression function that returns the OLS regression coefficient of Y on X.

Difference from Python TA-Lib’s BETA: TA-Lib’s BETA(high, low, timeperiod=5) is a rolling technical-analysis statistic. It calculates beta on the rate-of-change series of high and low over each window. DolphinDB’s beta(Y, X) calculates one OLS regression coefficient over the full input when vectors are specified. To reproduce TA-Lib’s indicator behavior, use DolphinDB’s TA module ta::beta.

Y and X are vectors of the same length.

A DOUBLE scalar/vector/table.

x=1 3 5 7 11 16 23
y=0.1 4.2 5.6 8.8 22.1 35.6 77.2;

beta(y,x);
// output: 3.378632