Numerix v0.6.0 Numerix.LinearRegression View Source

Linear regression functions.

Link to this section Summary

Least squares best fit for points {x, y} to a line y:x↦a+bx where x is the predictor and y the response.

Estimates a response y given a predictor x and a set of predictors and responses, i.e. it calculates y in y:x↦a+bx.

Measures how close the observed data are to the fitted regression line, i.e. how accurate the prediction is given the actual data.

fit(Numerix.Common.vector(), Numerix.Common.vector()) :: {float(), float()}

Least squares best fit for points {x, y} to a line y:x↦a+bx where x is the predictor and y the response.

Returns a tuple containing the intercept a and slope b.

predict(number(), Numerix.Common.vector(), Numerix.Common.vector()) :: number()

Estimates a response y given a predictor x and a set of predictors and responses, i.e. it calculates y in y:x↦a+bx.

r_squared(Numerix.Common.vector(), Numerix.Common.vector()) :: float()

Measures how close the observed data are to the fitted regression line, i.e. how accurate the prediction is given the actual data.

Returns a value between 0 and 1 where 0 indicates a prediction that is worse than the mean and 1 indicates a perfect prediction.