Numerix v0.5.0 Numerix.LinearRegression View Source
Linear regression functions.
Link to this section Summary
Functions
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
Link to this section Functions
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.