statsmodels.linearRegression() function

statsmodels.linearRegression() performs a linear regression.

It calculates and returns ŷ (y_hat), and residual sum of errors (rse). Output data includes the following columns:

• N: Number of points in the calculation.
• slope: Slope of the calculated regression.
• sx: Sum of x.
• sxx: Sum of x squared.
• sxy: Sum of x*y.
• sy: Sum of y.
• errors: Residual sum of squares. Defined by (r.y - r.y_hat) ^ 2 in this context
• x: An index [1,2,3,4…n], with the assumption that the timestamps are regularly spaced.
• y: Field value
• y_hat: Linear regression values

Function type signature

(
    <-tables: stream[A],
) => stream[{
    B with
    y_hat: float,
    y: float,
    x: float,
    sy: H,
    sxy: G,
    sxx: F,
    sx: E,
    slope: D,
    errors: float,
    N: C,
}] where A: Record, D: Divisible + Subtractable

Parameters

tables

Input data. Default is piped-forward data (<-).

Examples

Perform a linear regression on a dataset

import "contrib/anaisdg/statsmodels"
import "sampledata"

sampledata.float()
    |> statsmodels.linearRegression()