Select numeric X and measured Y variables from one Dataset, optionally restrict rows by a Dataset Selection, and choose linear or polynomial regression. Linear mode includes an intercept. Polynomial mode uses the requested positive degree and the public numerical API’s centered/scaled design for stability. Rank-deficient fits fail by default; an explicit minimum-norm policy is available and recorded.
Weight semantics are explicit. None gives ordinary
least squares. Statistical weight uses a separate
nonnegative dimensionless variable; zero-weight rows are excluded from
fitting. Inverse variance uncertainty uses a strictly
positive measurement standard uncertainty compatible with Y, converts it
to the Y unit, and computes 1 / uncertainty² weights. The
model and the public NUMERIC_API_v1 fit record the weight
semantics. Missing or nonfinite rows follow the selected policy; every
published fit row preserves the original one-based source-row
identity.
The module publishes <prefix>.fit (X, measured Y,
fitted Y, residuals and absolute residuals),
<prefix>.curve (sorted observed X and model
predictions for plotting), <prefix>.coefficients
(index, power, estimate, standard error and coefficient-unit text),
<prefix>.covariance (coefficient covariance matrix as
ordered pairs), and <prefix>.metrics (R², adjusted
R², RMSE, residual error metrics, rank, condition number, degrees of
freedom, SSE and weight semantics). Fit and residual plots are
persistent public Plot Dataset variants. Reports and provenance record
stable source Dataset/variable IDs, Selection and all scientific
choices. No nonlinear model fitting is added here.