Curve fitting guides

These guides explain the assumptions behind a fit and how to interpret its results. For data entry, controls, saving and exports, use application Help.

• Curve fitting with measurement uncertainties — Understand measurement and parameter uncertainties, Absolute and Nominal conventions, and what residual scaling assumes.

• Weighted least squares and a linear fit — Compare ordinary and weighted least squares with a worked linear example, and learn when x uncertainties change the fitting problem.

• Combining measurements with a weighted mean — Calculate a weighted average and distinguish propagated uncertainty from uncertainty estimated using residual scatter.

• Fitting a custom equation — Build an interpretable custom model, choose starting values, and recognize domain restrictions and parameters your data cannot separate.

• Fitting a Gaussian peak — Understand Gaussian height, center, width and background, explore a public example, and distinguish peak width from measurement uncertainty.