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* Functions to perform straight-line regressions are included in modules :mod:`type_a` and :mod:`type_b`.
* The regression functions in :mod:`type_a` act on sequences of numerical data in the conventional sense (i.e., only the values of data are used; if the data include uncertain number objects, the associated uncertainty is ignored). The residuals are evaluated and may contribute to the uncertainty of the results obtained, depending on the regression method.
* The regression functions in :mod:`type_b` act on sequences of uncertain-numbers, propagating uncertainty into the results obtained. In most cases, the regression functions in this module are paired with a function of the same name in :mod:`type_a`. For example, :func:`type_a.line_fit` and :func:`type_b.line_fit` both perform an ordinary least-squares regression. The uncertain-numbers for the intercept and slope obtained from :func:`type_a.line_fit` are correlated and have uncertainties that depend on the fitting residuals. On the other hand, the intercept and slope obtained by :func:`type_b.line_fit` depend on the uncertain-number data supplied, and does not take account of the residuals.
* The function :func:`type_a.merge` may be used to combine results obtained from type-A and type-B regressions performed on the same data.
* A number of example calculations are included from Appendix H of the *Guide to the expression of uncertainty in measurement* (`GUM <https://www.iso.org/sites/JCGM/GUM/JCGM100/C045315e-html/C045315e.html?csnumber=50461>`_).
* A number of example calculations are included from the 3rd Edition (2012) of the EURACHEM/CITAC Guide: *Quantifying Uncertainty in Analytical Measurement* (`CG4 <http://www.citac.cc/QUAM2012_P1.pdf>`_).
* There are several examples of applying GTC to linear calibration problems, including the use of regression functions in :mod:`type_a` and :mod:`type_b`.