The paper discusses the selection of regression model terms for the analysis of wind tunnel strain-gage balance calibration data. Different function class combinations are presented that may be used to analyze calibration data using either a non-iterative or an iterative method. The role of the intercept term in a regression model of calibration data is reviewed. In addition, useful algorithms and metrics originating from linear algebra and statistics are recommended that will help an analyst

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... ) to identify and avoid both linear and near-linear dependencies between regression model terms and (ii) to make sure that the selected regression model of the calibration data uses only statistically significant terms. Three different tests are suggested that may be used to objectively assess the predictive capability of the final regression model of the calibration data. These tests use both the original data points and regression model independent confirmation points. Finally, data from a simplified manual calibration of the Ames MK40 balance is used to illustrate the application of some of the metrics and tests to a realistic calibration data set. Nomenclature A, B = independent variables N F = total normal force acting on a strain-gage balance, [lbs] N 1 = normal force component at forward gage, [lbs] N 2 = normal force component at aft gage, [lbs] R1, R2, • • • , R6 = electrical outputs of the strain-gages of the balance, [microV /V ] ΔN F = normal force residual, i.e., the difference between the measured and fitted value