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Noncentral distributions are obtained by transformation of their respective central distributions, and are identified by a noncentrality parameter. The noncentrality parameter measures the degree to which mean of test statistics departs, when the null hypothesis is false. Central distributions are used to describe test statistics, when the null hypothesis is true. Noncentral distributions are used to calculate statistical power of a test in situations when the null hypothesis is false. The paper presents Visual Basic for Application code in Microsoft Excel to compute the cumulative distribution function for noncentral chi-square distributions. The results obtained were found to be comparable with the reported values.

The aim of this paper is to estimate the parameter precision values in nonlinear regression using Microsoft Excel. In case of simple linear regression, these values can be obtained from regression analysis in Excel. Commercially available programs output these values, but the main limitation is that they force the users to adopt a black box approach. The procedure described here can be adopted to any nonlinear dataset assuming that parameters obtained from solver accurately describe the nonlinear regression model. For one of the nonlinear datasets from NIST Statistical reference datasets, the parameters are initially estimated by using Solver and then by using Finite differences method parameter precision values are calculated. The results obtained were found to be comparable with the reported values. [ABSTRACT FROM AUTHOR]