Uncertainty analysis frequently involves the matrix of mixed second derivatives of 2  with respect to the parameters, called the Fisher Information Matrix. The inverse of this matrix gives the parameter variancecovariance matrix. From the Bayesian perspective, the diagonal elements of a parameter variancecovariance matrix give lower bounds for variances for the parameter values, assuming the distribution of likely parameter values is normal. These variances (or the corresponding standard deviations) are easily calculated and reported with the parameters themselves. Monte Carlo methods can also be used to explore ranges of parameter values. For data sets where more comprehensive uncertainty analysis is justified, or when formulas for the Fisher Information Matrix elements are not available, it can be advantageous and informative to carry out such explorations. A more detailed treatment of parameter uncertainty analysis is provided in Part 2 of this report.