Automated spectral analysis and estimation of signal amplitudes from magnetic resonance data generally constitutes a difficult nonlinear optimization problem. Obtaining a measure of the degree of confidence that one has in the estimated parameters is as important as the estimates themselves. This is particularly important if clinical diagnoses are to be based on estimated metabolite levels, as in applications of MR Spectroscopic Imaging for human studies. In this report, a standard method of obtaining confidence intervals for nonlinear estimation is applied to simulated data and short-TE clinical proton spectroscopic imaging data sets of human brain. So-called "confidence images" are generated to serve as visual indicators of how much trust should be placed in interpretation of spatial variations seen in images derived from fitted metabolite parameter estimates. This method is introduced in a Bayesian framework to enable comparison with similar techniques using Cramer-Rao bounds and the residuals of fitted results.