Improvement of parameter accuracy by choice and quality of observation

In a least squares adjustment (a minimum variance solution) using the technique of variation of coordinates (observation equations), a key result is the co-variance (dispersion) matrix of parameters. Assuming that standard errors of observations are used in the formation of the normal equations, rather than relative weights, this dispersion matrix gives the estimates of standard errors for the parameters solved for in the adjustment. A method will be presented which allows the designer of the observing plan to alter this dispersion matrix, which may not meet user requirements, so that it will meet user requirements and, from its inverse, solve mathematically for the selection and quality (accuracy) of the observations required to form this altered dispersion matrix of parameters.