1. A method for adjusting a data set defining a set of process runs, each process run having a set of data corresponding to a set of variables for a wafer processing operation, comprising: receiving a model derived from a data set; receiving a new data set corresponding to one process run; projecting the new data set to the model; identifying an outlier data point produced as a result of the projecting; identifying a variable corresponding to the one outlier data point, the identified variable exhibiting a high contribution; identifying a value for the variable from the new data set; determining whether the value for the variable is unimportant; creating a normalized matrix of data, using random data and the variable that was determined to be unimportant from each of the new data set and the data set; and updating the data set with the normalized matrix of data.