1. A computer-implemented method comprising: receiving a first set of data describing one or more first observations and a second set of data describing one or more second observations, the first set of data comprising at least two types of data and the second set of data comprising at least two types of data, wherein at least one of the two types of data in the first data set are common with at least one of the two types of data in the second data set, wherein the common types of data comprise common data to the first and second sets of data and the types of data that are not common comprise exclusive data for each of the first and second sets of data; developing a first multiple regression model for the first data set, wherein the common data for the first data set are set as independent variables and the exclusive data for the first data set are set as dependent variables; developing a second multiple regression model for the second data set, wherein the common data for the second data set are set as independent variables and the exclusive data for the second data set are set as dependent variables; receiving prediction results of the first and second multiple regression models; based on the prediction results, classifying at least some of the one or more first observations and the one or more second observations as reasonable observations, comprising well-predicted observations, and at least some of the one or more first observations and the one or more second observations as outlier observations, comprising all observations that are not classified as well-predicted observations; removing the outlier observations; assigning the reasonable observations into intervals for each of the types of data; and based on the assignment, merging the observations to create a third data set.