1. A method for predicting a dependent variable based on N independent variables, wherein each independent variable (i) has Li number of levels, comprising: storing a collection of data in storage unit of a computing device, wherein the data collection includes a plurality of observations that includes at least N data points, where each of the N data points within any given one of the plurality of observations corresponds to a different one of the N independent variables; retrieving at least a portion of the data collection to use as a training sample; retrieving at least a portion of the data collection to use as a rule selection sample; creating one or more TABs using the data contained in the training sample, wherein each of the one or more TABs comprises X number of vectors, wherein X is equal to the number of levels of the dependent variable, and wherein each of the X vectors consists essentially of Y values and each of the X vectors corresponds to a different one of the levels of the dependent variable, where Y=SUM(Li) for i=1 to N, where Li is the number of levels of the ith independent variable; selecting a rule from a set of rules; selecting an observation from the rule selection sample; predicting said variable assigning a prediction to a level of the dependent variable using one or more of the TABs, the selected rule, and the N data points from the selected observation that correspond to the N independent variables; and determining the accuracy of the prediction.