10. A system for predicting a future event associated with a business based on historical data associated with a model of past events of the business, comprising: a non-transitory memory communicating with a processor, the non-transitory memory having instructions stored thereon that, in response to execution by the processor, cause the processor to perform operations comprising: performing a transformation of a plurality of modeling variables to obtain a linear relationship of each of the plurality of modeling variables in relation to the dependent variable, wherein the plurality of modeling variables are associated with the model, and wherein a dependent variable is associated with the model and is dependent on the plurality of modeling variables, and wherein the historical data is associated with the model; selecting a subset of the plurality of transformed modeling variables, wherein the selecting comprises applying a selecting rule based on a log-likelihood difference that comprises determining a difference between a first model-fit statistic derived by utilizing an intercept model and a second model-fit statistic derived by utilizing an intercept-plus-covariate model; determining a set of prediction variables; and generating a predictive model using the set of prediction variables, wherein the predictive model predicts the future event.
10. A system for predicting a future event associated with a business based on historical data associated with a model of past events of the business, comprising: a non-transitory memory communicating with a processor, the non-transitory memory having instructions stored thereon that, in response to execution by the processor, cause the processor to perform operations comprising: performing a transformation of a plurality of modeling variables to obtain a linear relationship of each of the plurality of modeling variables in relation to the dependent variable, wherein the plurality of modeling variables are associated with the model, and wherein a dependent variable is associated with the model and is dependent on the plurality of modeling variables, and wherein the historical data is associated with the model; selecting a subset of the plurality of transformed modeling variables, wherein the selecting comprises applying a selecting rule based on a log-likelihood difference that comprises determining a difference between a first model-fit statistic derived by utilizing an intercept model and a second model-fit statistic derived by utilizing an intercept-plus-covariate model; determining a set of prediction variables; and generating a predictive model using the set of prediction variables, wherein the predictive model predicts the future event. 11. The system of claim 10 , wherein the selecting rule is based on at least one of: (i) a correlation between a transformed variable and the dependent variable, and (ii) a proportion of a range of the dependent variable explained by the transformed variable; or a selection rule based on a log-likelihood difference.