"Data modeling" can be used without knowing about the underlying processes that have generated the data; in this case the model is an empirical model. Moreover, in modelling, knowledge of the probability distribution of the errors is not required. "Regression analysis" requires assumptions to be made regarding probability distribution of the errors. Statistical tests are made on the basis of these assumptions. In regression analysis the term "model" embraces both the function used to model the data and the assumptions concerning probability distributions.