An analysis of the game includes the segmentation of a complex problem smaller section, and then analyzes the smaller parts individually. As soon as the smallest segments are analyzed, the results of new co-related with respect to the process to solve the problem.The statistics should analyze the information that is known in the first instance, to have the complete collection. The data points are then compared over time or other relevant variables. The sample data will be initially examined and tested and see if there is a solution that could be from the analysis. SAS (Statistical Analysis System) is a 4th generation procedural language that allows programmers to use the analysis of statistical data and data-room to build descriptive and predictive statistical models. Data mining is the process of discovering previously unknown information, actionable and profitable by big bases of consolidated data and using it to support tactical and strategic decisions and is made very easy with SAS. Each part of the process will have to be analyzed to display the quality and the output quantity. The system will include input, process and output. The statistical analysis will help in the measurement of the inputs being the first to obtain a desired output with the execution of this process. Many procedures are available that can be understood in terms of statistical analysis. You could in practice that the best and the most suitable one to select a logical connection to your process. The technical mining statistics are linear and logistic regression, multivariate analysis, principal component analysis, decision trees and neural nets. Traditional approaches without statistical inference with large databases, however, because with hundreds of thousands or millions of instances and hundreds of thousands of variables, there is a high degree of redundancy between the variables. With this data, there will be false reports, and even the weakest relationships will be very large for each statistical test. Lens with SAS is a significant predictive model to build. It is not sufficient to find that the reports are only statistically significant. Other possibilities included SAS management and modeling effort that the process of creating, managing and deploying analytical models easier. Another possibility of the language is to predict or analyze and predict outcomes based on historical statistical models. Econometrics, statistical methods to economic data, problems and trends is also available. The language makes it possible for continuous monitoring and improvement of quality, the ability to identify, monitor and measure the quality of processes over time. Operational research is also available through the use of techniques such as optimization, simulation and planning in order to obtain the best results over time.