Since medievil times with game theory and probabilty arithmetics the importance of statistics has constantly risen. Nowerdays statistics are not only a tool for academics but become even more important in industry for product- and process-optimization. 

The crucial part is that statistics provides many different methods that may be applied in any field of application. No matter which software is used - let it be IBM SPSS Statistics, SAS or R - the focus usually lies on providing the right conclusions about the data as fast as possible.

Applied Statistics

Applied statistics cover the most important statistical methods for the analysis of real world data. The fundamental methods are statistical hypothesis testing and data analysis via linear regression. But there are many more methods for specific problems.

If you are using the basic statistical models the choice of software is not that relevant. But for special applications, like Datamining, Design of Experiments or Econometrics, there are many different programs which are specialized for those fields of applications. Especially modular software, like IBM SPSS Statistics, SAS and R shine here. Starting with a basic level of features they can be extend by just the methods that you need.

Theoretical Statistics

While applied statistics focus on real world problems, theoretical statistics provides the methods of tomorrow. Statistical science constantly produces new methods and graphs to solve current and future problems. Part of this is of course the improvement of existing methods.

Especially with the constantly growing amounts of data - Big Data - methods have to be as efficient as possible. This is the point where statistics, math and informatics meet. Just when new methods are developed they are typically optimized in terms of runtime for common software like IBM SPSS Statistics, SAS or R.

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