SAS Statistical Programming

SAS Statistical Programming Introduction

Statistical Programming is an advanced originally developed by StatSoft is analytics software package. Statistica provides statistics, data management, data analysis, data mining, data visualization, text analytics & machine learning  procedures.  Statistica Enterprise for use across a site or organization product categories include, Concurrent Network Desktop ,Web-Based for use with a server and Single-User Desktop &  web browser.

SAS Statistical Programming Job Support

Statistics are applied every day – in research, industry and government – to become more scientific about decisions that need to be made. For example: Manufacturers use statistics to weave quality into beautiful fabrics, to bring lift to the airline industry and to help guitarists make beautiful music.Researchers keep children healthy by using statistics to analyze data from the production of viral vaccines, which ensures consistency and safety.  Communication companies use statistics to optimize network resources, improve service and reduce customer churn by gaining greater insight into subscriber requirements.Government agencies around the world rely on statistics for a clear understanding of their countries, their businesses and their people. Popular statistical computing practices include:

  • Statistical programming – From traditional analysis of variance and linear regression to exact methods and statistical visualization techniques, statistical programming is essential for making data-based decisions in every field.
  • Econometrics – Modeling, forecasting and simulating business processes for improved strategic and tactical planning. This method applies statistics to economics to forecast future trends.
  • Operations research – Identify the actions that will produce the best results – based on many possible options and outcomes.
  •  Matrix programming – Powerful computer techniques for implementing your own statistical methods and exploratory data analysis using row operation algorithms.
  • Statistical visualization – Fast, interactive statistical analysis and exploratory capabilities in a visual interface can be used to understand data and build models.
  • Statistical quality improvement – A mathematical approach to reviewing the quality and safety characteristics for all aspects of production.
  • High-performance statistics – For the biggest big data challenges, in-memory infrastructures and parallel processing can fit predictive models faster, perform more modeling iterations and use complex techniques for faster results.
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