A Brief Overview of Sas Software
Statistical Analysis System (SAS) Software
In a growing economy and globalization the need for a better statistical analysis tool in the business world become inevitable. There are a number of statistical software that have been developed to fulfill this dynamic need. Statistical software is important it allows mangers to analyze the past performance, understand the present and predict the future there by enable them to make well informed decision. One of the most powerful statistical software is SAS. SAS is an abbreviation for statistical analysis system . In this short article I will try to view in brief what SAS software is and its advantage over the other leading statistical software SPSS.
According to the official web site of SAS, the software developed in 1976 by one of the independent software vendors SAS institute of Cary in North Carolina (Institute).
SAS is a comprehensive data analysis software . It is a tool for managers that helps them in the analysis of data in to productive as well as predictive insight. This enable them to have more accurate and informed decisions. SAS program helps to analyze vast amount of data and calculate different statistical measures that helps business managers and or researchers in their decision making. The SAS system is a tool to organize and analyze a collection of data using SAS programming statements. SAS program has a collection of SAS statement in a logical sequence. Generally there are two main components of SAS programs the data Step and the procedure step (Spector). The data step used to prepare the data for one of the procedures. The data step create a SAS data set , reads data from external source or modify other previously created SAS data set . The procedure step perform analysis on the data, and produce (often huge amounts of) output (Spector). The Advantage/ Limitation of SAS over SPSS
Along with SAS the other major competitor in statistical software is SPSS. SPSS (statistical packages for
In a growing economy and globalization the need for a better statistical analysis tool in the business world become inevitable. There are a number of statistical software that have been developed to fulfill this dynamic need. Statistical software is important it allows mangers to analyze the past performance, understand the present and predict the future there by enable them to make well informed decision. One of the most powerful statistical software is SAS. SAS is an abbreviation for statistical analysis system . In this short article I will try to view in brief what SAS software is and its advantage over the other leading statistical software SPSS.
According to the official web site of SAS, the software developed in 1976 by one of the independent software vendors SAS institute of Cary in North Carolina (Institute).
SAS is a comprehensive data analysis software . It is a tool for managers that helps them in the analysis of data in to productive as well as predictive insight. This enable them to have more accurate and informed decisions. SAS program helps to analyze vast amount of data and calculate different statistical measures that helps business managers and or researchers in their decision making. The SAS system is a tool to organize and analyze a collection of data using SAS programming statements. SAS program has a collection of SAS statement in a logical sequence. Generally there are two main components of SAS programs the data Step and the procedure step (Spector). The data step used to prepare the data for one of the procedures. The data step create a SAS data set , reads data from external source or modify other previously created SAS data set . The procedure step perform analysis on the data, and produce (often huge amounts of) output (Spector). The Advantage/ Limitation of SAS over SPSS
Along with SAS the other major competitor in statistical software is SPSS. SPSS (statistical packages for
Cited: Institute, SAS. SAS Institute Home Page: http://www.sas.com. Spector, Philp. "An Introduction to The SAS System." 2004. Yaffee, Robert A. Connect Statistics and The Social Sciences (1996).