The Statistical Analysis System (SAS) specializes in advanced analytics. The main purpose of SAS is to retrieve, report and analyze statistical data. It has the flexibility to incorporate data from numerous sources and facilitate descriptive visualization through graphs. This article brings you the essentials of SAS for researchers who require the software for their data analysis.
What is SAS?
SAS is a software package that enables you to analyze and interpret data gathered from different types of data sources. It can be used to analyze structured and unstructured data. It is a complete package that integrates data modeling, statistics, and report generation. It is commonly used by government agencies, businesses, and educational institutions to perform advanced analytics. SAS is also used in many applications, including marketing research, finance, and quality assurance.
How to read and write data using SAS
SAS can be used to read and write data in a variety of formats, including raw data files, delimited data files, data tables, and spreadsheet files. The read-only capability allows you to look through the data and extract the data you need for analysis. Data can be visualized as tables, graphs, charts, or reports. You can create new data types within the software to support various applications.
Key concepts in SAS
There are a number of key concepts in SAS that you should understand before moving forward with your data analysis. These are: Data Modeling: This is the process of decomposing an object or data set into smaller pieces to make the data more tractable to analysis. The data modeling is done to create an organized view of the data. Data Extraction: Data extraction is the process of taking the data modeling and transforming it into forms that are easily accessible for analysis. Data Reduction: Data reduction is the process of removing or reducing non-value-added information from the data. Data Importance: Data Importance is the measure of importance or relevance of the data to the analysis. Data Population: Data Population is the set of values that make up the data. Data Type: Data type is the properties that define the data type. For example, number, character, financial data type, etc.
Why analyze with SAS
SAS supports all major statistical methods, including analysis of variance, regression modeling, time series, multivariate analysis and graphics. SAS also has a large collection of user-written routines that allow you to perform common statistical tasks without writing code. Data can be entered into a SAS program using a variety of means: keyboard entry or file transfer via a communications port; data can also be imported from other programs such as SPSS or Stata. The program is also able to read in data from disk files (e.g., text files) or databases (e.g., relational databases). One of its main features is its ability to handle very large datasets with ease. Even if a dataset has hundreds or thousands of records, SAS will process each record efficiently so that you don’t have to deal with processing issues on your own. It also offers various ways of analyzing data such as regression modeling, variance analysis, descriptive statistics, correlation analysis and much more.
Wrapping up
SAS provides a wide variety of reports that look similar to the original data set. These reports can be used to analyze the data to determine the cause of an anomaly, examine the importance of a given variable in the data, or generate highlights for a chart or graphs. The reports generated by SAS can help you answer specific questions such as what happened in a data set and understand the “why” of the changes.