Discuss why SAS is used by many life science organizations. Identify and describe a few statistical packages such as SAS, SPSS, and Excel, etc. available to statisticians. Review the SAS Tutorial and summarize the SAS tools as well as some of their functions. Additionally, describe how this statistical tool could be useful in your profession as a public health professional.
Introduction:
SAS (Statistical Analysis System) is widely used by many life science organizations for statistical analysis due to its extensive range of functionalities and features. This statistical package provides powerful tools for data management, analysis, and visualization, making it essential for researchers and statisticians in the life science field. In this paper, we will discuss the reasons why SAS is commonly used in life science organizations, provide an overview of statistical packages like SAS, SPSS, and Excel, review the SAS Tutorial to understand its tools and functions, and discuss the potential usefulness of SAS in the field of public health.
Why SAS is used in life science organizations:
SAS has become a popular choice among life science organizations for several reasons. Firstly, it offers a wide range of statistical techniques, allowing researchers to analyze complex data sets and perform various statistical analyses. SAS provides tools for both basic and advanced statistical analysis, including regression analysis, analysis of variance (ANOVA), survival analysis, and many others. The flexibility of SAS enables researchers to use the most appropriate analysis methods for their specific research questions and data types.
Secondly, SAS provides robust data management capabilities. With SAS, researchers can easily import, cleanse, transform, and merge large and complex data sets. The ability to handle massive amounts of data is crucial in the life science field, where researchers often work with datasets containing millions of records. SAS also supports data manipulation, allowing users to create new variables, recode existing variables, and perform data transformations efficiently.
Moreover, SAS offers powerful tools for data visualization. It provides various graphics capabilities that allow researchers to create visually appealing and informative graphs and charts to present their findings. Effective data visualization plays a vital role in understanding complex data patterns and communicating research results to a wider audience, including policymakers, stakeholders, and the general public.
Statistical packages available to statisticians:
In addition to SAS, statisticians have access to various other statistical packages such as SPSS (Statistical Package for the Social Sciences), Excel, R, Stata, and MATLAB, to name a few. Each of these packages has its own strengths and weaknesses, and the choice of statistical package depends on the specific requirements of the analysis and the familiarity of the user. Here, we briefly describe a few commonly used statistical packages:
1. SPSS: SPSS is a widely used statistical package known for its user-friendly interface and extensive range of statistical procedures. It is often favored by social scientists and researchers who are new to statistical analysis due to its ease of use and intuitive graphical interface.
2. Excel: Excel is a spreadsheet program that offers basic statistical analysis capabilities. It is commonly used for simple data analysis tasks and is often the go-to solution for individuals without specialized statistical software. However, Excel’s statistical capabilities are limited compared to dedicated statistical packages like SAS and SPSS.
3. R: R is a free and open-source programming language widely used for statistical computing and graphics. It offers a vast array of statistical techniques and packages, making it a popular choice among statisticians and data scientists. R provides extensive flexibility and customization options but requires programming skills to utilize its full potential.
Review of SAS Tutorial: