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SAS is the abbreviation for Statistical Analysis System; this statistical suite was developed by SAS Institute at the North Carolina State University. It took approximately a decade to complete the development of the software, which was finally launched in 1976. The software was further upgraded in 1980s with addition of various statistical procedures and new components like JMP. The purpose for developing SAS was to perform advanced analytics, criminal investigation, data management, multivariate analysis, predictive analysis, business intelligence estimations, and so on. The software was updated to its 9th version in 2004 when a click and point interface was added. A product for social media analysis was incorporated in 2010. SAS is supported by diverse computing environments and it works with ease on all platforms and operating systems. Its latest version is SAS 9.4.
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Advanced Data Analysis Abilities: SAS is a complete package for data analysis; it can work with the simplest statistical analysis to highly complex analysis which include plotting graphs from given data sets to finding correlation between different complex data sets. SAS comes with a wide array of functionalities saved in the SAS library and with these inbuilt packages, data can be analysed and reports can be created without extensive manual effort.
SAS is a 4th Generation programming language: As a 4th generation programming language, SAS has reduced the amount of manual coding required for common applications with its pre-built libraries. In addition, the codes are written in a statement-like language with easy syntax which act as precise instructions for the system. These features modularize the job for the users, thereby making SAS a convenient programming language for users who are not experts in coding. Besides, SAS is an interactive language. It comes with a log window that provides continuous instruction to users while programming and errors and notes are marked out in this window. The DS2 features is effective for data manipulation-complex data can be manipulated directly where it is located in the database.
SAS Studio: With this feature it becomes easy to access and use SAS from any web browser. All data files and libraries can be accessed from any of the commonly used web browsers which eliminates the need for client installation. Autocomplete attributes of SAS prompts with different instructions and procedures when one starts typing. A parameter list along with a pop-up syntax are also present for guiding the users along the way. Users have the choice to create their own individual snippets of code and add them to the snippet library, thereby customizing the way they work. There is also a point and click interface which reduces the need to go through detailed typing or manual effort, saving time along the way.
SAS Management: The management feature of SAS works well for surveillance since the SAS environment manager monitors, manages, and alerts the users about the analytics environment. Extended Java GUI functions to administer SAS tasks in SAS management console. The XML engine functions to import and export XML documents and to create XML maps. With its advanced management features, SAS offers scope for executing failed programs in restart mode where the program would resume from the point where it failed. There is also an Application Response Measurement interface which checks different applications to figure out if there is scope for transaction.
Data Encryption: SAS 9.4, which is the latest version of SAS at the this time, comes with a security feature called SAS/SECURE that sustains security for all data that is accessed through or used for analysis in SAS, irrespective of the ways in which they are accessed. It also provides the facility to encrypt SAS data separately on disks using the relevant algorithms.
SAS supports different types of data formats: One of SAS’s advanced abilities is that it can read data from any source and file, no matter what format the data is saved in. SAS can also work with files that contain missing data, which provides great assistance to users when they do not have access to complete data for analysis. SAS effectively fills in the gap and provides highly accurate analysis. Apart from this, SAS is dependent on SQL and it has a large database of character encoding, as a consequence of which SAS is able to support most of the popularly used languages. Code singularity is maintained which further ensures that SAS can work with data in several languages.
Various Formats for reporting output: SAS allow for reports to be created and saved in different formats including PDF, PowerPoint, RTF, e-book, I-book, and several others. While reporting outputs, SAS can use high quality graphics facilitated by ODS Graphics Designer and Editor, ODS Statistical Graphics, and others. Output of reports can be customized depending upon the requirements of customers and SAS provides users with the choice to share reports across different platforms.
Easy to learn: SAS syntax is one of the easiest to learn since commands are in the form of simple statements and coding in SAS is like providing the computer with instructions in common human language. This particularly works in favour of users who do not have a strong base in programming languages. The fact that SAS can deal with huge databases easily makes it ideal for complex statistical analysis by researchers without a background in coding.
Convenient to Debug: When there is an error in programming, it is quite easy to locate the problem and debug the code since commands are like simple statements. In addition to this, the log window is helpful in pointing out errors to the users, reducing the time taken to find out problems.
SAS GUI: Graphical User Interface of SAS is quite advanced which makes it ideal for visual representation of reports. Users have access to an extensive library and tools for graphs and plots, enhancing the visual quality of reports and making statistical computing trouble-free.
Data Security: SAS offers complete security for data which cannot be extracted without appropriate license, neither can it be manipulated. Provided that SAS is closed source, companies’ data remain completely protected, which is why it is preferred by big companies over other open-source alternatives like R.
SAS Customer Support: SAS provides easily accessible customer support to users since it is closed source and hence under the absolute control of the owning company. It cannot be edited or adulterated by external parties which makes it secure and reduces threats.
Knowledge of SAS improves job prospects: SAS is one of the most popularly used programming languages and one needs to possess knowledge of SAS before stepping into the analytics industry. Knowing SAS makes it easy for programmers to learn other statistical languages like R and python, and not being familiar with SAS can work to their disadvantage when applying for jobs.
Rigorously evaluated algorithms and highly accurate results: Algorithms which are implemented in SAS are rigorously tested, analysed, and verified by SAS developers, and this is possible precisely because SAS is a closed-source software. As a consequence, users can count on SAS to generate accurate outcomes which are properly formatted and very easy to comprehend.
Scope for reading and incorporating data: SAS is capable of reading data which have been created by other statistical packages, and as a consequence data files which have been created by Stata, SPSS, Excel, Minitab, Systat and other softwares can be included either directly in SAS programs or through file conversion software. This is a notable advantage since SAS can allow users to assimilate data from multiple statistical packages, broadening users’ access to reliable information.
Advanced Data Analysis: SAS is a statistical software of great potential and flexibility, and therefore it can perform all sorts statistical analysis including prescriptive, descriptive, inferential, predictive, and others. SAS is ideal for data analysis where forecasting trends is the objective. SAS is an integrated system in which all modules and procedures share a similar architecture. Thus, once users master one module, they can apply their knowledge to other modules, which makes the whole method of analysing quite smooth for users.
Predictive Analysis Homework Help: Predictive analysis is used by all big companies and since SAS is good with analysis that involves forecasting, many business giants including Google, Facebook, Netflix, and Twitter depend on SAS for predictive analysis. Predictive analysis incorporates statistical analysis along with data and machine learning to make predictions about future based on historical and present data. For instance, predictive analysis can be used by companies to estimate future trends in sale or demand of a particular product based on its past performance. Likewise, possible market trends can be detected with predictive analysis, and SAS ensures the accuracy of reports yielded. Apart from business tycoons, SAS is also used by government organizations and researchers to predict sociological and economic issues like growth in population, GDP curve in the coming years.
Clinical Research and Forecasting Homework Help: SAS plays a crucial role in the medical field with multiple applications; SAS is used to predict whether a patient with given health conditions is at risk or not, and whether they can safely undergo certain treatments. SAS is employed in diagnostic decision-making systems, and it helps with accurate decision making as is required during prognosis or diagnosis.
Business Intelligence Homework Help: Every business organization needs to analyse data related to business operations the reports of which are used by senior members to make crucial decisions regarding the business. For instance, before making a new investment, a business enterprise would be likely to make analysis of the risks involved and the prospects of returns. Based on the reports would the senior officers decide whether they want to make the investment or not. This information, which is regarded as business intelligence, can be derived through statistical softwares and SAS is useful in this regard because of its practical analytic features that offer understanding regarding historical, current, and predictive views of a business’ working.
Multivariate Analysis Homework Help: SAS is well-suited for multivariate analysis because of which it is popular among researchers in different fields. In multivariate analysis, multiple elements or variables are studied together to find the existing relationships between them, if there is any. SAS can perform such analysis of multiple variables simultaneously to find correlations without categorising them either as independent or dependant. Studies can be conducted using SAS to find the impact of different variables on the same result, and it can include different regressions in the process.
SAS and SPSS are two of the most popularly used platforms for statistical analysis but there exist some key differences between them which help users to choose one over the other depending on their needs. (Click here for SPSS Homework Help.)
Here are the major differences:
SPSS is a great tool for people who are not experts in the field of statistical studies. It comes with numerous drop-down menus and has a user-friendly interface. SPSS is mostly used for research purposes in fields of social sciences. SAS comes stocked with an enormous quantity of production codes which can be applied to multiple fields, and in research as well as industries. Compared to SPSS, it receives more preference in fields of commercial analytics. Corporates depend on it for its high rate of accuracy, which is a product of its well-tested algorithms, ensured by constant software updates that it receives in a closed environment.
SPSS has limited capability when it comes to handling data and can only work when the data size is below 100 MB. SAS, on the other hand, is equipped to deal with Big Data from diverse sources. When data is present in gargantuan quantities, SAS provides the facilities to sort and splice through the data.
In order to use SPSS, researchers do not need to learn coding at all. SPSS comes with a paste features that generates syntax for carrying out the steps executed in user interface. SAS is quite easy to learn but some amount of pre-existing knowledge on the users’ part make it more convenient to use. SAS uses Proc SQL, therefore, people who are already familiar with SQL find it much easier to use it; people who are not already familiar can also learn SAS with minimal effort.
SPSS is expensive, and users are required to buy different licenses for using different functionalities. In addition to this, it has limited syntax and is rather slow in adapting new technologies. SAS is also quite costly, but it comes with a yearly license. Comparisons show that on an average, SAS might be 1.7 times costlier than SPSS, but it provides much better functionalities, starting with the fact that there is no limit to the amount of data SAS can handle. It also offers multiple programs for data mining and visualization.
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