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SAS refers to a statistical software application which is used for storing, analyzing and modifying data. SAS is known as Statistical Analytical System which is used to carry out the information entry, management, retrieval, and analytical analysis. It is compatible to any computer environment,.
Import and export raw data files
Manipulate and transform data
Combine SAS data sets
Create basic detail and summary reports using SAS procedures.
Identify and correct data, syntax and programming logic errors.
We help with SAS Studio, Base SAS, SAS/STAT, SAS/IML, SAS/ACCESS and several time series forecasting procedures from SAS/ETS.
SAS is used for :
- Create multiple libraries
- Retrieve data easily
- Generate pictures and slick reports
- Develop new software application
- Examine data statistically and mathematically
- Make and plan decision for business development
- Enhance the process quality
SAS is platform independent software, this quality of SAS allow us to run it on any operating system such as Linux, windows, etc. Some main features of SAS software are given below:
- Fast and flexible
- Platform independent
- Provide accurate data
- Provide perfect clarity in work
SAS acts as an efficient resource of analytical solution to data, it allows SAS programmers to perform various functions given as:
- Data mining, statistical analysis and econometrics
- Data management and retrieval of knowledge
- Operation management and research of analytical jobs
- Remote calculation, development of analytical and statistical applications
- Plays an important role in forecasting and business preparation decisions
To manipulate data for SAS procedures, SAS language includes a programming language and this is known as SAS programming. A programmer can perform the typical task in SAS by identification of data. SAS contains a SQL procedure also, it allows SQL programmer to perform the task without extra knowledge. By providing solution to analytical problems, SAS programming help us in achieving enterprise goals. SAS programming comes with its different components that can be installed according to the requirements.
SAS programming mainly involves main three sections:
- Data steps: in this section, we identify the variables of data sets and load the required data into SAS memory. Data step also identifies the name of the SAS data sets, logic for manipulating the data and format of the input and output data.
- Proc steps: to analyze the data, we invoked a SAS built-in procedure. It performs complex operations on data sets.
- Output steps: this section involves the presentation of conditional output statements with data.
Statistical computing is a major concept in SAS which act as an interface between computer science and statistics. Nowadays, statistical computing become necessary for statistician as it performs the multiple tasks and enables us to do work with large data volumes. Statistical computing performs following operations:
- Operating research
- Statistical programming
- Improvement of statistical quality
- Statistical visualization
- Matrix programming
SAS is widely used software which comes with the wide range of applications. Applications of SAS are as follows:
- Financial risk analysis
- Business planning and intelligence
- Data warehousing
- Sales forecasting
- Pharmaceutical analysis
- IT, project & financial management
- Human resources and customer relationship management
The latest version of SAS software is SAS 9.4 which includes the high performance features. SAS 9.4 provide its benefit for SAS programmers, security administrators, business users, IT departments and statisticians. To speed up the processing, it breaks the analytic task into pieces. Some other versions of SAS are listed below:
- SAS 79.3 to 82.4
- SAS Version 4 series
- SAS Version 6 series
- SAS Version 7 series
- SAS Version 8 series
- SAS Version 9 series
SAS has released a wide range of its products. Some of its products are given below:
- SAS Enterprise Miner: this product synchronizes the data mining process in a specific manner to create predictive and descriptive models.
- SAS Model Manager: this product is specialized in creating, validating, deploying and managing analytical models.
- SAS Factory Miner: this product can easily build and retain numerous predictive models across multiple segments.
- SAS Scoring Accelerator: this product is beneficial to improve the model performance and produce the faster output.
- SAS Forecasting for Desktop: to create an effective plan, it generates the forecasts automatically.
- SAS Text Miner: it creates patterns in the collections of documents by mining unstructured data sources using unsupervised, semi supervised and supervised techniques.
- SAS Forecast Server: to improve decisions and planning, it produces forecasts automatically and quickly.
- SAS Visual Data Mining and Machine Learning: it helps us to solve complex programs faster.
- SAS Scoring Accelerator: to improve model performance, it automates data scoring processes into database.
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SAS System integrates with many SAS business solutions that enable large scale software solutions for areas such as human resource management, financial management,business intelligence, customer relationship management , The SAS System (originally Statistical Analysis System) is an integrated system of software products provided by SAS Institute that enables the programmer to perform: data entry, retrieval,management, and mining report writing and graphics, Statistical and mathematical analysis,business planning, forecasting, and decision support, operations research and project management, quality improvement, applications development, data warehousing (extract , transform, load), platform independent and Remote computing.
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- Base SAS - data management and basic procedures,SAS/STAT - statistical analysis ,SAS/GRAPH - presentation quality graphics,SAS/OR - Operations research,SAS/ETS - Econometrics and Time Series Analysis,SAS/IML - interactive matrix language,SAS/AF - applications facility (menus and interfaces),SAS/QC - quality control,Good file hygiene,organization,documentation,labels,Bringing up SAS.,Navigating the windows
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Topics include Variable Selection,Transformation of Variables ,Predicting Attrition Models for Ordinal Targets: Predicting,Accident Risk More on errors and error detection Estimation of Weights in a Neural Network Model, A Neural Network Model to Predict Response.
- Regression Concepts, Simple and Multiple Regression , Regression Analysis, Examining Data, Simple linear regression, Multiple regression, Regression Diagnostics , Unusual and Influential data, Tests on :, Normality of Residuals , Nonconstant Error of Variance , Multicollinearity & Nonlinearity, Model Specification, Regression with Categorical Predictors
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