- SAS Tutorial
- SAS - Home
- SAS - Overview
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- SAS - Decision Making
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- SAS - Read Raw Data
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- SAS - Concatenate Data Sets
- SAS - Merging Data Sets
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- SAS - SQL
- SAS - Output Delivery System
- SAS - Simulations
- SAS Data Representation
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- SAS Basic Statistical Procedure
- SAS - Arithmetic Mean
- SAS - Standard Deviation
- SAS - Frequency Distributions
- SAS - Cross Tabulations
- SAS - T Tests
- SAS - Correlation Analysis
- SAS - Linear Regression
- SAS - Bland-Altman Analysis
- SAS - Chi-Square
- SAS - Fishers Exact Tests
- SAS - Repeated Measure Analysis
- SAS - One-Way Anova
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SAS - Simulations
Simulation is a computational technique that uses repeating computation on many different random samples in order to estimate a statistical quantity. Using SAS we can simulate complex data that have specified statistical properties in real-world system. We use software to build a model of the system and numerically generate data that you can be used for a better understanding of the behavior of the real-world system. Part of the art of designing a computer simulation model is deciding which aspects of the real-life system are necessary to include in the model so that the data generated by the model can be used to make effective decisions. Because of this complexity, SAS has a dedicated software component for Simulation.
The SAS software component which is used in creating SAS simulation is called SAS Simulation Studio. Its graphical user interface provides a full set of tools for building, executing, and analyzing the results of discrete event simulation models.
Different types of statistical distributions on which SAS simulation can be applied is listed below.
- SIMULATE DATA FROM A CONTINUOUS DISTRIBUTION
- SIMULATE DATA FROM A DISCRETE DISTRIBUTION
- SIMULATE DATA FROM A MIXTURE OF DISTRIBUTIONS
- SIMULATE DATA FROM A COMPLEX DISTRIBUTION
- SIMULATE DATA FROM A MULTIVARIATE DISTRIBUTION
- APPROXIMATE A SAMPLING DISTRIBUTION
- ASSESS REGRESSION ESTIMATES