Add to ORKGRandom numbers are essential ingredients in a statistical analysis, They can be generated easily through statistical software packages, Each statistical package varies in terms of performance. Our objective is to compare the performances of three statistical software packages, namely R, SAS and SPSS based on their accurateness and time consumption in generating and analyzing random numbers. We obtain some estimated statistics to assess their accuracies. Comparison of the times in generating the random numbers is also observed
Includes bibliographical references (pages 91-93)This paper is an examination of the generation of r...
Simulation techniques are a core component of scientific computing and, more specifically, compu-tat...
The most effective cryptographic algorithm has more randomness in the numbers a generator generates,...
Abstract: A variety of software packages, such as Excel, Access, ACL, and RAT-STATS are used in bus...
Random number generators (RNGs) are widely used in conducting Monte Carlo simulation studies, which ...
In many applications, for example cryptography and Monte Carlo simulation, there is need for random ...
Winner, ScienceGood random number generators (RNGs) are required for many applications in science an...
In study of the statistical packages, simulation from probability distributions is one of the import...
The increasing popularity and complexity of random number intensive methods such as simulation and b...
The term random number has been used by many scholars to explain the behaviour of a stochastic syste...
Statistics has always benefited from Users of statistics have always tried to ease the burden of co...
Practical methods for generating acceptable random numbers from a variety of probability distributio...
As simulation arid Monte Carlo continue to play an increasing role in statistical research, careful ...
Random Numbers determine the security level of Cryptographic Applications as they are used to genera...
Is it possible to determine what randomness is let alone measure and classify it? Can random number ...
Includes bibliographical references (pages 91-93)This paper is an examination of the generation of r...
Simulation techniques are a core component of scientific computing and, more specifically, compu-tat...
The most effective cryptographic algorithm has more randomness in the numbers a generator generates,...
Abstract: A variety of software packages, such as Excel, Access, ACL, and RAT-STATS are used in bus...
Random number generators (RNGs) are widely used in conducting Monte Carlo simulation studies, which ...
In many applications, for example cryptography and Monte Carlo simulation, there is need for random ...
Winner, ScienceGood random number generators (RNGs) are required for many applications in science an...
In study of the statistical packages, simulation from probability distributions is one of the import...
The increasing popularity and complexity of random number intensive methods such as simulation and b...
The term random number has been used by many scholars to explain the behaviour of a stochastic syste...
Statistics has always benefited from Users of statistics have always tried to ease the burden of co...
Practical methods for generating acceptable random numbers from a variety of probability distributio...
As simulation arid Monte Carlo continue to play an increasing role in statistical research, careful ...
Random Numbers determine the security level of Cryptographic Applications as they are used to genera...
Is it possible to determine what randomness is let alone measure and classify it? Can random number ...
Includes bibliographical references (pages 91-93)This paper is an examination of the generation of r...
Simulation techniques are a core component of scientific computing and, more specifically, compu-tat...
The most effective cryptographic algorithm has more randomness in the numbers a generator generates,...