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Page 120
... standard deviation is 1.414 . The significance of the standard deviation is that it is a measure of the degree to which the data are dispersed . This is best seen from Eq . ( 6-5 ) which defines the standard deviation for discrete data ...
... standard deviation is 1.414 . The significance of the standard deviation is that it is a measure of the degree to which the data are dispersed . This is best seen from Eq . ( 6-5 ) which defines the standard deviation for discrete data ...
Page 121
Geoffrey Gordon. Because of the square root occurring in the definition , the standard deviation has the same dimensionality as the observations . It can therefore be compared directly with the mean value . The ratio of the standard ...
Geoffrey Gordon. Because of the square root occurring in the definition , the standard deviation has the same dimensionality as the observations . It can therefore be compared directly with the mean value . The ratio of the standard ...
Page 159
... standard deviation S : 50 SUBROUTINE GAUSS ( S , AM , V ) A = 0.0 DO 50 I = 1,12 CALL RANDOM ( INT , REAL ) A = A + ... standard deviation of 1. Other methods are given in ( 3 ) and ( 11 ) . If the inverse transformation method is used ...
... standard deviation S : 50 SUBROUTINE GAUSS ( S , AM , V ) A = 0.0 DO 50 I = 1,12 CALL RANDOM ( INT , REAL ) A = A + ... standard deviation of 1. Other methods are given in ( 3 ) and ( 11 ) . If the inverse transformation method is used ...
Contents
SYSTEM STUDIES | 21 |
3 | 35 |
CONTINUOUS SYSTEM SIMULATION | 58 |
Copyright | |
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activity aircraft analog computer assumed attributes autocovariances block diagram block type busy call length changes clock coefficient column conditional events confidence interval cumulative distribution cumulative distribution function curve defined denoted derived described differential equation discrete system distributed random numbers Erlang distribution estimate event notice event routine events chain example executed exponential distribution exponential growth field Figure given GPSS illustrated in Fig initial input inspector integral inter-arrival logic switch mathematical mean value measure method Monte Carlo method needed normal normally distributed number of entities occur output parameter permanent entities pointer Poisson distribution priority probability density function problem produce queue random variable record represent sample server utilization shown in Fig SIMSCRIPT simulation run specific standard deviation statistics stochastic storage System Dynamics system simulation Table TABULATE telephone system temporary entities tion transaction uniformly distributed zero