normal
Density 

Mean 

Variance 

Mode 

The Normal distribution is an unbounded continuous distribution. It is
sometimes called a Gaussian distribution or the bell curve. Because of
its property of representing an increasing sum of small, independent
errors, the Normal distribution finds many, many uses in statistics. It
is wrongly used in many situations. Possibly, the most important test
in the fitting of analytical distributions is the elimination of the
Normal distribution as a possible candidate.
The Normal distribution is used as an approximation for the Binomial
distribution when the values of n, p are in the appropriate range. The
Normal distribution is frequently used to represent symmetrical data,
but suffers from being unbounded in both directions. If the data is
known to have a lower bound, it may be better represented by suitable
parameterization of the Lognormal, Weibull or Gamma distributions. If
the data is known to have both upper and lower bounds, the Beta
distribution can be used, although much work has been done on truncated
Normal distributions.
Sample
sigma =1; mean = 0

normal(double
sigma,
double mean)

Description
Generates a sample of the Normal distribution.
Parameters
Name 
Type 
Description 
sigma 
double 
the shape
parameter = standard deviation 
mean 
double 
the shift
parameter = mean value 
Result
Type 
Description 
double 
the
generated sample 

normal(double
sigma)

Description
Generates a sample of the Normal distribution with mean set
to
0. Is equivalent to normal(sigma, 0).
Parameters
Name 
Type 
Description 
sigma 
double 
the shape
parameter = standard deviation 
Result
Type 
Description 
double 
the
generated sample 

normal()

Description
Generates a sample of the Normal distribution with mean set
to
0 and sigma set to 1. Is equivalent to normal(1, 0).
Result
Type 
Description 
double 
the
generated sample 

normal(double
sigma,
mean,java.util.Random r)

Description
Generates a sample of the Normal distribution using the
specified random number generator.
Parameters
Name 
Type 
Description 
sigma 
double 
the shape
parameter = standard deviation 
mean 
double 
the shift
parameter = mean value 
r 
java.util.Random 
the random
number generator 
Result
Type 
Description 
double 
the
generated sample 