Random Functions

random( )

A random number between 0 and 1 drawn from a uniform distribution.

random(max)

A random (real) number between 0 and max drawn from a uniform distribution.

random(min, max)

A random (real) number between min and max drawn from a uniform distribution: random(-10, 10) returns a number between –10 and 10.

randomInteger(min, max)

A random integer between its two arguments, inclusive. With no arguments, it returns either 0 or 1 with equal probability. With one argument, it returns an integer between 0 and the value of the argument: randomInteger(1, 6) gives 1, 2, 3, 4, 5, or 6, chosen at random, such that each integer has equal probability.

randomPick(a1, a2, … )

Gives an element randomly chosen from a list of any number of arguments:

randomPick(1, 2, 3, 4, 5, 6) makes a die.
randomPick("heads", "tails") makes a coin.
randomPick("Male", "Male", "Female") gives you a population that is two-thirds male.

randomBinomial(n, p)
n = number of trials
p = probability of success

Gives a random integer from a binomial distribution: randomBinomial(20, 0.5) gives the number of heads in 20 tosses of a fair coin.

Two optional arguments provide a minimum and maximum value:
randomBinomial(5,0.5,1,2) draws numbers from a binomial distribution and scales them so that the possible results are 1.0, 1.2, 1.4, 1.6, 1.8, and 2.0.

randomNormal(mu, sd)
mu = the mean
sd = standard deviation

A random real number pulled from a normal distribution. For example, randomNormal(0, 1) gives a number from a distribution with a mean of 0 and a standard deviation of 1.

randomGeometric(p)
p = the probability of

a “catch.”

Must be between 0 and 1.

A random nonnegative integer from a geometric distribution. Think of the result as the number of repetitions of some event before the result is positive given that the probability of that positive outcome is p.

Example: randomGeometric(0.5) generates the distribution of additional flips of a coin necessary to get a head. Two additional parameters to the function help here. randomGeometric(p, scale, min) has possible values min, min + scale, min + 2*scale, and so on.

So, randomGeometric(0.5, 1, 1) generates the distribution of the number of coin flips needed to get a head.

randomExponential(mu)
mu = the mean

and must be positive.

A random real number greater than zero, pulled from a distribution that declines exponentially (so there are more near zero, just as in the geometric distribution): randomExponential(5)min would be for simulating times between customers when the average time between customers is known to be 5 minutes.

A second, optional argument specifies the minimum value returned. randomExponential(mu, min) will have a mean of mu + min.