Require 'rubystats' adult_male_height = Rubystats :: NormalDistribution. To generate better random numbers for such use cases, you can use the rubystats gem. Other examples of things that follow a Normal Distribution are: ![]() You don't want the probability of getting an 8'9" man to be the same as getting a 6' man, because the latter is more common. If you want to generate data to simulate the height of men in a population, you might not want to use rand with these limits. The shortest height recorded is 54.6 cm (21.5 inches) while the tallest is 267 cm (8'9"). Let's take an adult man's height as an example. Mostly, a majority of the data tends to fall within a smaller range, with a smaller percentage falling within the larger range. If you have a range of values that something falls under, rarely do you get an equal distribution of all the values. In the real world, many things tend to follow a Normal Distribution. ![]() Generating Random Numbers Based on Normal Distribution Unlike Kernel#rand, if Random.rand is given a negative or 0 argument, it raises an ArgumentError. It could also help in isolating or reproducing bugs.īelow we use srand to set the seed and then call rand first to produce a couple of individual random numbers and then to produce a couple sequences of random numbers. It could be handy for testing code in your app that deals with randomness, with values that are random but still predictable enough to test. This is what Kernel#srand allows us to do. If you ran theĪlgorithm with the same seed, then you would get the same sequence of Sequence of numbers, thus the resulting random number output. The seed number will always be different, which would produce a different timestamp, the process ID of the program, e.t.c.īecause these elements vary for each request to generate a random number, The seed number is generated by the computer using a combination ofĭifferent elements, e.g. Through some algorithm and then spits out a seemingly random output. To do this, the computer starts with a seed number, which it runs What they do is generate a sequence of numbers that seem ![]() As stated earlier, computers don't generate truly random numbers purely fromĬomputation.
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