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There are four fundamental random number functions: `rand`, `randi`, `randn`, and `randperm`.
The `rand` function returns real numbers between
0 and 1 that are drawn from a uniform distribution. For example,

r1 = rand(1000,1);

`r1` is
a 1000-by-1 column vector containing real floating-point numbers drawn
from a uniform distribution. All the values in `r1` are
in the open interval, (0, 1). A histogram of these values is roughly
flat, which indicates a fairly uniform sampling of numbers.

The `randi` function returns `double` integer
values drawn from a discrete uniform distribution. For example,

r2 = randi(10,1000,1);

`r2` is
a 1000-by-1 column vector containing integer values drawn from a discrete
uniform distribution whose range is 1,2,...,10. A histogram of these
values is roughly flat, which indicates a fairly uniform sampling
of integers between 1 and 10.

The `randn` function returns arrays of real
floating-point numbers that are drawn from a standard normal distribution.
For example,

r3 = randn(1000,1);

`r3` is
a 1000-by-1 column vector containing numbers drawn from a standard
normal distribution. A histogram of `r3` looks like
a roughly normal distribution whose mean is 0 and standard deviation
is 1.

You can use the `randperm` function to create
arrays of random integer values that have no repeated values. For
example,

r4 = randperm(15,5);

`r4` is
a 1-by-5 array containing randomly selected integer values on the
closed interval, [1, 15]. Unlike `randi`, which
can return an array containing repeated values, the array returned
by `randperm` has no repeated values.

Successive calls to any of these functions return different results. This behavior is useful for creating several different arrays of random values.

`rand` | `randi` | `randn` | `randperm`

- Random Numbers within a Specific Range
- Random Integers
- Random Numbers from Normal Distribution with Specific Mean and Variance

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