`RandomDataImpl`

is created, the underlying
- random number generators are `reSeed()`

method
initializes the appropriate generator. If you do not explicitly seed the
generator, it is by default seeded with the current time in milliseconds.
@@ -176,15 +176,15 @@
however, generating them is much more difficult. The
org.apache.commons.math.CorrelatedRandomVectorGenerator class
- provides this service. In this case, the user must set a complete covariance matrix
- instead of a simple standard deviations vector, this matrix gather both the variance
+ provides this service. In this case, the user must set up a complete covariance matrix
+ instead of a simple standard deviations vector. This matrix gathers both the variance
and the correlation information of the probability law.
The main use for correlated random vector generation is for Monte-Carlo simulation of physical problems with several variables, for example to generate error vectors to be added to a nominal vector. A particularly - interesting case is when the generated vector should be drawn from a Multivariate Normal Distribution.

@@ -226,7 +226,7 @@ To select a random sample of objects in a collection, you can use the`nextSample`

method in the `RandomData`

interface.
Specifically, if `c`

is a collection containing at least
- `k`

objects, and `ranomData`

is a
+ `k`

objects, and `randomData`

is a
`RandomData`

instance `randomData.nextSample(c, k)`

will return an `object[]`

array of length `k`

consisting of elements randomly selected from the collection. If