Introduction to turbulence/Statistical analysis/Multivariate random variables
Joint pdfs and joint moments
Often it is importamt to consider more than one random variable at a time. For example, in turbulence the three components of the velocity vector are interralated and must be considered together. In addition to the marginal (or single variable) statistical moments already considered, it is necessary to consider the joint statistical moments.
For example if and are two random variables, there are three second-order moments which can be defined , , and . The product moment is called the cross-correlation or cross-covariance. The moments and are referred to as the covariances, or just simply the variances. Sometimes is also referred to as the correlation.
In a manner similar to that used to build-up the probabilility density function from its measurable counterpart, the histogram, a joint probability density function (or jpdf), , can be built-up from the joint histogram. Figure 2.5 illustrates several examples of jpdf's which have different cross correlations. For convenience the fluctuating variables and can be defined as
where as before capital letters are usd to represent the mean values. Clearly the fluctuating quantities and are random variables with zero man.
A psitive value of indicates that and tend to vary together. A negative value indicates
The bi-variate normal (or Gaussian) distribution