ABSTRACT
The chapter is introduced with the estimation of the mean and variance of clearances given sample means and variances of the components. This is followed by the definition of sample covariance and correlation with numerous examples that reinforce the concepts that: correlation is a measure of linear association; association does not in itself imply causation; and no linear association does not necessarily imply independence. Bivariate probability distributions are then introduced and population covariance and correlation are defined. The next section covers the mean and variance of a linear combination of random variables, propagation of error, and leads to a statement of the Central Limit Theorem – a proof is given on the website. The final section deals with propagation of error for non-linear functions. The chapter ends with: a summary of: notation used; the main results, MATLAB and R syntax; and exercises.
