The aim of this book is to provide, firstly, an introduction to probability and
statistics especially directed to the metrology and testing fields and secondly,
a comprehensive, newer set of modelling methods for data and uncertainty
analysis that are generally not considered yet within mainstream methods.
The book brings, for the first time, a coherent account of these newer methods
and their computational implementation. They are potentially important
because they address problems in application fields where the usual hypotheses
that are at the basis of most of the traditional statistical and probabilistic
methods, for example, relating to normality of the probability distributions,
are frequently not fulfilled to such an extent that an accurate treatment of the
calibration or test data using standard approaches is not possible. Additionally,
the methods can represent alternative ways of data analysis, allowing a
deeper understanding of complex situations in measurement. The book lends
itself as a possible textbook for undergraduate or postgraduate study in an
area where existing texts focus mainly on the most common and well-known
methods that do not encompass modern approaches to calibration and testing
problems.