Arnold Applications of Statistics S.
2 total works
Best practice in engineering involves the use of modern statistical techniques. This volume brings these techniques together, and the emphasis is on modelling. The topics include: random number generation, reliability and quality assurance; univariate and multivariate extreme value distributions; regression methods; Bayesian methods; time series analysis; Markov chains and stochastic dynamic programming; variograms and kriging; spectral analysis and wavelets; and design of experiments. The methods are introduced with practical examples, drawn from research and consultancy experience, and the underlying mathematical concepts are clearly explained in an informal manner. Readers are given enough information to write their own programs, but references to useful commercial software are also provided. A brief summary of a typical first course in statistics is included as an appendix.
This work introduces senior level students of management science and practising managers to the modern powerful statistical methods that are available for optimising and improving business processes. Using realistic case studies from business, the book explains the key statistical concepts for postgraduate management courses in statistical methods. In particular, the authors focus on the use of probabilistic methods and statistical analysis in a business context and the value of these methods for managers.