MTU Library Catalogue

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Statistics in engineering : a practical approach / Andrew V. Metcalfe.

By: Metcalfe, Andrew V.
Material type: materialTypeLabelBookSeries: Texts in statistical science.Publisher: London : Chapman & Hall, 1994Description: x, 446 p. ; 23 cm. + pbk.ISBN: 0412492202 .Subject(s): Engineering -- Statistical methodsDDC classification: 519.202462
Contents:
Why understand 'statistics'? -- Probability in engineering decisions -- Justifying engineering decisions -- Modelling variability -- Combining variables -- Precision of estimates -- Asset management plan -- Making predictions from one variable -- Making predictions from several explanatory variables -- Design of experiments -- Modelling variability in time and space.
Holdings
Item type Current library Call number Copy number Status Barcode
General lending MTU Bishopstown Library Lending 519.202462 (Browse shelf(Opens below)) 1 Available 00007738
Total holds: 0

Enhanced descriptions from Syndetics:

Statistics in Engineering provides a succinct introduction to statistics. The ideas are introduced with examples set in their practical context. The underlying mathematics are given in an informal way and are included for those who find that mathematical justification helps their understanding of concepts, and for anyone who needs to take the subject further The author indicates sections that can be omitted without any loss of continuity. The book is kept as simple as possible, and assumes only some familiarity with elementary calculus and matrices.

The first seven chapters of the book cover a typical 40-hour statistics module taken by engineering or science students who are beginning the subject. This includes the basic ideas, relationships between variables, and the design and analysis of experiments. The final chapter looks at some important engineering situations that are not fully covered by the methods of the preceding chapters.

Includes bibliographical references (pages 433-437) and index.

Why understand 'statistics'? -- Probability in engineering decisions -- Justifying engineering decisions -- Modelling variability -- Combining variables -- Precision of estimates -- Asset management plan -- Making predictions from one variable -- Making predictions from several explanatory variables -- Design of experiments -- Modelling variability in time and space.