Statistical Methods for Engineers

G. Geoffrey Vining, Scott Kowalski

ISBN 10: 053873518X / ISBN 13: 9780538735186

Edité par Cengage Learning, 2010

A propos de cet article

[ No Hassle 30 Day Returns ][ Ships Daily ] [ Underlining/Highlighting: NONE ] [ Writing: NONE ] [ Edition: third ] Publisher: Duxbury Press Pub Date: 1/1/2010 Binding: Hardcover Pages: 618 third edition.

N° de réf. du vendeur 6916928

Détails bibliographiques

• Titre: Statistical Methods for Engineers
• Éditeur: Cengage Learning
• Date de parution: 2010
• Langue: anglais
• ISBN à 10 chiffres: 053873518X
• ISBN à 13 chiffres: 9780538735186
• Reliure: Couverture rigide
• Édition: 3ème Édition
• État de l'article: Good

A propos de ce titre

Synopsis

Book by Vining G Geoffrey Kowalski Scott

Présentation de l'éditeur

STATISTICAL METHODS FOR ENGINEERS, 3e, International Edition offers a balanced, streamlined one-semester introduction to Engineering Statistics that emphasizes the statistical tools most needed by practicing engineers. Using real engineering problems with real data based on actual journals and consulting experience in the field, students see how statistics fits within the methods of engineering problem solving. The text teaches students how to think like an engineer at analyzing real data and planning a project the same way they will in their careers. Case studies simulate problems students will encounter professionally and tackle on long-term job projects. The presentation makes extensive use of graphical analysis, and use of statistical software is encouraged for problem-solving to illustrate how engineers rely on computers for data analysis. The authors relate their own extensive professional experience as engineers in short margin notes called Voice of Experience that lend valuable context to how students will apply concepts in the field and why they're important to learn. And a rich companion website provides hours of multimedia lecture presentation narrated by the authors to show the material related live by different voices, simulating how students will listen and learn from multiple colleagues in their jobs.