Introduction to Probability and Statistics for Engineers and Scientists - Tapa blanda

Acerca del autor

Dr. Sheldon M. Ross is a professor in the Department of Industrial and Systems Engineering at the University of Southern California. He received his PhD in statistics at Stanford University in 1968. He has published many technical articles and textbooks in the areas of statistics and applied probability. Among his texts are A First Course in Probability, Introduction to Probability Models, Stochastic Processes, and Introductory Statistics. Professor Ross is the founding and continuing editor of the journal Probability in the Engineering and Informational Sciences. He is a Fellow of the Institute of Mathematical Statistics, a Fellow of INFORMS, and a recipient of the Humboldt US Senior Scientist Award.

De la contraportada

Introduction to Probability and Statistics for Engineers and Scientists, now in its trusted Sixth Edition, provides a valuable overview of the subject written in the engaging style of Best-Selling Probability expert and author, Sheldon Ross. The book uniquely emphasizes how probability informs statistical problems to develop an intuitive understanding of the statistical procedures most commonly used by practicing engineers and scientists. Utilizing real data from actual studies across life science, engineering, computing, and business, this useful introduction supports reader comprehension through a wide variety of exercises and examples throughout the text. These examples and exercises are combined with updated problem sets and applications to connect probability theory to everyday statistical problems and situations.

The book also contains end of chapter review material that highlights key ideas as well as the risks associated with practical application of the material. In the Sixth Edition, new coverage includes information on big data and the use of R.

is intended for upper-level undergraduate and graduate students taking a probability and statistics course in engineering programs as well as those across the biological, physical, and computer science departments; it is also appropriate for scientists, engineers, and other professionals seeking a reference of foundational content and application to these fields.

Key features:

• Provides the author’s uniquely accessible and engaging approach, tailored for the needs of engineers and scientists
• Features real data examples that use significant real data from actual studies across life science, engineering, computing, and business
• Includes new coverage to support the use of R
• Offers new chapters on big data techniques