Introduction to Probability and Statistics for Engineers and Scientists

6th Auflage
ISBN: 978-0-12-824346-6
Verlag: Elsevier Science

Buch, Englisch, 704 Seiten, Format (B × H): 203 mm x 243 mm, Gewicht: 1231 g

Introduction to Probability and Statistics for Engineers and Scientists, Sixth Edition, uniquely emphasizes how probability informs statistical problems, thus helping readers develop an intuitive understanding of the statistical procedures 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. End-of-chapter reviews of materials highlight key ideas, also discussing the risks associated with the practical application of each material. In the new edition, coverage includes information on Big Data and the use of R.

This book 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.

Zielgruppe

<p>Undergraduate and graduate students in statistics, engineering or other sciences</p>

Autoren/Hrsg.

Ross, Sheldon M

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
• Technische Wissenschaften Technik Allgemein Mathematik für Ingenieure
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik

Inhaltsverzeichnis

CHAPTER 1 Introduction to statistics

CHAPTER 2 Descriptive statistics

CHAPTER 3 Elements of probability

CHAPTER 4 Random variables and expectation

CHAPTER 5 Special random variables

CHAPTER 6 Distributions of sampling statistics

CHAPTER 7 Parameter estimation

CHAPTER 8 Hypothesis testing

CHAPTER 9 Regression

CHAPTER 10 Analysis of variance

CHAPTER 11 Goodness of fit tests and categorical data analysis

CHAPTER 12 Nonparametric hypothesis tests

CHAPTER 13 Quality control

CHAPTER 14 Life testing

CHAPTER 15 Simulation, bootstrap statistical methods, and permutation tests

CHAPTER 16 Machine learning and big data

Über Autor(innen)

Ross, Sheldon M
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.