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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.
- Provides the author’s uniquely accessible and engaging approach as tailored for the needs of Engineers and Scientists
- Features 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
Undergraduate and graduate students in statistics, engineering or other sciences
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
- No. of pages:
- © Academic Press 2020
- 11th September 2020
- Academic Press
- eBook ISBN:
- Hardcover ISBN:
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.
Professor, Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, USA
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