Description
Elsevier Introduction to Probability and Statistics for Engineers and Scientists 6th Edition 2021 by Sheldon M Ross
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.
Key Features
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
Readership
Undergraduate and graduate students in statistics, engineering or other sciences
Table of Contents
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