Introduction to Probability and Statistics for Engineers and Scientists
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. Les mer
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På grunn av Brexit-tilpasninger og tiltak for å begrense covid-19 kan det dessverre oppstå forsinket levering.
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.
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.
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
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
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.