Introduction to Probability and Statistics for Engineers and Scientists
Autor Sheldon M. Rossen Limba Engleză Hardback – 7 feb 2021
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
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Specificații
ISBN-13: 9780128243466
ISBN-10: 0128243465
Pagini: 704
Ilustrații: 150 illustrations (75 in full color)
Dimensiuni: 191 x 235 x 28 mm
Greutate: 1.2 kg
Ediția:6
Editura: ELSEVIER SCIENCE
ISBN-10: 0128243465
Pagini: 704
Ilustrații: 150 illustrations (75 in full color)
Dimensiuni: 191 x 235 x 28 mm
Greutate: 1.2 kg
Ediția:6
Editura: ELSEVIER SCIENCE
Public țintă
Undergraduate and graduate students in statistics, engineering or other sciencesCuprins
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