Probability & Statistics for Engineers & Scientists, Global Edition (9th Ed.)
Langue : Anglais
Auteurs : Walpole Ronald, Myers Raymond, Myers Sharon, Ye Keying
For junior/senior undergraduates taking probability and statistics as applied to engineering, science, or computer science.
This classic text provides a rigorous introduction to basic probability theory and statistical inference, with a unique balance between theory and methodology. Interesting, relevant applications use real data from actual studies, showing how the concepts and methods can be used to solve problems in the field. This revision focuses on improved clarity and deeper understanding.
1. Introduction to Statistics and Data Analysis
- 1.1 Overview: Statistical Inference, Samples, Populations, and the Role of Probability
- 1.2 Sampling Procedures; Collection of Data
- 1.3 Measures of Location: The Sample Mean and Median
- Exercises
- 1.4 Measures of Variability
- Exercises
- 1.5 Discrete and Continuous Data
- 1.6 Statistical Modeling, Scientific Inspection, and Graphical Methods
- 1.7 General Types of Statistical Studies: Designed Experiment, Observational Study, and Retrospective Study
- Exercises
2. Probability
- 2.1 Sample Space
- 2.2 Events
- Exercises
- 2.3 Counting Sample Points
- Exercises
- 2.4 Probability of an Event
- 2.5 Additive Rules
- Exercises
- 2.6 Conditional Probability, Independence and Product Rules
- Exercises
- 2.7 Bayes' Rule
- Exercises
- Review Exercises
- 2.8 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters
3. Random Variables and Probability Distributions
- 3.1 Concept of a Random Variable
- 3.2 Discrete Probability Distributions
- 3.3 Continuous Probability Distributions
- Exercises
- 3.4 Joint Probability Distributions
- Exercises
- Review Exercises
- 3.5 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters
4. Mathematical Expectation
- 4.1 Mean of a Random Variable
- Exercises
- 4.2 Variance and Covariance of Random Variables
- Exercises
- 4.3 Means and Variances of Linear Combinations of Random Variables
- 4.4 Chebyshev's Theorem
- Exercises
- Review Exercises
- 4.5 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters
5. Some Discrete Probability Distributions
- 5.1 Introduction and Motivation
- 5.2 Binomial and Multinomial Distributions
- Exercises
- 5.3 Hypergeometric Distribution
- Exercises
- 5.4 Negative Binomial and Geometric Distributions
- 5.5 Poisson Distribution and the Poisson Process
- Exercises
- Review Exercises
- 5.6 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters
6. Some Continuous Probability Distributions
- 6.1 Continuous Uniform Distribution
- 6.2 Normal Distribution
- 6.3 Areas under the Normal Curve
- 6.4 Applications of the Normal Distribution
- Exercises
- 6.5 Normal Approximation to the Binomial
- Exercises
- 6.6 Gamma and Exponential Distributions
- 6.7 Chi-Squared Distribution
- 6.8 Beta Distribution
- 6.9 Lognormal Distribution (Optional)
- 6.10 Weibull Distribution (Optional)
- Exercises
- Review Exercises
- 6.11 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters
7. Functions of Random Variables (Optional)
- 7.1 Introduction
- 7.2 Transformations of Variables
- 7.3 Moments and Moment-Generating Functions
- Exercises
8. Sampling Distributions and More Graphical Tools
- 8.1 Random Sampling and Sampling Distributions
- 8.2 Some Important Statistics
- Exercises
- 8.3 Sampling Distributions
- 8.4 Sampling Distribution of Means and the Central Limit Theorem
- Exercises
- 8.5 Sampling Distribution of S2
- 8.6 t-Distribution
- 8.8 Quantile and Probability Plots
- Exercises
- Review Exercises
- 8.9 Potential Misconceptions and Hazards; Relationship to Mate
Hallmark Features
- The balance between theory and applications offers mathematical support to enhance coverage when necessary, giving engineers and scientists the proper mathematical context for statistical tools and methods.
- Mathematical level: this text assumes one semester of differential and integral calculus as a prerequisite.
- Calculus is confined to elementary probability theory and probability distributions
- Matrix algebra is used modestly in coverage of linear regression material
- Linear algebra and the use of matrices are applied in Chapters 11–15, where treatment of linear regression and analysis of variance is covered.
- Compelling exercise sets challenge students to use the concepts to solve problems that occur in many real-life scientific and engineering situations. Many exercises contain real data from studies in the fields of biomedical, bioengineering, business, computing, etc.
- Real-life applications of the Poisson, binomial, and hypergeometric distributions generate student interest using topics such as flaws in manufactured copper wire, highway potholes, hospital patient traffic, airport luggage screening, and homeland security.
- Statistical software coverage in the following case studies includes SAS® and MINITAB®, with screenshots and graphics as appropriate:
- Two-sample hypothesis testing
- Multiple linear regression
- Analysis of variance
- Use of two-level factorial-experiments
- Interaction plots provide examples of scientific interpretations and new exercises using graphics.
- Topic outline
- Chapter 1: elementary overview of statistical inference
- C
Date de parution : 08-2016
Ouvrage de 816 p.
18.8x23.4 cm
Thème de Probability & Statistics for Engineers & Scientists... :
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