Rent 9780534386696 | Probability and Statistics for Engineers and Scientists

Summary Table of Contents

The new edition of Anthony Hayter's book addresses the many challenges professors face in Engineering Statistics. Debate over teaching this course has centered on which topics to emphasize and how to do so (e.g., balancing data analysis with probability and use of the computer with the understanding of formulas). While Hayter's book assumes students have some quantitative ability, it provides a primarily applied rather than theoretical approach. Hayter has carefully constructed his book to allow for maximum customization. The material has been ... MOREdivided into four sections based on probability (Chapters 1 - 5), basic statistics (Chapters 6 - 10), advanced statistical methodologies (Chapters 11 - 14), and additional topics (Chapters 15 - 17). The Preface offers suggested paths that can be taken, based on topic preference. This is ideal for departments where different methods of teaching coexist. This flexible approach is also employed with regard to the use of computer tools. Because the book is not tied to a particular software package, instructors may choose to use the program that best suits their needs. However, the book does provide substantial computer output (using MINITAB and other programs) to give students the necessary practice in interpreting computer output. "Computer Note" sections offer tips for using various software packages to perform analysis of the data sets, which can be downloaded from the web site. Through the use of extensive examples and data sets, the book illustrates the importance of statistical data collection and analysis for students in the fields of aerospace, biochemical, civil, electrical, environmental, industrial, mechanical, and textile engineering, as well as students in physics, chemistry, computing, biology, management, and mathematics.

1. PROBABILITY THEORY Probabilities. Events. Combinations of Events. Conditional Probability. Probabilities of Event Intersections. Posterior Probabilities. Counting Techniques. 2. RANDOM VARIABLES Discrete Random Variables. Continuous Random Variables. The Expectation of a Random Variable. The Variance of a Random Variable. Jointly Distributed Random Variables. Combinations and Functions of Random Variables. 3. DISCRETE PROBABILITY DISTRIBUTIONS The Binomial Distribution. The Geometric and Negative Binomial Distributions. The Hypergeometric Distribution. The Poisson Distribution. The Multinomial Distribution. 4. CONTINUOUS PROBABILITY DISTRIBUTIONS The Uniform Distribution. The Exponential Distribution. The Gamma Distribution. The Weibull Distribution. The Beta Distribution. 5. THE NORMAL DISTRIBUTION Probability Calculations Using the Normal Distribution. Linear Combinations of Normal Random Variables. Approximating Distributions with the Normal Distribution. Distributions Related to the Normal Distribution. 6. DESCRIPTIVE STATISTICS Experimentation. Data Presentation. Sample Statistics. Examples. 7. STATISTICAL ESTIMATION AND SAMPLING DISTRIBUTIONS Point Estimates. Properties of Point Estimates. Sampling Distributions. Constructing Parameter Estimates. 8. INFERENCES ON A POPULATION MEAN Confidence Intervals. Hypothesis Testing. Summary. 9. COMPARING TWO POPULATION MEANS Introduction. Analysis of Paired Samples. Analysis of Independent Samples. Summary. 10. DISCRETE DATA ANALYSIS Inferences on a population Proportion. Comparing Two Population Proportions. Goodness-of-Fit Tests for One-Way Contingency Tables. Testing for Independence in Two-Way Contingency Tables. 11. THE ANALYSIS OF VARIANCE One Factor Analysis of Variance. Randomized Block Designs. 12. SIMPLE LINEAR REGRESSION AND CORRELATION The Simple Linear Regression Model. Fitting the Regression Line. Inferences on the Slope Parameter �1. Inferences on the Regression Line. Prediction Intervals for Future Response Values. The Analysis of Variance Tables. Residual Analysis. Variable Transformation. Correlation Analysis. 13. MULTIPLE LINEAR REGRESSION AND NONLINEAR REGRESSION Introduction to Multiple Linear Regression. Examples of Multiple Linear Regression. matrix Algebra Formulation of Multi-le Linear Regression. Evaluating Model Adequacy. Nonlinear Regression. 14. MULTIFACTOR EXPERIMENTAL DESIGN AND ANALYSIS Experiments with Two Factors. Experiments with Three or More Factors. 15. NONPARAMETRIC STATISTICAL ANALYSIS The analysis of a single Population. Comparing Two Populations. Comparing Three or More Populations. 16. QUALITY CONTROL METHODS Introduction. Statistical Process control. Variable Control Charts. Attribute Control Charts. Acceptance Sampling. 17. RELIABILITY ANALYSIS AND LIFE TESTING System Reliability. Modeling Failure Rates. Life Testing. TABLES. ANSWERS TO ODD-NUMBERED PROBLEMS. INDEX.

Preface

Probability Theory

(72)

Probabilities

(8)

... MORE

(7)

Combinations of Events

(21)

Conditional Probability

(7)

Probabilities of Event Intersections

(11)

Posterior Probabilities

(6)

Counting Techniques

(9)

Supplementary Problems

(3)

Random Variables

(83)

Discrete Random Variables

(12)

Continuous Random Variables

(13)

The Expectation of a Random Variables

(12)

The Variance of a Random Variables

110

(13)

Jointly Distributed Random Variables

123

(18)

Combinations and Functions of Random Variables

141

(12)

Supplementary Problems

153

(3)

Discrete Probability Distributions

156

(43)

The Binomial Distribution

156

(15)

The Geometric and Negative Binomial Distributions

171

(9)

The Hypergeometric Distribution

180

(5)

The Poisson Distribution

185

(8)

The Multinomial Distribution

193

(5)

Supplementary Problems

198

(1)

Continuous Probability Distributions

199

(33)

The Uniform Distribution

199

(4)

The Exponential Distribution

203

(10)

The Gamma Distribution

213

(6)

The Weibull Distribution

219

(6)

The Beta Distribution

225

(4)

Supplementary Problems

229

(3)

The Normal Distribution

232

(54)

Probability Calculations Using the Normal Distribution

232

(16)

Linear Combinations of Normal Random Variables

248

(11)

Approximating Distributions with the Normal Distribution

259

(12)

Distributions Related to the Normal Distribution

271

(13)

Supplementary Problems

284

(2)

Descriptive Statistics

286

(35)

Experimentation

286

(8)

Data Presentation

294

(12)

Sample Statistics

306

(8)

Example

314

(4)

Supplementary Problems

318

(3)

Statistical Estimation and Sampling Distributions

321

(36)

Point Estimates

321

(5)

Properties of Point Estimates

326

(12)

Sampling Distributions

338

(9)

Constructing Parameter Estimates

347

(8)

Supplementary Problems

355

(2)

Inferences on a Population Mean

357

(60)

Confidence Intervals

357

(18)

Hypothesis Testing

375

(37)

Summary

412

(2)

Supplementary Problems

414

(3)

Comparing Two Population Means

417

(48)

Introduction

417

(8)

Analysis of Paired Samples

425

(7)

Analysis of Independent Samples

432

(25)

Summary

457

(4)

Supplementary Problems

461

(4)

Discrete Data Analysis

465

(70)

Inferences on a Population Proportion

465

(26)

Comparing Two Population Proportions

491

(12)

Goodness of Fit Tests for One-Way Contingency Tables

503

(16)

Testing for Independence in Two-Way Contingency Tables

519

(13)

Supplementary Problems

532

(3)

The Analysis of Variance

535

(59)

One Factor Analysis of Variance

535

(31)

Randomized Block Designs

566

(24)

Supplementary Problems

590

(4)

Simple Linear Regression and Correlation

594

(74)

The Simple Linear Regression Model

594

(10)

Fitting the Regression Line

604

(14)

Inferences on the Slope Parameter β1

618

(9)

Inferences on the Regression Line

627

(6)

Prediction Intervals for Future Response Values

633

(6)

The Analysis of Variance Table

639

(6)

Residual Analysis

645

(5)

Variable Transformations

650

(7)

Correlation Analysis

657

(6)

Supplementary Problems

663

(5)

Multiple Linear Regression and Nonlinear Regression

668

(54)

Introduction to Multiple Linear Regression

668

(11)

Examples of Multiple Linear Regression

679

(18)

Matrix Algebra Formulation of Multiple Linear Regression

697

(11)

Evaluating Model Adequacy

708

(7)

Nonlinear Regression

715

(4)

Supplementary Problems

719

(3)

Multifactor Experimental Design and Analysis

722

(56)

Experiments with Two Factors

722

(37)

Experiments with Three or More Factors

759

(14)

Supplementary Problems

773

(5)

Nonparametric Statistical Analysis

778

(49)

The Analysis of a Single Population

779

(26)

Comparing Two Populations

805

(11)

Comparing Three or More Populations

816

(8)

Supplementary Problems

824

(3)

Quality Control Methods

827

(32)

Introduction

827

(1)

Statistical Process Control

827

(8)

Variable Control Charts

835

(9)

Attribute Control Charts

844

(7)

Acceptance Sampling

851

(6)

Supplementary Problems

857

(2)

Reliability Analysis and Life Testing

859

(22)

System Reliability

859

(6)

Modeling Failure Rates

865

(5)

Life Testing

870

(9)

Supplementary Problems

879

(2)

Tables

881

(10)

Cumulative Distribution Function of the Standard Normal Distribution

881

(2)

Critical Points of the Chi-Square Distribution

883

(1)

Critical Points of the t-Distribution

884

(1)

Critical Points of the F-Distribution

885

(3)

Critical Points qα,k,v of the Studentized Range Distribution

888

(2)

Critical Points dα,n for Kolmogorov and Kolmogorov-Smirnov Procedures

890

(1)

Answers to Odd-Numbered Problems

891

(18)

Index

909

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Probability and Statistics for Engineers and Scientists

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