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by William Mendenhall and Terry Sincich

Edition: 5TH 07Copyright: 2007

Publisher: Prentice Hall, Inc.

Published: 2007

International: No

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For engineering statistics courses in departments of Statistics and Engineering.

This text is designed for a two-semester introductory course in statistics for students majoring in engineering or any of hte physical sciences. Inevitalby, once these studenrts graduate and are employed, they will be involved in the collection and analysis of data and will be required to think critically about the results. Consequently, they need to acquire knowledge of the basic concepts of data description and statistical inference and familiarity with statistical methods they are required to use on the job.The text includes optional theoretical exercises allowing instructors who choose to emphasize theory to do so without requiring additional materials.

The assumed mathematical background is a two-semester sequence in calculus - that is, the course could be taught to students of average mathematical talent and with a basic understanding of the principles of differential and integral calculus.

**Features**

- 1. Blend of theory and applications. The basic theoretical concepts of mathematical statistics are integrated with a two-semester presentation of statistical methodology. Thus, the instructor has the option of presenting a course with either of two characteristics -- a course stressing basic concepts and applied statistics or a course that, while still tilted toward application, presents a modes introduction to the theory underlying statistical inference.
- 2. Statistical software applications with tutorials. The instructor and student have the option of using statistical software to perform the statistical calculations. Printouts from three popular statistical software packages -- SAS, SPSS, and Minitab -- as well as Microsoft Excel output are fully integrated into text. Tutorials with menu screens and dialog boxes are provided in Appendices C, D, and E. These tutorials are designed for the novice user; no prior experience with the software is needed.
- 3. Blended coverage of topics and applications. To meet the diverse needs of future engineers and scientists, the text provides coverage of a wide range of data analysis topics.The material on multiple regression and model building (Chapters 11-12), principles of experimental design (Chapter 13), quality control (Chapter 16), and reliability (Chapter 17) sets the text apart from the typical introductory statistics text. Although the material often refers to theoretical concepts, the presentation is oriented toward applications.
- 4. Numerous real data-based exercises.The text contains a large number of applied exercises designed to motivate a student and suggest future uses of the methodology. Nearly every exercise is based on data or experimental results extracted from professional journals or obtained from organizations in the engineering and physical sciences. Exercises are located at the ends of key sections and at the ends of chapters.
- 5. ''Statistics in Action'' case studies.The 5th edition of the text now includes a contemporary scientific study (''Statistics in Action'') and the accompanying data and analysis at the end of each chapter. Our goal is to show students the importance of applying sound statistical techniques in order to evaluate the findings and to think through the statistical issues involved.
- 6. Data sets provided on CD.All of the data sets associated with examples, exercises, and cases are provided on a CD that accompanies this text. (Each data set is marked with a CD icon and file name in the text.) The data files are saved in four different formats: MINITAB, SAS, SPSS, and ASCII (for easy importing into other statistical software packages). By analyzing these data using statistical software, calculations are minimized, allowing the student to concentrate on the interpretation of the results.
- 7. Numerical answers to exercises available at the back of the book. This gives students the guidence and immediate feedback they require as they work the exercises.

CHAPTER 1: INTRODUCTION

1.1. Statistics: The Science of Data

1.2. Fundamental Elements of Statistics

1.3. Types of Data

1.4. The Role of Statistics in Critical Thinking

1.5. A Guide to Statistical Methods Presented in this Text

Statistics in Action: Contamination of Fish in the Tennessee River Collecting theData

CHAPTER 2: DESCRIPTIVE STATISTICS

2.1. Graphical and Numerical Methods for Describing Qualitative Data

2.2. Graphical Methods for Describing Quantitative Data

2.3. Numerical Methods for Describing Quantitative Data

2.4. Measures of Central Tendency

2.5. Measures of Variation

2.6. Measures of Relative Standing

2.7. Methods for Detecting Outliers

2.8. Distorting the Truth with Descriptive Statistics

Statistics in Action: Characteristics of Contaminated Fish in the Tennessee River

CHAPTER 3: PROBABILITY

3.1. The Role of Probability in Statistics

3.2. Events, Sample Spaces, and Probability

3.3. Compound Events

3.4. Complementary Events

3.5. Conditional Probability

3.6. Probability Rules for Unions and Intersections

3.7. Bayes' Rule (Optional)

3.8. Some Counting Rules

3.9. Probability and Statistics: An Example

3.10 Random Sampling

Statistics in Action: Assessing Predictors of Software Defects

CHAPTER 4: DISCRETE RANDOM VARIABLES

4.1. Discrete Random Variables

4.2. The Probability Distribution for a Discrete Random Variable

4.3. Expected Values for Random Variables

4.4. Some Useful Expectation Theorems

4.5. Bernoulli Trials

4.6. The Binomial Probability Distribution

4.7. The Multinomial Probability Distribution

4.8. The Negative Binomial and the Geometric Probability Distributions

4.9. The Hypergeometric Probability Distribution

4.10 The Poisson Probability Distribution

4.11 Moments and Moment Generating Functions (Optional)

Statistics in Action: The Reliability of a "One-Shot" Device

CHAPTER 5: CONTINUOUS RANDOM VARIABLES.

5.1. Continuous Random Variables

5.2. The Density Function for a Continuous Random Variable

5.3. Expected Values for Continuous Random Variables

5.4. The Uniform Probability Distribution

5.5. The Normal Probability Distribution

5.6. Descriptive Methods for Assessing Normality

5.7. Gamma-Type Probability Distributions

5.8. The Weibull Probability Distriibution

5.9. Beta-Type Probability Distributions

5.10 Moments and Moment Generating Functions (Optional)

Statistics in Action: Super Weapons Development: Optimizing the Hit Ratio

CHAPTER 6: JOINT PROBABILITY DISTRIBUTIONS AND SAMPLING DISTRIBUTIONS

6.1. Bivariate Probability Distributions for Discrete Random Variables

6.2. Bivariate Probability Distributions for Continuous Random Variables

6.3. The Expected Value of Functions of Two Random Variables

6.4. Independence

6.5. The Covariance and Correlation of Two Random Variables

6.6. Probability Distributions and Expected Values of Functions of Random Variables (Optional)

6.7. Sampling Distributions

6.8. Approximating a Sampling Distribution by Monte Carlo Simulation

6.9. The Sampling Distributions of Means and Sums

6.10 Normal Approximation to the Binomial Distribution

6.11 Sampling Distributions Related to the Normal Distribution

Statistics in Action: Availability of an Up/Down System

CHAPTER 7: ESTIMATION USING CONFIDENCE INTERVALS

7.1. Point Estimators and their Properties

7.2. Finding Point Estimators: Classical Methods of Estimation

7.3. Finding Interval Estimators: The Pivotal Method

7.4. Estimation of Population Mean

7.5. Estimation of the Difference Between Two Population Means: Independent Samples

7.6. Estimation of the Difference Between Two Population Means: Matched Pairs

7.7. Estimation of a Poulation Proportion

7.8. Estimation of the Difference Between Two Population Proportions

7.9. Estimation of a Population Variance

7.10 Estimation of the Ratio of Two Population Variances

7.11 Choosing the Sample Size

7.12 Alternative Estimation Methods: Bootstrapping and Bayesian Methods (Optional)

Statistics in Action: Bursting Strength of PET Beverage Bottles

CHAPTER 8: TESTS OF HYPOTHESES

8.1. The Relationship Between Statistical Tests of Hypotheses and Confidence Intervals

8.2. Elements and Properties of a Statistical Test

8.3. Finding Statistical Tests: Classical Methods

8.4. Choosing the Null and Alternative Hypotheses

8.5. Testing a Population Mean

8.6. The Observed Significance Level for a Test

8.7. Testing the Difference Between Two Population Means: Independent Samples

8.8. Testing the Difference Between Two Population Means: Independent Samples

8.9. Testing a Population Proportion

8.10 Testing the Difference Between Two Population Proportions

8.11 Testing a Population Variance

8.12 Testing the Ration of Two Population Variances

8.13 Alternative Testing Procedures: Bootstrapping and Bayesian Methods (Optional)

Statistics in Action: Comparing Methods for Dissolving Drug Tablets - Dissolution Method Equivalence Testing

CHAPTER 9: CATEGORICAL DATA ANALYSIS

9.1. Categorical Data and Multinomial Probabilities

9.2. Estimating Category Probabilities in a One-Way Table

9.3. Testing Category Probabilities in a One-Way Table

9.4. Inferences About Category Probabilities in a Two-Way (Contingency) Table

9.5. Contingency Tables with Fixed Marginal Totals

9.6. Exact Tests for Independence in a Contingency Table Analysis (Optional)

Statistics in Action: The Public's Perception of Engineers and Engineering

CHAPTER 10: SIMPLE LINEAR REGRESSION

10.1 Regression Models

10.2 Model Assumptions

10.3 Estimating ?0 and ?1: The Method of Least Squares

10.4 Properties of the Least Squares Estimators

10.5 An Estimator of ?2

10.6 Assessing the Utility of the Model: Making Inferences About the Slope ?1

10.7 The Coefficient of Correlation

10.8 The Coefficient of Determination

10.9 Using the Model for Estimation and Pediction

10.10 A Complete Example

10.11 A Summary of the Steps to Follow in Simple Linear Regression

Statistics in Action: Can Dowser's Really Detect Water?

CHAPTER 11: MULTIPLE REGRESSION ANALYSIS

11.1. General Form of a Multiple Regression Model

11.2. Model Assumptions

11.3. Fitting the Model: The Method of Least Squares

11.4. Computations using Matrix Algebra; Estimating and Making Inferences about the ? Parameters

11.5. Assessing Overall Model Adequacy

11.6. A Confidence Interval for E(y) and a prediction interval for a Future Value of y

11.7. A First-Order Model with Quantitative Predictors

11.8. An Interaction Model with Quantitative Predictors

11.9. A Quadratic (Second-Order) Model with a Quantitative Predictor

11.10 Checking Assumptions: Residual Analysis

11.11 Some Pitfalls: Estimability, Multicollinearity, and Extrapolation

11.12 A Summary of the Steps to Follow in a Multiple Regression Analysis

Statistics in Action: Bid-Rigging in the Highway Construction Industry

CHAPTER 12: MODEL BUILDING

12.1. Introduction: Why Model Building is Important

12.2. The Two Types of Independent Variables: Quantitative and Qualitative

12.3. Models with a Single Quantitative Independent Variable

12.4. Models with Two Quantitative Independent Variables

12.5. Coding Quantitative Independent Variables (Optional)

12.6. Models with One Qualitative Independent Variable

12.7. Models with Both Quantitative and Qualitative Independent Variables

12.8. Tests for Comparing Nested Models

12.9. External Model Validation (Optional)

12.10 Stepwise Regression

Statistics in Action: Deregulation of the Intrastate Trucking Industry

CHAPTER 13: PRINCIPLES OF EXPERIMENTAL DESIGN

13.1. Introduction

13.2. Experimental Design Terminology

13.3. Controlling the Information in an Experiment

13.4. Noise-Reducing Designs

13.5. Volume-Increasing Designs

13.6. Selecting the Sample Size

13.7. The Importance of Randomization

Statistics in Action: Anti-Corrosive Behavior of Epoxy Coatings Augmented with Zinc

CHAPTER 14: ANALYSIS OF VARIANCE FOR DESIGNED EXPERIMENTS

14.1. Introduction

14.2. The Logic Behind an Analysis of Variance

14.3. One-Factor Completely Randomized Designs

14.4. Randomized Block Designs

14.5. Two-Factor Factorial Experiments

14.6. More Complex Factorial Designs (Optional)

14.7. Nested Sampling Designs (Optional)

14.8. Multiple Comparisons of Teatment Means

14.9. Checking ANOVA Assumptions

Statistics in Action: On the Trail of the Cockroach

CHAPTER 15: NONPARAMETRIC STATISTICS

15.1. Introduction: Distribution-Free Tests

15.2. Testing for Location of a Single Population

15.3. Comparing Two Populations: Independent Random Samples

15.4. Comparing Two Populations: Matched-Pair Design

15.5. Comparing Three or More Populations: Completely Randomized Design

15.6. Comparing Three or More Populations: Randomized Block Design

15.7. Nonparametric Regression

Statistics in Action: Agent Orange and Vietnam Vets

CHAPTER 16: STATISTICAL PROCESS AND QUALITY CONTROL

16.1. Total Quality Management

16.2. Variable Control Charts

16.3. Control Chart for Means: x-Chart

16.4. Control Chart for Process Variation: R-Chart

16.5. Detecting Trends in a Control Chart: Runs Analysis

16.6. Control Chart for Percent Defective: p-Chart

16.7. Control Chart for number of Defectives per item: c-Chart

16.8. Tolerance Limits

16.9. Capability Analysis (Optional)

16.10 Acceptance Sampling for Defectives

16.11 Other Sampling Plans (Optional)

16.12 Evolutionary Operations (Optional)

Statistics in Action: Testing Jet Fuel Additive for Safety

CHAPTER 17: PRODUCT AND SYSTEM RELIABILITY

17.1. Introduction

17.2. Failure Time Distributions

17.3. Hazard Rates

17.4. Life Testing: Censored Sampling

17.5. Estimating the Parameters of an Exponential Failure Time Distribution

17.6. Estimating the Parameters of a Weibull Failure Time Distribution

17.7 System Reliability

Statistics in Action: Modeling the Hazard Rate of Reinforced Concrete Bridge Deck Deterioration

APPENDIX A: MATRIX ALGEBRA

APPENDIX B: USEFUL STATISTICAL TABLES

APPENDIX C: SAS FOR WINDOWS TUTORIAL

APPENDIX D: MINITAB FOR WINDOWS TUTORIAL

APPENDIX E: SPSS FOR WINDOWS TUTORIAL

ANSWERS TO SELECTED EXERCISES

INDEX

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Summary

For engineering statistics courses in departments of Statistics and Engineering.

This text is designed for a two-semester introductory course in statistics for students majoring in engineering or any of hte physical sciences. Inevitalby, once these studenrts graduate and are employed, they will be involved in the collection and analysis of data and will be required to think critically about the results. Consequently, they need to acquire knowledge of the basic concepts of data description and statistical inference and familiarity with statistical methods they are required to use on the job.The text includes optional theoretical exercises allowing instructors who choose to emphasize theory to do so without requiring additional materials.

The assumed mathematical background is a two-semester sequence in calculus - that is, the course could be taught to students of average mathematical talent and with a basic understanding of the principles of differential and integral calculus.

**Features**

- 1. Blend of theory and applications. The basic theoretical concepts of mathematical statistics are integrated with a two-semester presentation of statistical methodology. Thus, the instructor has the option of presenting a course with either of two characteristics -- a course stressing basic concepts and applied statistics or a course that, while still tilted toward application, presents a modes introduction to the theory underlying statistical inference.
- 2. Statistical software applications with tutorials. The instructor and student have the option of using statistical software to perform the statistical calculations. Printouts from three popular statistical software packages -- SAS, SPSS, and Minitab -- as well as Microsoft Excel output are fully integrated into text. Tutorials with menu screens and dialog boxes are provided in Appendices C, D, and E. These tutorials are designed for the novice user; no prior experience with the software is needed.
- 3. Blended coverage of topics and applications. To meet the diverse needs of future engineers and scientists, the text provides coverage of a wide range of data analysis topics.The material on multiple regression and model building (Chapters 11-12), principles of experimental design (Chapter 13), quality control (Chapter 16), and reliability (Chapter 17) sets the text apart from the typical introductory statistics text. Although the material often refers to theoretical concepts, the presentation is oriented toward applications.
- 4. Numerous real data-based exercises.The text contains a large number of applied exercises designed to motivate a student and suggest future uses of the methodology. Nearly every exercise is based on data or experimental results extracted from professional journals or obtained from organizations in the engineering and physical sciences. Exercises are located at the ends of key sections and at the ends of chapters.
- 5. ''Statistics in Action'' case studies.The 5th edition of the text now includes a contemporary scientific study (''Statistics in Action'') and the accompanying data and analysis at the end of each chapter. Our goal is to show students the importance of applying sound statistical techniques in order to evaluate the findings and to think through the statistical issues involved.
- 6. Data sets provided on CD.All of the data sets associated with examples, exercises, and cases are provided on a CD that accompanies this text. (Each data set is marked with a CD icon and file name in the text.) The data files are saved in four different formats: MINITAB, SAS, SPSS, and ASCII (for easy importing into other statistical software packages). By analyzing these data using statistical software, calculations are minimized, allowing the student to concentrate on the interpretation of the results.
- 7. Numerical answers to exercises available at the back of the book. This gives students the guidence and immediate feedback they require as they work the exercises.

Table of Contents

CHAPTER 1: INTRODUCTION

1.1. Statistics: The Science of Data

1.2. Fundamental Elements of Statistics

1.3. Types of Data

1.4. The Role of Statistics in Critical Thinking

1.5. A Guide to Statistical Methods Presented in this Text

Statistics in Action: Contamination of Fish in the Tennessee River Collecting theData

CHAPTER 2: DESCRIPTIVE STATISTICS

2.1. Graphical and Numerical Methods for Describing Qualitative Data

2.2. Graphical Methods for Describing Quantitative Data

2.3. Numerical Methods for Describing Quantitative Data

2.4. Measures of Central Tendency

2.5. Measures of Variation

2.6. Measures of Relative Standing

2.7. Methods for Detecting Outliers

2.8. Distorting the Truth with Descriptive Statistics

Statistics in Action: Characteristics of Contaminated Fish in the Tennessee River

CHAPTER 3: PROBABILITY

3.1. The Role of Probability in Statistics

3.2. Events, Sample Spaces, and Probability

3.3. Compound Events

3.4. Complementary Events

3.5. Conditional Probability

3.6. Probability Rules for Unions and Intersections

3.7. Bayes' Rule (Optional)

3.8. Some Counting Rules

3.9. Probability and Statistics: An Example

3.10 Random Sampling

Statistics in Action: Assessing Predictors of Software Defects

CHAPTER 4: DISCRETE RANDOM VARIABLES

4.1. Discrete Random Variables

4.2. The Probability Distribution for a Discrete Random Variable

4.3. Expected Values for Random Variables

4.4. Some Useful Expectation Theorems

4.5. Bernoulli Trials

4.6. The Binomial Probability Distribution

4.7. The Multinomial Probability Distribution

4.8. The Negative Binomial and the Geometric Probability Distributions

4.9. The Hypergeometric Probability Distribution

4.10 The Poisson Probability Distribution

4.11 Moments and Moment Generating Functions (Optional)

Statistics in Action: The Reliability of a "One-Shot" Device

CHAPTER 5: CONTINUOUS RANDOM VARIABLES.

5.1. Continuous Random Variables

5.2. The Density Function for a Continuous Random Variable

5.3. Expected Values for Continuous Random Variables

5.4. The Uniform Probability Distribution

5.5. The Normal Probability Distribution

5.6. Descriptive Methods for Assessing Normality

5.7. Gamma-Type Probability Distributions

5.8. The Weibull Probability Distriibution

5.9. Beta-Type Probability Distributions

5.10 Moments and Moment Generating Functions (Optional)

Statistics in Action: Super Weapons Development: Optimizing the Hit Ratio

CHAPTER 6: JOINT PROBABILITY DISTRIBUTIONS AND SAMPLING DISTRIBUTIONS

6.1. Bivariate Probability Distributions for Discrete Random Variables

6.2. Bivariate Probability Distributions for Continuous Random Variables

6.3. The Expected Value of Functions of Two Random Variables

6.4. Independence

6.5. The Covariance and Correlation of Two Random Variables

6.6. Probability Distributions and Expected Values of Functions of Random Variables (Optional)

6.7. Sampling Distributions

6.8. Approximating a Sampling Distribution by Monte Carlo Simulation

6.9. The Sampling Distributions of Means and Sums

6.10 Normal Approximation to the Binomial Distribution

6.11 Sampling Distributions Related to the Normal Distribution

Statistics in Action: Availability of an Up/Down System

CHAPTER 7: ESTIMATION USING CONFIDENCE INTERVALS

7.1. Point Estimators and their Properties

7.2. Finding Point Estimators: Classical Methods of Estimation

7.3. Finding Interval Estimators: The Pivotal Method

7.4. Estimation of Population Mean

7.5. Estimation of the Difference Between Two Population Means: Independent Samples

7.6. Estimation of the Difference Between Two Population Means: Matched Pairs

7.7. Estimation of a Poulation Proportion

7.8. Estimation of the Difference Between Two Population Proportions

7.9. Estimation of a Population Variance

7.10 Estimation of the Ratio of Two Population Variances

7.11 Choosing the Sample Size

7.12 Alternative Estimation Methods: Bootstrapping and Bayesian Methods (Optional)

Statistics in Action: Bursting Strength of PET Beverage Bottles

CHAPTER 8: TESTS OF HYPOTHESES

8.1. The Relationship Between Statistical Tests of Hypotheses and Confidence Intervals

8.2. Elements and Properties of a Statistical Test

8.3. Finding Statistical Tests: Classical Methods

8.4. Choosing the Null and Alternative Hypotheses

8.5. Testing a Population Mean

8.6. The Observed Significance Level for a Test

8.7. Testing the Difference Between Two Population Means: Independent Samples

8.8. Testing the Difference Between Two Population Means: Independent Samples

8.9. Testing a Population Proportion

8.10 Testing the Difference Between Two Population Proportions

8.11 Testing a Population Variance

8.12 Testing the Ration of Two Population Variances

8.13 Alternative Testing Procedures: Bootstrapping and Bayesian Methods (Optional)

Statistics in Action: Comparing Methods for Dissolving Drug Tablets - Dissolution Method Equivalence Testing

CHAPTER 9: CATEGORICAL DATA ANALYSIS

9.1. Categorical Data and Multinomial Probabilities

9.2. Estimating Category Probabilities in a One-Way Table

9.3. Testing Category Probabilities in a One-Way Table

9.4. Inferences About Category Probabilities in a Two-Way (Contingency) Table

9.5. Contingency Tables with Fixed Marginal Totals

9.6. Exact Tests for Independence in a Contingency Table Analysis (Optional)

Statistics in Action: The Public's Perception of Engineers and Engineering

CHAPTER 10: SIMPLE LINEAR REGRESSION

10.1 Regression Models

10.2 Model Assumptions

10.3 Estimating ?0 and ?1: The Method of Least Squares

10.4 Properties of the Least Squares Estimators

10.5 An Estimator of ?2

10.6 Assessing the Utility of the Model: Making Inferences About the Slope ?1

10.7 The Coefficient of Correlation

10.8 The Coefficient of Determination

10.9 Using the Model for Estimation and Pediction

10.10 A Complete Example

10.11 A Summary of the Steps to Follow in Simple Linear Regression

Statistics in Action: Can Dowser's Really Detect Water?

CHAPTER 11: MULTIPLE REGRESSION ANALYSIS

11.1. General Form of a Multiple Regression Model

11.2. Model Assumptions

11.3. Fitting the Model: The Method of Least Squares

11.4. Computations using Matrix Algebra; Estimating and Making Inferences about the ? Parameters

11.5. Assessing Overall Model Adequacy

11.6. A Confidence Interval for E(y) and a prediction interval for a Future Value of y

11.7. A First-Order Model with Quantitative Predictors

11.8. An Interaction Model with Quantitative Predictors

11.9. A Quadratic (Second-Order) Model with a Quantitative Predictor

11.10 Checking Assumptions: Residual Analysis

11.11 Some Pitfalls: Estimability, Multicollinearity, and Extrapolation

11.12 A Summary of the Steps to Follow in a Multiple Regression Analysis

Statistics in Action: Bid-Rigging in the Highway Construction Industry

CHAPTER 12: MODEL BUILDING

12.1. Introduction: Why Model Building is Important

12.2. The Two Types of Independent Variables: Quantitative and Qualitative

12.3. Models with a Single Quantitative Independent Variable

12.4. Models with Two Quantitative Independent Variables

12.5. Coding Quantitative Independent Variables (Optional)

12.6. Models with One Qualitative Independent Variable

12.7. Models with Both Quantitative and Qualitative Independent Variables

12.8. Tests for Comparing Nested Models

12.9. External Model Validation (Optional)

12.10 Stepwise Regression

Statistics in Action: Deregulation of the Intrastate Trucking Industry

CHAPTER 13: PRINCIPLES OF EXPERIMENTAL DESIGN

13.1. Introduction

13.2. Experimental Design Terminology

13.3. Controlling the Information in an Experiment

13.4. Noise-Reducing Designs

13.5. Volume-Increasing Designs

13.6. Selecting the Sample Size

13.7. The Importance of Randomization

Statistics in Action: Anti-Corrosive Behavior of Epoxy Coatings Augmented with Zinc

CHAPTER 14: ANALYSIS OF VARIANCE FOR DESIGNED EXPERIMENTS

14.1. Introduction

14.2. The Logic Behind an Analysis of Variance

14.3. One-Factor Completely Randomized Designs

14.4. Randomized Block Designs

14.5. Two-Factor Factorial Experiments

14.6. More Complex Factorial Designs (Optional)

14.7. Nested Sampling Designs (Optional)

14.8. Multiple Comparisons of Teatment Means

14.9. Checking ANOVA Assumptions

Statistics in Action: On the Trail of the Cockroach

CHAPTER 15: NONPARAMETRIC STATISTICS

15.1. Introduction: Distribution-Free Tests

15.2. Testing for Location of a Single Population

15.3. Comparing Two Populations: Independent Random Samples

15.4. Comparing Two Populations: Matched-Pair Design

15.5. Comparing Three or More Populations: Completely Randomized Design

15.6. Comparing Three or More Populations: Randomized Block Design

15.7. Nonparametric Regression

Statistics in Action: Agent Orange and Vietnam Vets

CHAPTER 16: STATISTICAL PROCESS AND QUALITY CONTROL

16.1. Total Quality Management

16.2. Variable Control Charts

16.3. Control Chart for Means: x-Chart

16.4. Control Chart for Process Variation: R-Chart

16.5. Detecting Trends in a Control Chart: Runs Analysis

16.6. Control Chart for Percent Defective: p-Chart

16.7. Control Chart for number of Defectives per item: c-Chart

16.8. Tolerance Limits

16.9. Capability Analysis (Optional)

16.10 Acceptance Sampling for Defectives

16.11 Other Sampling Plans (Optional)

16.12 Evolutionary Operations (Optional)

Statistics in Action: Testing Jet Fuel Additive for Safety

CHAPTER 17: PRODUCT AND SYSTEM RELIABILITY

17.1. Introduction

17.2. Failure Time Distributions

17.3. Hazard Rates

17.4. Life Testing: Censored Sampling

17.5. Estimating the Parameters of an Exponential Failure Time Distribution

17.6. Estimating the Parameters of a Weibull Failure Time Distribution

17.7 System Reliability

Statistics in Action: Modeling the Hazard Rate of Reinforced Concrete Bridge Deck Deterioration

APPENDIX A: MATRIX ALGEBRA

APPENDIX B: USEFUL STATISTICAL TABLES

APPENDIX C: SAS FOR WINDOWS TUTORIAL

APPENDIX D: MINITAB FOR WINDOWS TUTORIAL

APPENDIX E: SPSS FOR WINDOWS TUTORIAL

ANSWERS TO SELECTED EXERCISES

INDEX

Publisher Info

Publisher: Prentice Hall, Inc.

Published: 2007

International: No

Published: 2007

International: No

For engineering statistics courses in departments of Statistics and Engineering.

This text is designed for a two-semester introductory course in statistics for students majoring in engineering or any of hte physical sciences. Inevitalby, once these studenrts graduate and are employed, they will be involved in the collection and analysis of data and will be required to think critically about the results. Consequently, they need to acquire knowledge of the basic concepts of data description and statistical inference and familiarity with statistical methods they are required to use on the job.The text includes optional theoretical exercises allowing instructors who choose to emphasize theory to do so without requiring additional materials.

The assumed mathematical background is a two-semester sequence in calculus - that is, the course could be taught to students of average mathematical talent and with a basic understanding of the principles of differential and integral calculus.

**Features**

- 1. Blend of theory and applications. The basic theoretical concepts of mathematical statistics are integrated with a two-semester presentation of statistical methodology. Thus, the instructor has the option of presenting a course with either of two characteristics -- a course stressing basic concepts and applied statistics or a course that, while still tilted toward application, presents a modes introduction to the theory underlying statistical inference.
- 2. Statistical software applications with tutorials. The instructor and student have the option of using statistical software to perform the statistical calculations. Printouts from three popular statistical software packages -- SAS, SPSS, and Minitab -- as well as Microsoft Excel output are fully integrated into text. Tutorials with menu screens and dialog boxes are provided in Appendices C, D, and E. These tutorials are designed for the novice user; no prior experience with the software is needed.
- 3. Blended coverage of topics and applications. To meet the diverse needs of future engineers and scientists, the text provides coverage of a wide range of data analysis topics.The material on multiple regression and model building (Chapters 11-12), principles of experimental design (Chapter 13), quality control (Chapter 16), and reliability (Chapter 17) sets the text apart from the typical introductory statistics text. Although the material often refers to theoretical concepts, the presentation is oriented toward applications.
- 4. Numerous real data-based exercises.The text contains a large number of applied exercises designed to motivate a student and suggest future uses of the methodology. Nearly every exercise is based on data or experimental results extracted from professional journals or obtained from organizations in the engineering and physical sciences. Exercises are located at the ends of key sections and at the ends of chapters.
- 5. ''Statistics in Action'' case studies.The 5th edition of the text now includes a contemporary scientific study (''Statistics in Action'') and the accompanying data and analysis at the end of each chapter. Our goal is to show students the importance of applying sound statistical techniques in order to evaluate the findings and to think through the statistical issues involved.
- 6. Data sets provided on CD.All of the data sets associated with examples, exercises, and cases are provided on a CD that accompanies this text. (Each data set is marked with a CD icon and file name in the text.) The data files are saved in four different formats: MINITAB, SAS, SPSS, and ASCII (for easy importing into other statistical software packages). By analyzing these data using statistical software, calculations are minimized, allowing the student to concentrate on the interpretation of the results.
- 7. Numerical answers to exercises available at the back of the book. This gives students the guidence and immediate feedback they require as they work the exercises.

CHAPTER 1: INTRODUCTION

1.1. Statistics: The Science of Data

1.2. Fundamental Elements of Statistics

1.3. Types of Data

1.4. The Role of Statistics in Critical Thinking

1.5. A Guide to Statistical Methods Presented in this Text

Statistics in Action: Contamination of Fish in the Tennessee River Collecting theData

CHAPTER 2: DESCRIPTIVE STATISTICS

2.1. Graphical and Numerical Methods for Describing Qualitative Data

2.2. Graphical Methods for Describing Quantitative Data

2.3. Numerical Methods for Describing Quantitative Data

2.4. Measures of Central Tendency

2.5. Measures of Variation

2.6. Measures of Relative Standing

2.7. Methods for Detecting Outliers

2.8. Distorting the Truth with Descriptive Statistics

Statistics in Action: Characteristics of Contaminated Fish in the Tennessee River

CHAPTER 3: PROBABILITY

3.1. The Role of Probability in Statistics

3.2. Events, Sample Spaces, and Probability

3.3. Compound Events

3.4. Complementary Events

3.5. Conditional Probability

3.6. Probability Rules for Unions and Intersections

3.7. Bayes' Rule (Optional)

3.8. Some Counting Rules

3.9. Probability and Statistics: An Example

3.10 Random Sampling

Statistics in Action: Assessing Predictors of Software Defects

CHAPTER 4: DISCRETE RANDOM VARIABLES

4.1. Discrete Random Variables

4.2. The Probability Distribution for a Discrete Random Variable

4.3. Expected Values for Random Variables

4.4. Some Useful Expectation Theorems

4.5. Bernoulli Trials

4.6. The Binomial Probability Distribution

4.7. The Multinomial Probability Distribution

4.8. The Negative Binomial and the Geometric Probability Distributions

4.9. The Hypergeometric Probability Distribution

4.10 The Poisson Probability Distribution

4.11 Moments and Moment Generating Functions (Optional)

Statistics in Action: The Reliability of a "One-Shot" Device

CHAPTER 5: CONTINUOUS RANDOM VARIABLES.

5.1. Continuous Random Variables

5.2. The Density Function for a Continuous Random Variable

5.3. Expected Values for Continuous Random Variables

5.4. The Uniform Probability Distribution

5.5. The Normal Probability Distribution

5.6. Descriptive Methods for Assessing Normality

5.7. Gamma-Type Probability Distributions

5.8. The Weibull Probability Distriibution

5.9. Beta-Type Probability Distributions

5.10 Moments and Moment Generating Functions (Optional)

Statistics in Action: Super Weapons Development: Optimizing the Hit Ratio

CHAPTER 6: JOINT PROBABILITY DISTRIBUTIONS AND SAMPLING DISTRIBUTIONS

6.1. Bivariate Probability Distributions for Discrete Random Variables

6.2. Bivariate Probability Distributions for Continuous Random Variables

6.3. The Expected Value of Functions of Two Random Variables

6.4. Independence

6.5. The Covariance and Correlation of Two Random Variables

6.6. Probability Distributions and Expected Values of Functions of Random Variables (Optional)

6.7. Sampling Distributions

6.8. Approximating a Sampling Distribution by Monte Carlo Simulation

6.9. The Sampling Distributions of Means and Sums

6.10 Normal Approximation to the Binomial Distribution

6.11 Sampling Distributions Related to the Normal Distribution

Statistics in Action: Availability of an Up/Down System

CHAPTER 7: ESTIMATION USING CONFIDENCE INTERVALS

7.1. Point Estimators and their Properties

7.2. Finding Point Estimators: Classical Methods of Estimation

7.3. Finding Interval Estimators: The Pivotal Method

7.4. Estimation of Population Mean

7.5. Estimation of the Difference Between Two Population Means: Independent Samples

7.6. Estimation of the Difference Between Two Population Means: Matched Pairs

7.7. Estimation of a Poulation Proportion

7.8. Estimation of the Difference Between Two Population Proportions

7.9. Estimation of a Population Variance

7.10 Estimation of the Ratio of Two Population Variances

7.11 Choosing the Sample Size

7.12 Alternative Estimation Methods: Bootstrapping and Bayesian Methods (Optional)

Statistics in Action: Bursting Strength of PET Beverage Bottles

CHAPTER 8: TESTS OF HYPOTHESES

8.1. The Relationship Between Statistical Tests of Hypotheses and Confidence Intervals

8.2. Elements and Properties of a Statistical Test

8.3. Finding Statistical Tests: Classical Methods

8.4. Choosing the Null and Alternative Hypotheses

8.5. Testing a Population Mean

8.6. The Observed Significance Level for a Test

8.7. Testing the Difference Between Two Population Means: Independent Samples

8.8. Testing the Difference Between Two Population Means: Independent Samples

8.9. Testing a Population Proportion

8.10 Testing the Difference Between Two Population Proportions

8.11 Testing a Population Variance

8.12 Testing the Ration of Two Population Variances

8.13 Alternative Testing Procedures: Bootstrapping and Bayesian Methods (Optional)

Statistics in Action: Comparing Methods for Dissolving Drug Tablets - Dissolution Method Equivalence Testing

CHAPTER 9: CATEGORICAL DATA ANALYSIS

9.1. Categorical Data and Multinomial Probabilities

9.2. Estimating Category Probabilities in a One-Way Table

9.3. Testing Category Probabilities in a One-Way Table

9.4. Inferences About Category Probabilities in a Two-Way (Contingency) Table

9.5. Contingency Tables with Fixed Marginal Totals

9.6. Exact Tests for Independence in a Contingency Table Analysis (Optional)

Statistics in Action: The Public's Perception of Engineers and Engineering

CHAPTER 10: SIMPLE LINEAR REGRESSION

10.1 Regression Models

10.2 Model Assumptions

10.3 Estimating ?0 and ?1: The Method of Least Squares

10.4 Properties of the Least Squares Estimators

10.5 An Estimator of ?2

10.6 Assessing the Utility of the Model: Making Inferences About the Slope ?1

10.7 The Coefficient of Correlation

10.8 The Coefficient of Determination

10.9 Using the Model for Estimation and Pediction

10.10 A Complete Example

10.11 A Summary of the Steps to Follow in Simple Linear Regression

Statistics in Action: Can Dowser's Really Detect Water?

CHAPTER 11: MULTIPLE REGRESSION ANALYSIS

11.1. General Form of a Multiple Regression Model

11.2. Model Assumptions

11.3. Fitting the Model: The Method of Least Squares

11.4. Computations using Matrix Algebra; Estimating and Making Inferences about the ? Parameters

11.5. Assessing Overall Model Adequacy

11.6. A Confidence Interval for E(y) and a prediction interval for a Future Value of y

11.7. A First-Order Model with Quantitative Predictors

11.8. An Interaction Model with Quantitative Predictors

11.9. A Quadratic (Second-Order) Model with a Quantitative Predictor

11.10 Checking Assumptions: Residual Analysis

11.11 Some Pitfalls: Estimability, Multicollinearity, and Extrapolation

11.12 A Summary of the Steps to Follow in a Multiple Regression Analysis

Statistics in Action: Bid-Rigging in the Highway Construction Industry

CHAPTER 12: MODEL BUILDING

12.1. Introduction: Why Model Building is Important

12.2. The Two Types of Independent Variables: Quantitative and Qualitative

12.3. Models with a Single Quantitative Independent Variable

12.4. Models with Two Quantitative Independent Variables

12.5. Coding Quantitative Independent Variables (Optional)

12.6. Models with One Qualitative Independent Variable

12.7. Models with Both Quantitative and Qualitative Independent Variables

12.8. Tests for Comparing Nested Models

12.9. External Model Validation (Optional)

12.10 Stepwise Regression

Statistics in Action: Deregulation of the Intrastate Trucking Industry

CHAPTER 13: PRINCIPLES OF EXPERIMENTAL DESIGN

13.1. Introduction

13.2. Experimental Design Terminology

13.3. Controlling the Information in an Experiment

13.4. Noise-Reducing Designs

13.5. Volume-Increasing Designs

13.6. Selecting the Sample Size

13.7. The Importance of Randomization

Statistics in Action: Anti-Corrosive Behavior of Epoxy Coatings Augmented with Zinc

CHAPTER 14: ANALYSIS OF VARIANCE FOR DESIGNED EXPERIMENTS

14.1. Introduction

14.2. The Logic Behind an Analysis of Variance

14.3. One-Factor Completely Randomized Designs

14.4. Randomized Block Designs

14.5. Two-Factor Factorial Experiments

14.6. More Complex Factorial Designs (Optional)

14.7. Nested Sampling Designs (Optional)

14.8. Multiple Comparisons of Teatment Means

14.9. Checking ANOVA Assumptions

Statistics in Action: On the Trail of the Cockroach

CHAPTER 15: NONPARAMETRIC STATISTICS

15.1. Introduction: Distribution-Free Tests

15.2. Testing for Location of a Single Population

15.3. Comparing Two Populations: Independent Random Samples

15.4. Comparing Two Populations: Matched-Pair Design

15.5. Comparing Three or More Populations: Completely Randomized Design

15.6. Comparing Three or More Populations: Randomized Block Design

15.7. Nonparametric Regression

Statistics in Action: Agent Orange and Vietnam Vets

CHAPTER 16: STATISTICAL PROCESS AND QUALITY CONTROL

16.1. Total Quality Management

16.2. Variable Control Charts

16.3. Control Chart for Means: x-Chart

16.4. Control Chart for Process Variation: R-Chart

16.5. Detecting Trends in a Control Chart: Runs Analysis

16.6. Control Chart for Percent Defective: p-Chart

16.7. Control Chart for number of Defectives per item: c-Chart

16.8. Tolerance Limits

16.9. Capability Analysis (Optional)

16.10 Acceptance Sampling for Defectives

16.11 Other Sampling Plans (Optional)

16.12 Evolutionary Operations (Optional)

Statistics in Action: Testing Jet Fuel Additive for Safety

CHAPTER 17: PRODUCT AND SYSTEM RELIABILITY

17.1. Introduction

17.2. Failure Time Distributions

17.3. Hazard Rates

17.4. Life Testing: Censored Sampling

17.5. Estimating the Parameters of an Exponential Failure Time Distribution

17.6. Estimating the Parameters of a Weibull Failure Time Distribution

17.7 System Reliability

Statistics in Action: Modeling the Hazard Rate of Reinforced Concrete Bridge Deck Deterioration

APPENDIX A: MATRIX ALGEBRA

APPENDIX B: USEFUL STATISTICAL TABLES

APPENDIX C: SAS FOR WINDOWS TUTORIAL

APPENDIX D: MINITAB FOR WINDOWS TUTORIAL

APPENDIX E: SPSS FOR WINDOWS TUTORIAL

ANSWERS TO SELECTED EXERCISES

INDEX