An introductory perspective on statistical applications in the field of engineering Modern Engineering Statistics presents state-of-the-art statistical methodology germane to engineering applications. With a nice blend of methodology and applications, this book provides and carefully explains the concepts necessary for students to fully grasp and appreciate contemporary statistical techniques in the context of engineering.
With almost thirty years of teaching experience, many of which were spent teaching engineering statistics courses, the author has successfully developed a book that displays modern statistical techniques and provides effective tools for student use. This book features:
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Examples demonstrating the use of statistical thinking and methodology for practicing engineers
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A large number of chapter exercises that provide the opportunity for readers to solve engineering-related problems, often using real data sets
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Clear illustrations of the relationship between hypothesis tests and confidence intervals
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Extensive use of Minitab and JMP to illustrate statistical analyses
The book is written in an engaging style that interconnects and builds on discussions, examples, and methods as readers progress from chapter to chapter. The assumptions on which the methodology is based are stated and tested in applications. Each chapter concludes with a summary highlighting the key points that are needed in order to advance in the text, as well as a list of references for further reading. Certain chapters that contain more than a few methods also provide end-of-chapter guidelines on the proper selection and use of those methods. Bridging the gap between statistics education and real-world applications, Modern Engineering Statistics is ideal for either a one- or two-semester course in engineering statistics.
Table of Contents:
Preface xvii
1. Methods of Collecting and Presenting Data 1
1.1 Observational Data and Data from Designed Experiments 3
1.2 Populations and Samples 5
1.3 Variables 6
1.4 Methods of Displaying Small Data Sets 7
1.5 Methods of Displaying Large Data Sets 16
1.6 Outliers 22
1.7 Other Methods 22
1.8 Extremely Large Data Sets: Data Mining 23
1.9 Graphical Methods: Recommendations 23
1.10 Summary 24
References 24
Exercises 25
2. Measures of Location and Dispersion 45
2.1 Estimating Location Parameters 46
2.2 Estimating Dispersion Parameters 50
2.3 Estimating Parameters from Grouped Data 55
2.4 Estimates from a Boxplot 57
2.5 Computing Sample Statistics with MINITAB 58
2.6 Summary 58
Reference 58
Exercises 58
3. Probability and Common Probability Distributions 68
3.1 Probability: From the Ethereal to the Concrete 68
3.3 Common Discrete Distributions 76
3.4 Common Continuous Distributions 92
3.5 General Distribution Fitting 106
3.6 How to Select a Distribution 107
3.7 Summary 108
References 109
Exercises 109
4. Point Estimation 121
4.1 Point Estimators and Point Estimates 121
4.2 Desirable Properties of Point Estimators 121
4.3 Distributions of Sampling Statistics 125
4.4 Methods of Obtaining Estimators 128
4.5 Estimating σθ 132
4.6 Estimating Parameters Without Data 133
4.7 Summary 133
References 134
Exercises 134
5. Confidence Intervals and Hypothesis Tests—One Sample 140
5.1 Confidence Interval for μ: Normal Distribution σ Not Estimated from Sample Data 140
5.2 Confidence Interval for μ: Normal Distribution σ Estimated from Sample Data 146
5.3 Hypothesis Tests for μ: Using Z and t 147
5.4 Confidence Intervals and Hypothesis Tests for a Proportion 157
5.5 Confidence Intervals and Hypothesis Tests for σ2 and σ 161
5.6 Confidence Intervals and Hypothesis Tests for the Poisson Mean 164
5.7 Confidence Intervals and Hypothesis Tests When Standard Error Expressions are Not Available 166
5.8 Type I and Type II Errors 168
5.9 Practical Significance and Narrow Intervals: The Role of n 172
5.10 Other Types of Confidence Intervals 173
5.11 Abstract of Main Procedures 174
5.12 Summary 175
Appendix: Derivation 176
References 176
Exercises 177
6. Confidence Intervals and Hypothesis Tests—Two Samples 189
6.1 Confidence Intervals and Hypothesis Tests for Means: Independent Samples 189
6.2 Confidence Intervals and Hypothesis Tests for Means: Dependent Samples 197
6.3 Confidence Intervals and Hypothesis Tests for Two Proportions 200
6.4 Confidence Intervals and Hypothesis Tests for Two Variances 202
6.5 Abstract of Procedures 204
6.6 Summary 205
References 205
Exercises 205
7. Tolerance Intervals and Prediction Intervals 214
7.1 Tolerance Intervals: Normality Assumed 215
7.2 Tolerance Intervals and Six Sigma 219
7.3 Distribution-Free Tolerance Intervals 219
7.4 Prediction Intervals 221
7.5 Choice Between Intervals 227
7.6 Summary 227
References 228
Exercises 229
8. Simple Linear Regression Correlation and Calibration 232
8.1 Introduction 232
8.2 Simple Linear Regression 232
8.3 Correlation 254
8.4 Miscellaneous Uses of Regression 256
8.5 Summary 264
References 264
Exercises 265
9. Multiple Regression 276
9.1 How Do We Start? 277
9.2 Interpreting Regression Coefficients 278
9.3 Example with Fixed Regressors 279
9.4 Example with Random Regressors 281
9.5 Example of Section 8.2.4 Extended 291
9.6 Selecting Regression Variables 293
9.7 Transformations 299
9.8 Indicator Variables 300
9.9 Regression Graphics 300
9.10 Logistic Regression and Nonlinear Regression Models 301
9.11 Regression with Matrix Algebra 302
9.12 Summary 302
References 303
Exercises 304
10. Mechanistic Models 314
10.1 Mechanistic Models 315
10.2 Empirical–Mechanistic Models 316
10.3 Additional Examples 324
10.4 Software 325
10.5 Summary 326
References 326
Exercises 327
11. Control Charts and Quality Improvement 330
11.1 Basic Control Chart Principles 330
11.2 Stages of Control Chart Usage 331
11.3 Assumptions and Methods of Determining Control Limits 334
11.4 Control Chart Properties 335
11.5 Types of Charts 336
11.6 Shewhart Charts for Controlling a Process Mean and Variability (Without Subgrouping) 336
11.7 Shewhart Charts for Controlling a Process Mean and Variability (With Subgrouping) 344
11.8 Important Use of Control Charts for Measurement Data 349
11.9 Shewhart Control Charts for Nonconformities and Nonconforming Units 349
11.10 Alternatives to Shewhart Charts 356
11.11 Finding Assignable Causes 359
11.12 Multivariate Charts 362
11.13 Case Study 362
11.14 Engineering Process Control 364
11.15 Process Capability 365
11.16 Improving Quality with Designed Experiments 366
11.17 Six Sigma 367
11.18 Acceptance Sampling 368
11.19 Measurement Error 368
11.20 Summary 368
References 369
Exercises 370
12. Design and Analysis of Experiments 382
12.1 Processes Must be in Statistical Control 383
12.2 One-Factor Experiments 384
12.3 One Treatment Factor and at Least One Blocking Factor 392
12.4 More Than One Factor 395
12.5 Factorial Designs 396
12.6 Crossed and Nested Designs 405
12.7 Fixed and Random Factors 406
12.8 ANOM for Factorial Designs 407
12.9 Fractional Factorials 409
12.10 Split-Plot Designs 413
12.11 Response Surface Designs 414
12.12 Raw Form Analysis Versus Coded Form Analysis 415
12.13 Supersaturated Designs 416
12.14 Hard-to-Change Factors 416
12.15 One-Factor-at-a-Time Designs 417
12.16 Multiple Responses 418
12.17 Taguchi Methods of Design 419
12.18 Multi-Vari Chart 420
12.19 Design of Experiments for Binary Data 420
12.20 Evolutionary Operation (EVOP) 421
12.21 Measurement Error 422
12.22 Analysis of Covariance 422
12.23 Summary of MINITAB and Design-Expert® Capabilities for Design of Experiments 422
12.24 Training for Experimental Design Use 423
12.25 Summary 423
Appendix A Computing Formulas 424
Appendix B Relationship Between Effect Estimates and
Regression Coefficients 426
References 426
Exercises 428
13. Measurement System Appraisal 441
13.1 Terminology 442
13.2 Components of Measurement Variability 443
13.3 Graphical Methods 449
13.4 Bias and Calibration 449
13.5 Propagation of Error 454
13.6 Software 455
13.7 Summary 456
References 456
Exercises 457
14. Reliability Analysis and Life Testing 460
14.1 Basic Reliability Concepts 461
14.2 Nonrepairable and Repairable Populations 463
14.3 Accelerated Testing 463
14.4 Types of Reliability Data 466
14.5 Statistical Terms and Reliability Models 467
14.6 Reliability Engineering 473
14.7 Example 474
14.8 Improving Reliability with Designed Experiments 474
14.9 Confidence Intervals 477
14.10 Sample Size Determination 478
14.11 Reliability Growth and Demonstration Testing 479
14.12 Early Determination of Product Reliability 480
14.13 Software 480
14.14 Summary 481
References 481
Exercises 482
15. Analysis of Categorical Data 487
15.1 Contingency Tables 487
15.2 Design of Experiments: Categorical Response Variable 497
15.3 Goodness-of-Fit Tests 498
15.4 Summary 500
References 500
Exercises 501
16. Distribution-Free Procedures 507
16.1 Introduction 507
16.2 One-Sample Procedures 508
16.3 Two-Sample Procedures 512
16.4 Nonparametric Analysis of Variance 514
16.5 Exact Versus Approximate Tests 519
16.6 Nonparametric Regression 519
16.7 Nonparametric Prediction Intervals and Tolerance Intervals 521
16.8 Summary 521
References 521
Exercises 522
17. Tying It All Together 525
17.1 Review of Book 525
17.2 The Future 527
17.3 Engineering Applications of Statistical Methods 528
Reference 528
Exercises 528
Answers to Selected Excercises 533
Appendix: Statistical Tables 562
Table A Random Numbers 562
Table B Normal Distribution 564
Table C t-Distribution 566
Table D F-Distribution 567
Table E Factors for Calculating Two-Sided 99% Statistical Intervals for a Normal Population to Contain at Least 100p% of the Population 570
Table F Control Chart Constants 571
Author Index 573
Subject Index 579
