Introductory Statistics for Engineering Experimentation
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Table of contents
- Cover
- Copyright Pageiv
- Contentsv
- Prefacexi
- Chapter 1. Introduction1
- Variability2
- Experimental Design2
- Random Sampling3
- Randomization3
- Replication4
- Problems5
- Chapter 2. Summarizing Data6
- 2.1 Simple Graphical Techniques6
- 2.2 Numerical Summaries and Box Plots18
- 2.3 Graphical Tools for Designed Experiments26
- 2.4 Chapter Problems33
- Chapter 3. Models for Experiment Outcomes35
- 3.1 Models for Single-Factor Experiments37
- 3.2 Models for Two-Factor Factorial Experiments41
- 3.3 Models for Bivariate Data49
- 3.4 Models for Multivariate Data67
- 3.5 Assessing the Fit of a Model71
- 3.6 Chapter Problems79
- Chapter 4. Models for the Random Error84
- 4.1 Random Variables84
- 4.2 Important Discrete Distributions93
- 4.3 Important Continuous Distributions106
- 4.4 Assessing the Fit of a Distribution133
- 4.5 Chapter Problems150
- Chapter 5. Inference for a Single Population155
- 5.1 Central Limit Theorem155
- 5.2 A Confidence Interval for µ165
- 5.3 Prediction and Tolerance Intervals178
- 5.4 Hypothesis Tests190
- 5.5 Inference for Binomial Populations203
- 5.6 Chapter Problems212
- Chapter 6. Comparing Two Populations217
- 6.1 Paired Samples217
- 6.2 Independent Samples223
- 6.3 Comparing Two Binomial Populations232
- 6.4 Chapter Problems242
- Chapter 7. One-Factor Multi-Sample Experiments245
- 7.1 Basic Inference246
- 7.2 The Analysis of Means250
- 7.3 ANOM with Unequal Sample Sizes261
- 7.4 ANOM for Proportions266
- 7.5 The Analysis of Variance273
- 7.6 The Equal Variances Assumption277
- 7.7 Sample Sizes287
- 7.8 Chapter Problems290
- Chapter 8. Experiments with Two Factors295
- 8.1 Interaction295
- 8.2 More Than One Observation Per Cell296
- 8.3 Only One Observation per Cell322
- 8.4 Blocking to Reduce Variability328
- 8.5 Chapter Problems334
- Chapter 9. Multi-Factor Experiments338
- 9.1 ANOVA for Multi-Factor Experiments338
- 9.2 2k Factorial Designs350
- 9.3 Fractional Factorial Designs359
- 9.4 Chapter Problems365
- Chapter 10. Inference for Regression Models369
- 10.1 Inference for a Regression Line369
- 10.2 Inference for Other Regression Models386
- 10.3 Chapter Problems391
- Chapter 11. Response Surface Methods395
- 11.1 First-Order Designs395
- 11.2 Second-Order Designs406
- 11.3 Chapter Problems423
- Chapter 12. Appendices424
- 12.1 Appendix A – Descriptions of Data Sets424
- 12.2 Appendix B – Tables442
- 12.3 Appendix C – Figures478
- 12.4 Appendix D – Sample Projects483
- Chapter 13. References508
- Index511
The Accreditation Board for Engineering and Technology (ABET) introduced a criterion starting with their 1992-1993 site visits that "Students must demonstrate a knowledge of the application of statistics to engineering problems." Since most engineering curricula are filled with requirements in their own discipline, they generally do not have time for a traditional two semesters of probability and statistics. Attempts to condense that material into a single semester often results in so much time being spent on probability that the statistics useful for designing and analyzing engineering/scientific experiments is never covered. In developing a one-semester course whose purpose was to introduce engineering/scientific students to the most useful statistical methods, this book was created to satisfy those needs.
- Provides the statistical design and analysis of engineering experiments & problems
- Presents a student-friendly approach through providing statistical models for advanced learning techniques
- Covers essential and useful statistical methods used by engineers and scientists
- Provides the statistical design and analysis of engineering experiments & problems
- Presents a student-friendly approach through providing statistical models for advanced learning techniques
- Covers essential and useful statistical methods used by engineers and scientists