Engineering Statistics, SI Version

Montgomery, Runger, and Hubele provide modern coverage of engineering statistics, focusing on how statistical tools are integrated into the engineering problem-solving process. All major aspects of engineering statistics are covered, including descriptive statistics, probability and probability distributions, statistical test and confidence intervals for one and two samples, building regression models, designing and analyzing engineering experiments, and statistical process control. Developed with sponsorship from the National Science Foundation, this revision incorporates many insights from the authors' teaching experience along with feedback from numerous adopters of previous editions.

Table of Contents:
Chapter 1 The Role of Statistics in Engineering 1 1-1 The Engineering Method and Statistical Thinking 2 1-2 Collecting Engineering Data 6 1-2.1 Retrospective Study 7 1-2.2 Observational Study 8 1-2.3 Designed Experiments 9 1-2.4 Random Samples 12 1-3 Mechanistic and Empirical Models 15 1-4 Observing Processes Over Time 17 Chapter 2 Data Summary and Presentation 23 2-1 Data Summary and Display 24 2-2 Stem-and-Leaf Diagram 29 2-3 Histograms 34 2-4 Box Plot 39 2-5 Time Series Plots 41 2-6 Multivariate Data 46 Chapter 3 Random Variables and Probability Distributions 57 3-1 Introduction 58 3-2 Random Variables 60 3-3 Probability 62 3-4 Continuous Random Variables 66 3-4.1 Probability Density Function 66 3-4.2 Cumulative Distribution Function 68 3-4.3 Mean and Variance 70 3-5 Important Continuous Distributions 74 3-5.1 Normal Distribution 74 3-5.2 Lognormal Distribution 84 3-5.3 Gamma Distribution 86 3-5.4 Weibull Distribution 86 3-5.5 Beta Distribution 88 3-6 Probability Plots 92 3-6.1 Normal Probability Plots 92 3-6.2 Other Probability Plots 94 3-7 Discrete Random Variables 97 3-7.1 Probability Mass Function 97 3-7.2 Cumulative Distribution Function 98 3-7.3 Mean and Variance 99 3-8 Binomial Distribution 102 3-9 Poisson Process 109 3-9.1 Poisson Distribution 109 3-9.2 Exponential Distribution 113 3-10 Normal Approximation to the Binomial and Poisson Distributions 119 3-11 More than One Random Variable and Independence 123 3-11.1 Joint Distributions 123 3-11.2 Independence 124 3-12 Functions of Random Variables 129 3-12.1 Linear Functions of Independent Random Variables 130 3-12.2 Linear Functions of Random Variables That are Not Independent 131 3-12.3 Nonlinear Functions of Independent Random Variables 133 3-13 Random Samples, Statistics, and the Central Limit Theorem 136 Chapter 4 Decision Making for a Single Sample 148 4-1 Statistical Inference 149 4-2 Point Estimation 150 4-3 Hypothesis Testing 156 4-3.1 Statistical Hypotheses 156 4-3.2 Testing Statistical Hypotheses 158 4-3.3 P-Values in Hypothesis Testing 164 4-3.4 One-Sided and Two-Sided Hypotheses 166 4-3.5 General Procedure for Hypothesis Testing 167 4-4 Inference on the Mean of a Population, Variance Known 169 4-4.1 Hypothesis Testing on the Mean 169 4-4.2 Type II Error and Choice of Sample Size 173 4-4.3 Large-Sample Test 177 4-4.4 Some Practical Comments on Hypothesis Testing 177 4-4.5 Confidence Interval on the Mean 178 4-4.6 General Method for Deriving a Confidence Interval 184 4-5 Inference on the Mean of a Population, Variance Unknown 186 4-5.1 Hypothesis Testing on the Mean 187 4-5.2 Type II Error and Choice of Sample Size 193 4-5.3 Confidence Interval on the Mean 195 4-6 Inference on the Variance of a Normal Population 199 4-6.1 Hypothesis Testing on the Variance of a Normal Population 199 4-6.2 Confidence Interval on the Variance of a Normal Population 203 4-7 Inference on a Population Proportion 205 4-7.1 Hypothesis Testing on a Binomial Proportion 205 4-7.2 Type II Error and Choice of Sample Size 208 4-7.3 Confidence Interval on a Binomial Proportion 210 4-8 Other Interval Estimates for a Single Sample 216 4-8.1 Prediction Interval 216 4-8.2 Tolerance Intervals for a Normal Distribution 217 4-9 Summary Tables of Inference Procedures for a Single Sample 219 4-10 Testing for Goodness of Fit 219 Chapter 5 Decision Making for Two Samples 230 5-1 Introduction 231 5-2 Inference on the Means of Two Populations, Variances Known 232 5-2.1 Hypothesis Testing on the Difference in Means, Variances Known 233 5-2.2 Type II Error and Choice of Sample Size 234 5-2.3 Confidence Interval on the Difference in Means, Variances Known 235 5-3 Inference on the Means of Two Populations, Variances Unknown 239 5-3.1 Hypothesis Testing on the Difference in Means 239 5-3.2 Type II Error and Choice of Sample Size 246 5-3.3 Confidence Interval on the Difference in Means 247 5-4 The Paired t-Test 252 5-5 Inference on the Ratio of Variances of Two Normal Populations 259 5-5.1 Hypothesis Testing on the Ratio of Two Variances 259 5-5.2 Confidence Interval on the Ratio of Two Variances 263 5-6 Inference on Two Population Proportions 265 5-6.1 Hypothesis Testing on the Equality of Two Binomial Proportions 265 5-6.2 Type II Error and Choice of Sample Size 268 5-6.3 Confidence Interval on the Difference in Binomial Proportions 269 5-7 Summary Tables for Inference Procedures for Two Samples 271 5-8 What if We Have More than Two Samples? 272 5-8.1 Completely Randomized Experiment and Analysis of Variance 272 5-8.2 Randomized Complete Block Experiment 281 Chapter 6 Building Empirical Models 298 6-1 Introduction to Empirical Models 299 6-2 Simple Linear Regression 304 6-2.1 Least Squares Estimation 304 6-2.2 Testing Hypotheses in Simple Linear Regression 312 6-2.3 Confidence Intervals in Simple Linear Regression 315 6-2.4 Prediction of a Future Observation 318 6-2.5 Checking Model Adequacy 319 6-2.6 Correlation and Regression 322 6-3 Multiple Regression 326 6-3.1 Estimation of Parameters in Multiple Regression 326 6-3.2 Inferences in Multiple Regression 331 6-3.3 Checking Model Adequacy 336 6-4 Other Aspects of Regression 344 6-4.1 Polynomial Models 344 6-4.2 Categorical Regressors 346 6-4.3 Variable Selection Techniques 348 Chapter 7 Design of Engineering Experiments 360 7-1 The Strategy of Experimentation 361 7-2 Factorial Experiments 362 7-3 2k Factorial Design 365 7-3.1 22 Design 366 7-3.2 Statistical Analysis 368 7-3.3 Residual Analysis and Model Checking 374 7-3.4 2k Design for k ≥ 3 Factors 376 7-3.5 Single Replicate of a 2k Design 382 7-4 Center Points and Blocking in 2k Designs 390 7-4.1 Addition of Center Points 390 7-4.2 Blocking and Confounding 393 7-5 Fractional Replication of a 2k Design 398 7-5.1 One-Half Fraction of a 2k Design 398 7-5.2 Smaller Fractions: 2k-pFractional Factorial Designs 404 7-6 Response Surface Methods and Designs 414 7-6.1 Method of Steepest Ascent 416 7-6.2 Analysis of a Second-Order Response Surface 418 7-7 Factorial Experiments With More Than Two Levels 424 Chapter 8 Statistical Process Control 438 8-1 Quality Improvement and Statistical Process Control 439 8-2 Introduction to Control Charts 440 8-2.1 Basic Principles 440 8-2.2 Design of a Control Chart 444 8-2.3 Rational Subgroups 446 8-2.4 Analysis of Patterns on Control Charts 447 8-3 X̄ and R Control Charts 449 8-4 Control Charts For Individual Measurements 456 8-5 Process Capability 461 8-6 Attribute Control Charts 465 8-6.1 P Chart (Control Chart for Proportions) and nP Chart 465 8-6.2 U Chart (Control Chart for Average Number of Defects per Unit) and C Chart 467 8-7 Control Chart Performance 470 8-8 Measurement Systems Capability 473 Appendices 483 Appendix A Statistical Tables and Charts 485 Appendix B Bibliography 500 Appendix C* Answers to Selected Exercises 502 Index 511