Engineering Statistics, SI Version

Häftad, Engelska, 2011

Av Douglas C. Montgomery, George C. Runger, Norma F. Hubele, Douglas C. (Georgia Institute of Technology) Montgomery, George C. (Rensselaer Polytechnic Institute) Runger, Norma F. (Arizona State University) Hubele

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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.

Produktinformation

Utgivningsdatum2011-12-09
Mått201 x 252 x 15 mm
Vikt907 g
FormatHäftad
SpråkEngelska
Antal sidor544
Upplaga5
FörlagJohn Wiley & Sons Inc
ISBN9780470646076

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Chapter 1 The Role of Statistics in Engineering 11-1 The Engineering Method and Statistical Thinking 21-2 Collecting Engineering Data 61-2.1 Retrospective Study 71-2.2 Observational Study 81-2.3 Designed Experiments 91-2.4 Random Samples 121-3 Mechanistic and Empirical Models 151-4 Observing Processes Over Time 17Chapter 2 Data Summary and Presentation 232-1 Data Summary and Display 242-2 Stem-and-Leaf Diagram 292-3 Histograms 342-4 Box Plot 392-5 Time Series Plots 412-6 Multivariate Data 46Chapter 3 Random Variables and Probability Distributions 573-1 Introduction 583-2 Random Variables 603-3 Probability 623-4 Continuous Random Variables 663-4.1 Probability Density Function 663-4.2 Cumulative Distribution Function 683-4.3 Mean and Variance 703-5 Important Continuous Distributions 743-5.1 Normal Distribution 743-5.2 Lognormal Distribution 843-5.3 Gamma Distribution 863-5.4 Weibull Distribution 863-5.5 Beta Distribution 883-6 Probability Plots 923-6.1 Normal Probability Plots 923-6.2 Other Probability Plots 943-7 Discrete Random Variables 973-7.1 Probability Mass Function 973-7.2 Cumulative Distribution Function 983-7.3 Mean and Variance 993-8 Binomial Distribution 1023-9 Poisson Process 1093-9.1 Poisson Distribution 1093-9.2 Exponential Distribution 1133-10 Normal Approximation to the Binomial and Poisson Distributions 1193-11 More than One Random Variable and Independence 1233-11.1 Joint Distributions 1233-11.2 Independence 1243-12 Functions of Random Variables 1293-12.1 Linear Functions of Independent Random Variables 1303-12.2 Linear Functions of Random Variables That are Not Independent 1313-12.3 Nonlinear Functions of Independent Random Variables 1333-13 Random Samples, Statistics, and the Central Limit Theorem 136Chapter 4 Decision Making for a Single Sample 1484-1 Statistical Inference 1494-2 Point Estimation 1504-3 Hypothesis Testing 1564-3.1 Statistical Hypotheses 1564-3.2 Testing Statistical Hypotheses 1584-3.3 P-Values in Hypothesis Testing 1644-3.4 One-Sided and Two-Sided Hypotheses 1664-3.5 General Procedure for Hypothesis Testing 1674-4 Inference on the Mean of a Population, Variance Known 1694-4.1 Hypothesis Testing on the Mean 1694-4.2 Type II Error and Choice of Sample Size 1734-4.3 Large-Sample Test 1774-4.4 Some Practical Comments on Hypothesis Testing 1774-4.5 Confidence Interval on the Mean 1784-4.6 General Method for Deriving a Confidence Interval 1844-5 Inference on the Mean of a Population, Variance Unknown 1864-5.1 Hypothesis Testing on the Mean 1874-5.2 Type II Error and Choice of Sample Size 1934-5.3 Confidence Interval on the Mean 1954-6 Inference on the Variance of a Normal Population 1994-6.1 Hypothesis Testing on the Variance of a Normal Population 1994-6.2 Confidence Interval on the Variance of a Normal Population 2034-7 Inference on a Population Proportion 2054-7.1 Hypothesis Testing on a Binomial Proportion 2054-7.2 Type II Error and Choice of Sample Size 2084-7.3 Confidence Interval on a Binomial Proportion 2104-8 Other Interval Estimates for a Single Sample 2164-8.1 Prediction Interval 2164-8.2 Tolerance Intervals for a Normal Distribution 2174-9 Summary Tables of Inference Procedures for a Single Sample 2194-10 Testing for Goodness of Fit 219Chapter 5 Decision Making for Two Samples 2305-1 Introduction 2315-2 Inference on the Means of Two Populations, Variances Known 2325-2.1 Hypothesis Testing on the Difference in Means, Variances Known 2335-2.2 Type II Error and Choice of Sample Size 2345-2.3 Confidence Interval on the Difference in Means, Variances Known 2355-3 Inference on the Means of Two Populations, Variances Unknown 2395-3.1 Hypothesis Testing on the Difference in Means 2395-3.2 Type II Error and Choice of Sample Size 2465-3.3 Confidence Interval on the Difference in Means 2475-4 The Paired t-Test 2525-5 Inference on the Ratio of Variances of Two Normal Populations 2595-5.1 Hypothesis Testing on the Ratio of Two Variances 2595-5.2 Confidence Interval on the Ratio of Two Variances 2635-6 Inference on Two Population Proportions 2655-6.1 Hypothesis Testing on the Equality of Two Binomial Proportions 2655-6.2 Type II Error and Choice of Sample Size 2685-6.3 Confidence Interval on the Difference in Binomial Proportions 2695-7 Summary Tables for Inference Procedures for Two Samples 2715-8 What if We Have More than Two Samples? 2725-8.1 Completely Randomized Experiment and Analysis of Variance 2725-8.2 Randomized Complete Block Experiment 281Chapter 6 Building Empirical Models 2986-1 Introduction to Empirical Models 2996-2 Simple Linear Regression 3046-2.1 Least Squares Estimation 3046-2.2 Testing Hypotheses in Simple Linear Regression 3126-2.3 Confidence Intervals in Simple Linear Regression 3156-2.4 Prediction of a Future Observation 3186-2.5 Checking Model Adequacy 3196-2.6 Correlation and Regression 3226-3 Multiple Regression 3266-3.1 Estimation of Parameters in Multiple Regression 3266-3.2 Inferences in Multiple Regression 3316-3.3 Checking Model Adequacy 3366-4 Other Aspects of Regression 3446-4.1 Polynomial Models 3446-4.2 Categorical Regressors 3466-4.3 Variable Selection Techniques 348Chapter 7 Design of Engineering Experiments 3607-1 The Strategy of Experimentation 3617-2 Factorial Experiments 3627-3 2k Factorial Design 3657-3.1 22 Design 3667-3.2 Statistical Analysis 3687-3.3 Residual Analysis and Model Checking 3747-3.4 2k Design for k ≥ 3 Factors 3767-3.5 Single Replicate of a 2k Design 3827-4 Center Points and Blocking in 2k Designs 3907-4.1 Addition of Center Points 3907-4.2 Blocking and Confounding 3937-5 Fractional Replication of a 2k Design 3987-5.1 One-Half Fraction of a 2k Design 3987-5.2 Smaller Fractions: 2k-pFractional Factorial Designs 4047-6 Response Surface Methods and Designs 4147-6.1 Method of Steepest Ascent 4167-6.2 Analysis of a Second-Order Response Surface 4187-7 Factorial Experiments With More Than Two Levels 424Chapter 8 Statistical Process Control 4388-1 Quality Improvement and Statistical Process Control 4398-2 Introduction to Control Charts 4408-2.1 Basic Principles 4408-2.2 Design of a Control Chart 4448-2.3 Rational Subgroups 4468-2.4 Analysis of Patterns on Control Charts 4478-3 X̄ and R Control Charts 4498-4 Control Charts For Individual Measurements 4568-5 Process Capability 4618-6 Attribute Control Charts 4658-6.1 P Chart (Control Chart for Proportions) and nP Chart 4658-6.2 U Chart (Control Chart for Average Number of Defects per Unit) and C Chart 4678-7 Control Chart Performance 4708-8 Measurement Systems Capability 473Appendices 483Appendix A Statistical Tables and Charts 485Appendix B Bibliography 500Appendix C* Answers to Selected Exercises 502Index 511