Applied Engineering Statistics, 2nd Edition
- Length: 500 pages
- Edition: 2
- Language: English
- Publisher: CRC Press
- Publication Date: 2021-11-02
- ISBN-10: 1032119489
- ISBN-13: 9781032119489
- Sales Rank: #0 (See Top 100 Books)
Thoroughly updated throughout, this second edition will continue to be about the practicable methods of statistical applications for engineers, and as well for scientists and those in business. It remains a what-I-wish-I-had-known-when-starting-my-career compilation of techniques.
Contrasting a mathematical and abstract orientation of many statistics texts, which expresses the science/math values of researchers, this book has its focus on the application to concrete examples and the interpretation of outcomes. Supporting application propriety, this book also presents the fundamental concepts, provides supporting derivation, and has frequent do and not-do notes.
Key Features:
- Contains details of the computation for the examples.
- Includes new examples and exercises.
- Includes expanded topics supporting data analysis.
The book is for upper-level undergraduate or graduate students in engineering, the hard sciences, or business programs. The intent is that the text would continue to be useful in professional life, and appropriate as a self-learning tool after graduation – whether in graduate school or in professional practice.
Cover Half Title Title Page Copyright Page Disclaimer Table of Contents Nomenclature Preface to Second Edition. Section 1 Fundamentals of Probability and Statistics Chapter 1 Introduction 1.1 Introduction 1.2 Deterministic and Stochastic 1.3 Treatments, Process, and Outcomes 1.4 Uses of Statistics 1.5 Stationarity 1.6 There are No Absolutes 1.7 A Caution on Statements of Confidence 1.8 Correlation is Not Causation 1.9 Uncertainty and Disparate Metrics 1.10 Takeaway 1.11 Exercises Chapter 2 Probability 2.1 Probability 2.2 Probability Calculations 2.2.1 A Priori Probability Calculations 2.2.1.1 Rule 1: Multiplication 2.2.1.2 Rule 2: Addition 2.2.2 Conditional Probability Calculations 2.2.3 Bayes’ Belief Calculations 2.3 Takeaway 2.4 Exercises Chapter 3 Distributions 3.1 Introduction 3.2 Definitions 3.3 Discrete Distributions 3.3.1 Discrete Uniform Distribution 3.3.2 Binomial Distribution 3.3.3 Poisson Distribution 3.3.4 Negative Binomial Distribution 3.3.5 Hypergeometric Distribution 3.3.6 Geometric Distribution 3.4 Continuous Distributions 3.4.1 Continuous Uniform Distribution 3.4.2 Proportion 3.4.3 Exponential Distribution 3.4.4 Gamma Distribution 3.4.5 Normal Distribution 3.4.6 “Student’s” t-Distribution 3.4.7 Chi-Squared Distribution 3.4.8 F-Distribution 3.4.9 Log-Normal Distribution 3.4.10 Weibull Distribution 3.5 Experimental Distributions for Continuum-Valued Data 3.6 Values of Distributions and Inverses 3.6.1 For Continuum-Valued Variables 3.6.2 For Discrete-Valued Variables 3.7 Distribution Properties, Identities, and Excel Cell Functions 3.7.1 Continuum-Valued Variables 3.7.1.1 Standard Normal Distribution 3.7.1.2 t-Distribution 3.7.1.3 Chi-Squared Distribution 3.7.1.4 F-Distribution 3.7.2 Discrete-Valued Variables 3.7.2.1 Binomial Distribution 3.7.2.2 Poisson Distribution 3.8 Propagating Distributions with Variable Transformations 3.9 Takeaway 3.10 Exercises Chapter 4 Descriptive Statistics 4.1 Measures of Location (Centrality) 4.2 Measures of Variability 4.3 Measures of Patterns in the Data 4.4 Scaled Measures of Deviations 4.5 Degrees of Freedom 4.6 Expectation 4.7 A Note about Dimensional Consistency 4.7.1 Average and Central Limit Representations 4.7.2 Dimensional Consistency in Other Equations (An Aside) 4.8 Takeaway 4.9 Exercises Chapter 5 Data and Parameter Interval Estimation 5.1 Interval Estimation 5.1.1 Continuous Distributions 5.1.2 Discrete Distributions 5.2 Distribution Parameter Estimation 5.2.1 Continuous Distributions 5.2.2 Discrete Distributions 5.3 Approximation with the Normal Distribution 5.4 Empirical Data 5.4.1 Data Range 5.4.2 Empirical Distribution Parameter Range 5.5 Takeaway 5.6 Exercises Chapter 6 Hypothesis Formulation and Testing – Parametric Tests 6.1 Introduction 6.1.1 Critical Value Method 6.1.2 p-Value Assessment 6.1.3 Probability Ratio Method 6.1.4 What Distribution to Use? 6.2 Types of Hypothesis Testing Errors 6.3 Two-Sided and One-Sided Tests 6.4 Tests about the Mean 6.5 Tests on the Difference of Two Means 6.5.1 Case 1 ( and Known) 6.5.2 Case 2 ( and Both Unknown but Presumed Equal) 6.5.3 Case 3 ( and Both Unknown and Presumed Unequal) 6.5.4 An Interpretation of the Comparison of Means – A One-Sided Test 6.6 Paired t-Test 6.7 Tests on a Single Variance 6.8 Tests Concerning Two Variances 6.9 Characterizing Experimental Distributions 6.10 Contingency Tests 6.11 Testing Proportions 6.12 Testing Probable Outcomes 6.13 Takeaway 6.14 Exercises Chapter 7 Nonparametric Hypothesis Tests 7.1 Introduction 7.2 The Sign Test 7.3 Wilcoxon Signed-Rank Test 7.4 Modification to the Sign and Signed-Rank Tests 7.5 Runs Test 7.6 Chi-Squared Goodness-of-Fit Test 7.7 Kolmogorov-Smirnov Goodness-of-Fit Test 7.8 Takeaway 7.9 Exercises Chapter 8 Reporting and Propagating Uncertainty in Calculations 8.1 Introduction 8.1.1 Applications 8.1.2 Objectives/Rationale 8.1.3 Propagation of Uncertainty 8.1.4 Nomenclature 8.2 Fundamentals 8.2.1 What Experimental Variation is and is Not 8.2.2 Measures of Random Variation 8.2.3 Sources of Variation 8.2.4 Data and Process Models 8.2.5 Explicit and Implicit Models 8.2.6 Significant Digits 8.2.7 Estimating Uncertainty on Input Values 8.2.8 Random and Systematic Error 8.2.9 Coefficient Error Types 8.3 Propagation of Uncertainty in Models 8.3.1 Analytical Method for Maximum Uncertainty 8.3.2 Analytical Method for Probable Uncertainty 8.3.3 Numerical Method for Maximum Uncertainty 8.3.4 Numerical Method for Probable Uncertainty 8.4 Identifying Key Sources of Uncertainty 8.5 Bias and Precision 8.6 Takeaway 8.7 Exercises Chapter 9 Stochastic Simulation 9.1 Introduction 9.2 Generating Data That Represents a Distribution 9.3 Generating Data That Represents Natural Variation over Time 9.4 Generating Stochastic Models 9.5 Number of Realizations Needed for Various Statistics 9.6 Correlated and Conditional Perturbations 9.7 Takeaway 9.8 Exercises Section 2 Choices Chapter 10 Choices 10.1 Introduction 10.2 Cases 10.2.1 The Hypothesis 10.2.2 Truncating or Rounding 10.2.3 The Conclusion 10.2.4 Data Collection 10.2.5 Data Preprocessing 10.2.6 Data Post Processing 10.2.7 Choice of Distribution and Test 10.2.8 Choice of N 10.2.9 Parametric or Nonparametric Test 10.2.10 Level of Confidence, Level of Significance, T-I Error, Alpha 10.2.11 One-Sided or Two-Sided Test 10.2.12 Choosing the Quantity for Comparison 10.2.13 Use the Mean or the Probability of an Extreme Value? 10.2.14 Correlation vs Causation 10.2.15 Intuition 10.2.16 A Possible Method to Determine Values of α, β, and N 10.2.17 The Hypothesis is not the Supposition 10.2.18 Seek to Reject, Not to Support 10.3 Takeaway 10.4 Exercises Section 3 Applications of Probability and Statistical Fundamentals Chapter 11 Risk 11.1 Introduction 11.2 Estimating the Financial Penalty 11.3 Frequency or Probability? 11.3.1 Estimating Event Probability – Independent Events 11.3.2 Estimating Event Probability – Common Cause Events 11.3.3 Intermittent or Continuous Use 11.3.4 Catastrophic Events 11.4 Estimating the Penalty from Multiple Possible Events 11.5 Using Risk in Comparing Treatments 11.6 Uncertainty 11.7 Detectability 11.8 Achieving Zero Risk 11.9 Takeaway 11.10 Exercises Chapter 12 Analysis of Variance 12.1 Introduction 12.2 One-Way ANOVA 12.2.1 One-Way ANOVA Method 12.2.2 Alternate Analysis Approaches 12.2.3 Model for One-Way Analysis of Variance 12.2.4 Subsampling in One-Way Analysis of Variance 12.3 Two-Way Analysis of Variance 12.3.1 Model for Two-Way Analysis of Variance 12.3.2 Two-Way Analysis of Variance Without Replicates 12.3.3 Interaction in Two-Way ANOVA 12.3.4 Two-Way Analysis of Variance with Replicates 12.4 Takeaway 12.5 Exercises Chapter 13 Correlation 13.1 Introduction 13.2 Correlation Between Variables 13.2.1 Method 13.2.2 An Illustration 13.2.3 Determining Confidence in a Correlation 13.3 Autocorrelation 13.3.1 Method 13.3.2 An Autocorrelation Illustration 13.3.3 Determining Confidence in Autocorrelation 13.4 Takeaway 13.5 Exercises Chapter 14 Steady State and Transient State Identification in Noisy Processes 14.1 Introduction 14.1.1 Approaches and Issues to SSID and TSID 14.2 A Ratio of Variances Methods 14.2.1 Filter Method 14.2.2 Choice of Filter Factor Values 14.2.3 Critical Values 14.2.4 Illustration 14.2.5 Discussion of Important Attributes 14.2.5.1 Distribution Separation 14.2.5.2 Average Run Length 14.2.5.3 Balancing ARL, T-I and T-II Errors 14.2.5.4 Distribution Robustness 14.2.5.5 Autocorrelation 14.2.5.6 Signal Discretization 14.2.5.7 Aberrational Autocorrelation 14.2.5.8 Multivariable Extension 14.2.5.9 Cross Correlation 14.2.5.10 Selection of Variables 14.2.5.11 Noiseless Data 14.2.6 Alternate R-Statistic Structure-Array 14.3 4Point Method 14.4 Using SSID as Regression Convergence Criterion 14.5 Using SSID as Stochastic Optimization Convergence Criterion 14.6 Takeaway 14.7 Exercises Chapter 15 Linear Regression – Steady-State Models 15.1 Introduction 15.2 Simple Linear Regression 15.2.1 Hypotheses in Simple Linear Regression 15.2.2 Interval Estimation in Simple Linear Regression 15.2.3 Inverse Prediction in Simple Linear Regression 15.2.4 Evaluation of Outliers 15.2.5 Testing Equality of Slopes 15.2.6 Regression Through a Point 15.2.7 Measures of Goodness-of-Fit 15.3 Multiple Linear Regression 15.4 Polynomial Regression 15.4.1 Determining Model Complexity 15.4.2 Culling Irrelevant Model Functionalities 15.4.3 Extrapolation of Polynomial Models 15.5 Functional Linearization of Models with Nonlinear Coefficients 15.6 Takeaway 15.7 Exercises Chapter 16 Nonlinear Regression – An Introduction 16.1 Introduction 16.2 Takeaway 16.3 Exercises Chapter 17 Experimental Replicate Planning and Testing 17.1 Introduction 17.2 A Priori Estimation of N 17.2.1 Classic Estimation of n 17.2.2 Economic Estimation of n – Method 17.2.3 Economic Estimation of n – Method 17.2.4 Economic Estimation of n – Method 17.3 A Posteriori Estimation of N 17.4 Takeaway 17.5 Exercises Chapter 18 Experimental Design for Linear Steady-State Models – Screening Designs 18.1 Introduction 18.2 Random Ordering of the Experimental Sequence 18.3 Factorial Experiments 18.3.1 Constraints 18.3.2 Missing Data 18.3.3 Confounding 18.3.4 Alternate Screening Trial Designs 18.4 Takeaway 18.5 Exercises Chapter 19 Data-Based Model Validation 19.1 Introduction 19.2 Data-Based Evaluation Criteria and Tests 19.3 Bootstrapping to Estimate Model Uncertainty 19.4 Test for Variance Expectations 19.4.1 Trouble Shooting Variance Indications 19.5 Closing Remarks 19.6 Takeaway 19.7 Exercises Chapter 20 Experimental Design for Data-Based Model Validation 20.1 Introduction 20.2 Patterns Desired and Undesired 20.3 An experimental Plan 20.4 Data Sources and Other Modeling Objectives 20.5 Takeaway 20.6 Exercises Chapter 21 Statistical Process Control 21.1 SPC Concepts 21.2 Process Capability 21.3 Mean and Range Charts 21.4 Modifications to the and R Charts 21.5 CUSUM and RUNSUM Charts 21.6 Attribute Charts: Nonconforming 21.7 Attribute Charts: Defects 21.8 Takeaway 21.9 Exercises Chapter 22 Reliability 22.1 Introduction 22.2 Probability Distributions 22.3 Calculation of Composite Probabilities 22.3.1 “And” Events 22.2.2 “Or” Events 22.3.3 Combinations of Events 22.3.4 Conditional Events 22.3.5 Weakest Link 22.4 Measures of Reliability 22.4.1 Average Life or Mean Time to Failure 22.4.2 On-Stream Time 22.4.3 Monte Carlo Techniques 22.5 Reliability in Process Design Choices 22.5.1 Sizing Equipment in Series 22.5.2 Selecting Redundancy 22.5.3 Selecting Reliability of Component Parts 22.6 Takeaway 22.7 Exercises Section 4 Case Studies Case Study 1 – DJIA and Political Party Exercises Case Study 2 – PBT Justification for a Change Exercises Case Study 3 – Central Limit Phenomena and μ and σ Exercises Case Study 4 – A Corkboard Exercises Appendix: Critical Value Tables Appendix: Tables of Critical Values Table A.1 Critical Values of r in the Sign Test Table A.2 Critical Values of s in the Wilcoxon Matched-Pairs Signed-Rank Test Table A.3a Critical Values of u in the Runs Test for small N = n + m Table A.3b Critical Values of the u in the Runs Test for Large N Table A.4 Critical Values of d in the Kolmogorov–Smirnov Goodness-of-Fit Test Index
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