Table of contents for Applied statistics for engineers and physical scientists / Johannes Ledolter, Robert V. Hogg.

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CONTENTS
Preface xiii 1 COLLECTION AND ANALYSIS OF INFORMATION 1 
1.1	Introduction 1 1.1-1 Data Collection 1 1.1-2 Types of Data 3 1.1-3 The Study of Variability 4 1.1-4 Distributions 7 1.1-5 Importance of Variability (or Lack Thereof) for Quality and Productivity Improvement 9 Exercises 11 
1.2	Measurements Collected over Time 12 1.2-1 Time-Sequence Plots 12 1.2-1 Control Charts:A Special Case of Time-Sequence Plots 15 Exercises 17 
1.3	Data Display and Summary 18 1.3-1 Summary and Display of Measurement Data 18 1.3-2 Measures of Location 22 1.3-3 Measures of Variation 26 1.3-4 Exploratory Data Analysis: Stem-and-Leaf Displays and Box-and-Whisker Plots 28 1.3-5 Analysis of Categorical Data 33 Exercises 37 
1.4	Comparisons of Samples: The Importance of Strati_cation 44 1.4-1 Comparing Two Types of Wires 45 1.4-2 Comparing Lead Concentrations from Two Different Years 45 1.4-3 Number of Flaws for Three Different Products 49 1.4-4 Effects of Wind Direction on the Water Levels of Lake Neusiedl 49 Exercises 52 
1.5	Graphical Techniques, Correlation, and an Introduction to Least Squares 54 1.5-1 The Challenger Disaster 54 1.5-2 The Sample Correlation Coef_cient as a Measure of Association in a Scatter Plot 56 1.5-3 Introduction to Least Squares 60 Exercises 62 
v 
1.6	The Importance of Experimentation 66 1.6-1 Design of Experiments 67 1.6-2 Design of Experiments with Several Factors and the Determination of Optimum Conditions 71 Exercises 73 
1.7	Available Statistical Computer Software and the Visualization of Data 74 1.7-1 Computer Software 74 1.7-2 The Visualization of Data 76 Exercises 77 
Chapter 1 Additional Remarks 78
Projects 79
2 PROBABILITY MODELS AND DISCRETE DISTRIBUTIONS 89 
2.1	Probability 89 2.1-1 The Laws of Probability 91 Exercises 97 
2.2	Conditional Probability and Independence 98 2.2-1 Conditional Probability 98 2.2-2 Independence 100 2.2-3 Bayes' Theorem 102 Exercises 104 
2.3	Random Variables and Expectations 108 2.3-1 Random Variables and Their Distributions 108 2.3-2 Expectations of Random Variables 111 Exercises 114 
2.4	The Binomial and Related Distributions 116 2.4-1 Bernoulli Trials 116 2.4-2 The Binomial Distribution 117 2.4-3 The Negative Binomial Distribution 121 2.4-4 The Hypergeometric Distribution 121 Exercises 122 
2.5	Poisson Distribution and Poisson Process 125 2.5-1 The Poisson Distribution 125 2.5-2 The Poisson Process 127 Exercises 129 
2.6	Multivariate Distributions 131 2.6-1 Joint, Marginal, and Conditional Distributions 131 2.6-2 Independence and Dependence of Random Variables 135 2.6-3 Expectations of Functions of Several Random Variables 136 2.6-4 Means and Variances of Linear Combinations of Random Variables 140 Exercises 141 
2.7	The Estimation of Parameters from Random Samples 144
2.7-1 Maximum Likelihood Estimation 144
2.7-2 Examples 145
2.7-3 Properties of Estimators 147
Exercises 149
Chapter 2 Additional Remarks 150 
Projects 152 
3	CONTINUOUS PROBABILITY MODELS 155 
3.1	Continuous Random Variables 155 3.1-1 Empirical Distributions 155 3.1-2 Distributions of Continuous Random Variables 158 Exercises 163 
3.2	The Normal Distribution 165 Exercises 170 
3.3	Other Useful Distributions 172 3.3-1 Weibull Distribution 173 3.3-2 Gompertz Distribution 174 3.3-3 Extreme Value Distribution 176 3.3-4 Gamma Distribution 176 3.3-5 Chi-Square Distribution 177 3.3-6 Lognormal Distribution 177 Exercises 181 
3.4	Simulation: Generating Random Variables 183 3.4-1 Motivation 183 3.4-2 Generating Discrete Random Variables 184 3.4-3 Generating Continuous Random Variables 186 Exercises 187 
3.5	Distributions of Two or More Continuous Random Variables 189 
3.5-1 Joint, Marginal, and Conditional Distributions, and Mathematical Expectations 189 
3.5-2 Propagation of Errors 190
Exercises 193
3.6	Fitting and Checking Models 196 3.6-1 Estimation of Parameters 196 3.6-2 Checking for Normality 198 3.6-3 Checking Other Models through Quantile-Quantile Plots 202 Exercises 206 
3.7	Introduction to Reliability 210
Exercises 213
Chapter 3 Additional Remarks 214
Projects 216
4 STATISTICAL INFERENCE:SAMPLING DISTRIBUTIONS, CONFIDENCE INTERVALS, AND TESTS OF HYPOTHESES 219 
4.1	Sampling Distributions 219 4.1-1 Introduction and Motivation 219 4.1-2 Distribution of the Sample Mean X 221 4.1-3 The Central Limit Theorem 223 4.1-4 Normal Approximation of the Binomial Distribution 225 Exercises 227 
4.2	Con_dence Intervals for Means 228 4.2-2 Determination of the Sample Size 231 4.2-3 Con_dence Intervals for m1 -m2 232 Exercises 233 
4.3	Inferences from Small Samples and with Unknown Variances 234 4.3-2 Tolerance Limits 238 4.3-3 Con_dence Intervals for m1 -m2 241 Exercises 242 
4.4	Other Con_dence Intervals 244 4.4-1 Con_dence Intervals for Variances 244 4.4-2 Con_dence Intervals for Proportions 247 Exercises 249 
4.5	Tests of Characteristics of a Single Distribution 250 4.5-1 Introduction 250 4.5-2 Possible Errors and Operating Characteristic Curves 252 4.5-3 Tests of Hypotheses when the Sample Size Can Be Selected 254 4.5-2 Tests of Hypotheses when the Sample Size Is Fixed 256 Exercises 262 
4.6	Tests of Characteristics of Two Distributions 264
4.6-1 Comparing Two Independent Samples 265
4.6-2 Paired-Samples t-Test 267
4.6-3 Test of p1 = p2 270
4.6-4 Test of s 21 = s 22 271
Exercises 272
4.7	Certain Chi-Square Tests 274 
4.7-1 Testing Hypotheses about Parameters in a Multinomial Distribution 275 
4.7-2 Contingency Tables and Tests of Independence 278 
4.7-3 Goodness-of-Fit Tests 280 
Exercises 281 
Chapter 4 Additional Remarks 283 
Projects 285 
5	STATISTICAL PROCESS CONTROL 295 
5.1	Shewhart Control Charts 295 5.1-1 x-Charts and R-charts 296 5.1-2 p-Charts and c-Charts 300 5.1-3 Other Control Charts 304 Exercises 304 
5.2	Process Capability Indices 307 5.2-1 Introduction 307 5.2-2 Process Capability Indices 310 5.2-3 Discussion of Process Capability Indices 315 Exercises 317 
5.3	Acceptance Sampling 318 Exercises 324 
5.4	Problem Solving 325 5.4-1 Introduction 325 5.4-2 Pareto Diagram 326 5.4-3 Diagnosis of Causes of Defects 328 5.4-4 Six Sigma Initiatives 328 Exercises 332 
Chapter 5 Additional Remarks 333 
Projects 335 
6	EXPERIMENTS WITH ONE FACTOR 338 
6.1	Completely Randomized One-Factor Experiments 339
6.1-1 Analysis-of-Variance Table 343
6.1-2 F-Test for Treatment Effects 345
6.1-3 Graphical Comparison of k Samples 347
Exercises 347
6.2	Other Inferences in One-Factor Experiments 351 6.2-1 Reference Distribution for Treatment Averages 351 6.2-2 Con_dence Intervals for a Particular Difference 352 6.2-3 Tukey's Multiple-Comparison Procedure 353 6.2-4 Model Checking 354 6.2-5 The Random-Effects Model 354 6.2-6 Computer Software 358 Exercises 359 
6.3	Randomized Complete Block Designs 365 6.3-1 Estimation of Parameters and ANOVA 367 6.3-2 Expected Mean Squares and Tests of Hypotheses 369 6.3-3 Increased Ef_ciency by Blocking 371 6.3-4 Follow-Up Tests 371 6.3-5 Diagnostic Checking 372 6.3-6 Computer Software 374 Exercises 374 
6.4	Designs with Two Blocking Variables: Latin Squares 377 6.4-1 Construction and Randomization of Latin Squares 378 6.4-2 Analysis of Data from a Latin Square 380 Exercises 381 
7	EXPERIMENTS WITH TWO OR MORE FACTORS 383 
7.1 Two-Factor Factorial Designs 383 7.1-1 Graphics in the Analysis of Two-Factor Experiments 392 7.1-2 Special Case: n = 1 393 7.1-3 Random Effects 393 7.1-4 Computer Software 394 Exercises 394 
7.2	Nested Factors and Hierarchical Designs 397 Exercises 402 
7.3	General Factorial and 2k Factorial Experiments 406 7.3-1 2k Factorial Experiment 407 7.3-2 Signi_cance of Estimated Effects 414 Exercises 416 
7.4	2k-p Fractional Factorial Experiments 422 7.4-1 Half Fractions of 2k Factorial Experiments 423 7.4-2 Higher Fractions of 2k Factorial Experiments 426 7.4-3 Computer Software 429 Exercises 431 
Chapters 6 and 7 Additional Remarks 435
Chapters 6 and 7 Projects 436
8	REGRESSION ANALYSIS 439 
8.1	The Simple Linear Regression Model 440 8.1-1 Estimation of Parameters 442 8.1-2 Residuals and Fitted Values 445 8.1-3 Sampling Distribution of bN 0 and bN 1 446 Exercises 447 
8.2	Inferences in the Regression Model 450 8.2-1 Coef_cient of Determination 451 8.2-2 Analysis-of-Variance Table and F-Test 452 8.2-3 Con_dence Intervals and Tests of Hypotheses for Regression Coef_cients 454 Exercises 457 
8.3	The Adequacy of the Fitted Model 459 8.3-1 Residual Checks 459 8.3-2 Output from Computer Programs 465 8.3-3 The Importance of Scatter Plots in Regression 470 Exercises 472 
8.4	The Multiple Linear Regression Model 479 8.4-1 Estimation of the Regression Coef_cients 480 8.4-2 Residuals, Fitted Values, and the Sum-of-Squares Decomposition 482 8.4-3 Inference in the Multiple Linear Regression Model 483 8.4-4 A Further Example: Formaldehyde Concentrations 484 Exercises 488 
8.5	More on Multiple Regression 494 8.5-1 Multicollinearity among the Explanatory Variables 494 8.5-2 Another Example of Multiple Regression 500 8.5-3 A Note on Computer Software 506 8.5-4 Nonlinear Regression 507 Exercises 507 
8.6	Response Surface Methods 517 8.6-1 The "Change One Variable at a Time"Approach 518 8.6-2 Method of Steepest Ascent 519 8.6-3 Designs for Fitting Second-Order Models:The 3k Factorial and the Central Composite Design 521 8.6-4 Interpretation of the Second-Order Model 523 8.6-5 An Illustration 525 Exercises 528 
Chapter 8 Additional Remarks 531 Projects 532 
Appendix A: References 542 
Appendix B: Answers to Selected Exercises 548 
Appendix C: Statistical Tables 560 
Appendix D: List of Files (Minitab Files and ASCII Listing of the Numbers) 000 
Index 000 

Library of Congress Subject Headings for this publication:

Engineering -- Statistical methods.