probability and statistics_for_engineers_and_scientists_8th_edition_by_ronald_e._walpole

1. Probability & Statistics for Engineers &; Scientists

2. Probability & Statistics for Engineers & Scientists E I G H T H E D I T I O N Ronald E. Walpole Roanoke College Raymond H. Myers Virginia Polytechnic Institute and State University Sharon L. Myers Radford University Keying Ye University of Texas at San Antonio Pearson Education International PEARSON Prentice Hall

3. If you purchased this book within the United States or Canada you should be aware that it has been wrongfully imported without the approval of the Publisher or Author. Editor in Chief: Sally Yagun Production Editor: Lynn Savino Wendel Senior Managing Editor: Linda Mihatov Belmms Assistant Managing Editor: Bayani Mendoza de Leon Executive Managing Editor: Kathleen Schiaparelli Manufacturing Buyer: Maura Zaldivar Manufacturing Manager: Alexis Heydt-Long Marketing Manager: Halee Dinsey Marketing Assistant: Jennifer de Leeuwcrk Director of Marketing: Patrice Jones Editorial Assistant/Print Supplements Editor: Jennifer Urban Art Editor: Thomas Benfatti Art Director: Heather Scott Creative Director: Juan R. Lopez Director of Creative Services: Paul Belfanti Cover Photo: Corbis Royalty Free Art Studio: Laser-words PEARSON Prentice Hall 2007, 2002, 1998. 1993, 1989, 1985, 1978, 1972 Pearson Education, Inc. Pearson Prentice Hall Pearson Education, Inc. Upper Saddle River, N.I 07458 All rights reserved. No part, of tins book may be reproduced, in any form or by any means, without permission in writing from the publisher. Pearson Prentice Hall is a trademark of Pearson Education, Inc. 1 0 9 8 7 6 5 4 3 ISBN 0 - 1 3 - 2 0 4 7 6 7 - 5 Pearson Education LTD., London Pearson Education Australia PTY. Limited, Sydney Pearson Education Singapore, Pte. Ltd. Pearson Education North Asia Ltd., Hong Kong Pearson Education Canada, Ltd.. Toronto Pearson Education de Mexico, S.A. de C.V. Pearson Education-Japan, Tokyo Pearson Education Malaysia, Pte. Ltd. Pearson Education, Upper Saddle River, New Jersey

4. This book is dedicated to Billy and Julie R.H.M. and S.L.M. Limin K.Y.

5. Contents Preface xv 1 Introduction to Statistics and Data Analysis 1 1.1 Overview: Statistical Inference, Samples, Populations and Exper- imental Design 1 1.2 The Hole of Probability 4 1.3 Sampling Procedures; Collection of Data 7 1.4 Measures of Location: The Sample Mean and Median 11 Exercises 13 1.5 Measures of Variability 1 ! Exercises 17 1.0 Discrete and Continuous Data 17 1.7 Statistical Modeling, Scientific Inspection, and Graphical Diag- nostics ]!) J .8 Graphical Methods and Data Description 20 1.9 General Types of Statistical Studies: Designed Experiment, Observational Study, and Retrospective Study 25 Exercises 28 2 Probability 31 2.1 Sample Space 31 2.2 Events 34 Exercises 38 '2.3 Counting Sample Points 40 Exercises 47 2.4 Probability of an Event 48 2.5 Additive Rules 52 Exercises 55 2.6 Conditional Probability 58 2.7 Multiplicative Rules 61 Exercises 65

6. Vlll Contents 2.8 Bayes' Rule 68 Exercises 72 Review Exercises 73 3 Random Variables and Probability Distributions 77 3.1 Concept of a Random Variable 77 3.2 Discrete Probability Distributions 80 3.3 Continuous Probability Distributions 84 Exercises 88 3.4 Joint Probability Distributions 91 Exercises 101 Review Exercises 103 3.5 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters 106 4 Mathematical Expectation 107 4.1 Mean of a Random Variable 107 Exercises 113 4.2 Variance and Covariance of Random Variables 115 Exercises 122 4.3 Means and Variances of Linear Combinations of Random Variables 123 4.4 Chebyshov's Theorem 131 Exercises 134 Review Exercises 136 4.5 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters 138 5 Some Discrete Probability Distributions 141 5.1 Introduction and Motivation 141 5.2 Discrete Uniform Distribution 141 5.3 Binomial and Multinomial Distributions 143 Exercises 150 5.4 Hypergeometric Distribution 152 Exercises 157 5.5 Negative Binomial and Geometric Distributions 158 5.6 Poisson Distribution and the Poisson Process 161 Exercises 165 Review Exercises 167 5.7 Potential Misconceptions and Hazards: Relationship to Material in Other Chapters 169

7. Contents IX 6 Some Continuous Probability Distributions 171 6.1 Continuous Uniform Distribution 171 6.2 Normal Distribution 172 6.3 Areas under the Normal Curve 176 6.4 Applications of the Normal Distribution 182 Exercises 185 6.5 Normal Approximation to the Binomial 187 Exercises 19;} 6.6 Gamma and Exponential Distributions 194 6.7 Applications of the Exponential and Gamma Distributions 197 6.8 Chi-Squared Distribution 200 6.9 Lognormal Distribution 201 6.10 VVeibull Distribution (Optional) 202 Exercises 205 Review Exercises 206 6.1 1 Potential Misconceptions and Hazards: Relationship to Material in Other Chapters 209 7 Functions of Random Variables (Optional).. 211 7.1 Introduction 211 7.2 Transformations of Variables 211 7.3 Moments and Moment-Generating Functions 219 Exercises 226 8 Fundamental Sampling Distributions and Data Descriptions 229 8.1 Random Sampling 229 8.2 Some Important Statistics 231 Exercises 23 1 8.3 Data Displays and Graphical Methods 236 8.4 Sampling Distributions 243 8.5 Sampling Distribution of Means 244 Exercises 251 8.6 Sampling Distribution of S 251 8.7 ^-Distribution 257 8.8 F-Distribution 261 Exercises 265 Review Exercises 266 8.9 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters 268

8. Contents 9 One- and Two-Sample Estimation Problems 269 9.1 Introduction 269 9.2 Statistical Inference 269 9.3 Classical Methods of Estimation 270 9.4 Single Sample: Estimating the Mean 274 9.5 Standard Error of a Point Estimate 280 9.6 Prediction Intervals 281 9.7 Tolerance Limits 283 Exercises 285 9.8 Two Samples: Estimating the Difference between Two Means ... 288 9.9 Paired Observations 294 Exercises 297 9.10 Single Sample: Estimating a Proportion 299 9.11 Two Samples: Estimating the Difference between Two Proportions 302 Exercises 304 9.12 Single Sample: Estimating the Variance 306 9.13 Two Samples: Estimating the Ratio of Two Variances 308 Exercises 310 9.14 Maximum Likelihood Estimation (Optional) 310 Exercises 315 Review Exercises 315 9.15 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters 319 10 One- and Two-Sample Tests of Hypotheses 321 10.1 Statistical Hypotheses: General Concepts 321 10.2 Testing a Statistical Hypothesis 323 10.3 One- and Two-Tailed Tests 332 10.4 The Use of P-Values for Decision Making in Testing Hypotheses. 334 Exercises 336 10.5 Single Sample: Tests Concerning a Single Mean (Variance Known) 338 10.6 Relationship to Confidence Interval Estimation 341 10.7 Single Sample: Tests on a Single Mean (Variance Unknown) 342 10.8 Two Samples: Tests on Two Means 345 10.9 Choice of Sample Size for Testing Means 350 10.10 Graphical Methods for Comparing Means 355 Exercises 357 10.11 One Sample: Test on a Single Proportion 361 10.12 Two Samples: Tests on Two Proportions 364 Exercises 366 10.13 One- and Two-Sample Tests Concerning Variances 367

9. Contents xi Exercises 370 10.14 Goodness-of-Fit Test 371 10.15 Test for Independence (Categorical Data) 374 10.16 Test for Homogeneity 377 10.17 Testing for Several Proportions 378 10.18 Two-Sample Case Study 380 Exercises 383 Review Exercises 385 10.19 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters 387 11 Simple Linear Regression and Correlation.. 389 11.1 Introduction to Linear Regression 389 11.2 The Simple Linear Regression Model 390 11.3 Least Squares and the Fitted Model 394 Exercises 397 11.4 Properties of the Least Squares Estimators 400 11.5 Inferences Concerning the Regression Coefficients 402 11.6 Prediction 409 Exercises 412 11.7 Choice of a Regression Model 414 11.8 Analysis-of-Variance Approach 415 11.9 Test for Linearity of Regression: Data with Repeated Observations 417 Exercises 423 11.10 Data Plots and Transformations 425 11.11 Simple Linear Regression Case Study 430 11.12 Correlation 432 Exercises 438 Review Exercises 438 11.13 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters 443 12 Multiple Linear Regression and Certain Nonlinear Regression Models 445 12.1 Introduction 445 12.2 Estimating the Coefficients 446 12.3 Linear Regression Model Using Matrices (Optional) 449 Exercises 452 12.4 Properties of the Least Squares Estimators 456 12.5 Inferences in Multiple Linear Regression 458 Exercises 464

10. xii Contents 12.6 Choice of a Fitted Model through Hypothesis Testing 465 12.7 Special Case of Orthogonality (Optional) 469 Exercises 473 12.8 Categorical or Indicator Variables 474 Exercises 478 12.9 Sequential Methods for Model Selection 479 12.10 Study of Residuals and Violation of Assumptions 485 12.11 Cross Validation, C;), and Other Criteria for Model Selection 490 Exercises 496 12.12 Special Nonlinear Models for Nonideal Conditions 499 Review Exercises 503 12.13 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters 508 13 One-Factor Experiments: General 511 13.1 Analysis-of-Variance Technique 511 13.2 The Strategy of Experimental Design 512 13.3 One-Way Analysis of Variance: Completely Randomized Design (One-Way ANOVA) 513 13.4 Tests for the Equality of Several Variances 518 Exercises 521 13.5 Single-Dcgree-of-Freedom Comparisons 523 13.6 Multiple Comparisons 527 13.7 Comparing Treatments with a Control 531 Exercises 533 13.8 Comparing a Set of Treatments in Blocks 535 13.9 Randomized Complete Block Designs 537 13.10 Graphical Methods and Model Checking 544 13.11 Data Transformations In Analysis of Variance) 547 13.12 Latin Squares (Optional) 549 Exercises 551 13.13 Random Effects Models 555 13.14 Power of Analysis-of-Variance Tests 559 13.15 Case Study 563 Exercises 565 Review Exercises 567 13.16 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters 571

11. Contents xiii 14 Factorial Experiments (Two or More Factors) 573 14.1 Introduction 573 14.2 Interaction in the Two-Factor Experiment 574 14.3 Two-Factor Analysis of Variance 577 Exercises 587 14.4 Three-Factor Experiments 590 Exercises 597 14.5 Model II and III Factorial Experiments 600 14.6 Choice of Sample Size 603 Exercises 605 Review Exercises 607 14.7 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters 609 15 2k Factorial Experiments and Fractions 611 15.1 Introduction 611 15.2 The 2fc Factorial: Calculation of Effects and Analysis of Variance 612 15.3 Nonreplicated 2k Factorial Experiment 618 15.4 Injection Molding Case Study 619 Exercises 622 15.5 Factorial Experiments in a Regression Setting 625 15.6 The Orthogonal Design 631 15.7 Factorial Experiments in Incomplete Blocks 639 Exercises 645 15.8 Fractional Factorial Experiments 647 15.9 Analysis of Fractional Factorial Experiments 653 Exercises 656 15.10 Higher Fractions and Screening Designs 657 15.11 Construction of Resolution III and IV Designs 658 15.12 Other Two-Level Resolution III Designs; The Plackett-Burman Designs 660 15.13 Robust Parameter Design 661 Exercises 666 Review Exercises 667 15.14 Potential Misconceptions and Hazards: Relationship to Material in Other Chapters 669 16 Nonparametric Statistics 671 16.1 Nonparametric Tests 671 16.2 Signed-Rank Test 676

12. xiv Contents Exercises 679 16.3 Wilcoxon Rank-Sum Test 681 16.4 Kruskal-Wallis Test 684 Exercises 686 1G.5 Runs Test. 687 16.6 Tolerance Limits 690 16.7 Rank Correlation Coefficient 690 Exercises 693 Review Exercises 695 17 Statistical Quality Control 697 17.1 Introduction 697 17.2 Nature of the Control Limits 699 17.3 Purposes of the Control Chart 699 17.4 Control Charts for Variables 700 17.5 Control Charts for Attributes 713 17.6 Cusum Control Charts 721 Review Exercises 722 18 Bayesian Statistics (Optional) 725 18.1 Bayesian Concepts 725 18.2 Bayesian Inferences 726 18.3 Bayes Estimates Using Decision Theory Framework 732 Exorcises 734 Bibliography 737 A Statistical Tables and Proofs 741 B Answers to Odd-Numbered Non-Review Exercises 795 Index 811

13. Preface General Approach and Mathematical Level The general goals for the eighth edition remain the same as those in recent editions. We feel as if it is important to retain a balance between theory and applications. Engineers and physical scientists as well as computer scientists are trained in calculus and thus mathematical support is given when we feel as if the pedagogy- is enhanced by it. This approach prohibits the material from becoming a collection of tools with no mathematical roots. Certainly students with a mathematical background of calculus, and, in a few cases, linear algebra, have the capability to understand the concepts more thoroughly and use the resulting tools more intel- ligently. Otherwise there is a clear danger that, the student will only be able to apply the material within very narrow bounds. The new edition contains a substantially larger number of exercises. These exercises challenge the student to be able to use concepts from the text to solve problems dealing with many real-life scientific and engineering situations. The data sets involved in the exercises are available for download from website at http://www.prenhaU.com.. The increase in the quantity of exercises results in a much broader spectrum of areas of applications, including biomedical, biocngi- neering, business problems, computer issues, and many others. Even the chapters that deal in introductory probability theory contain examples and exercises that carry a broad range of applications that students of science and engineering will easily recognize as important. As in past editions, the use of calculus is confined to elementary probability theory and probability distributions. These topics are discussed in Chapters 2. 3, 1, 6, and 7. Chapter 7 is an optional chapter that includes transformations of variables and moment generating functions. Matrix algebra is used only a, modest amount in linear regression material in Chapters 11 and 12. For those who desire the use of more substantial support with matrices, an optional section in Chapter 12 is available. The instructor who wishes to minimize the use of matrices may bypass this section with no loss of continuity. Students using this text should have completed the equivalent of one semester of differential and integral calculus. An exposure to matrix algebra would be helpful but not necessary if the course context excludes I he aforementioned optional section given in Chapter 12.

14. xvi Preface Content and Course Planning This text is designed for either a one- or two-semester course. A reasonable cur- riculum for a one-semester course might include Chapters 1 through 10. Many instructors desire for students to be exposed in some degree to simple linear regression in a one semester course. Thus one may choose to include a portion of Chapter 11. On the other hand, some instructors wish to teach a portion of analysis of variance, in which case Chapters 11 and 12 may be excluded in favor of some portion of Chapter 13, which features one factor analysis of variance. In order to provide sufficient time for one or perhaps even both of these topics, the instructor may wish to eliminate Chapter 7 and/or certain specialized topics in Chapters 5 and 6 (for example, treatment on the gamma, lognormal, and Weibull distributions, or material on the negative binomial and geometric distributions). Indeed, some instructors find that in a one-semester course in which regression analysis and analysis of variance are of primary interest, there may be topics in Chapter 9 on estimation that may be removed (e.g., maximum likelihood, prediction intervals, and/or tolerance limits). We feel as if the flexibility exists that allows a one-semester course given any priorities set down by the instructor. Chapter 1 is an elementary overview of statistical inference designed for the beginner. It contains material on sampling and data analysis and contains many- examples and exercises for motivation. Indeed, some very rudimentary aspects of experimental design are included along with an appreciation of graphic techniques and certain vital characteristics of data collection. Chapters 2, 3, and 4 deal with basic probability as well as discrete and continuous random variables. Chapters 5 and 6 cover specific discrete and continuous distributions with illustrations of their use and relationships among them. In addition, a substantial number of examples and exercises are given that illustrate their use. Chapter 7 is an optional chapter that treats transformation of random variables. An instructor may wish to cover this material only if he or she is teaching a more theoretical course. This chapter is clearly the most mathematical chapter in the text. Chapter 8 contains additional material on graphical methods as well as a very important introduction to the notion of a sampling distribution. Probability plotting is discussed. The material on sampling distribution is reinforced by a thorough discussion of the central limit theorem as well as the distribution of a sample variance under normal i.i.d. (independently and identically distributed) sampling. The t and F distributions are introduced along with motivation regarding their use in chapters that follow. Chapters 9 and 10 contain material on one and two sample point and interval estimation and hypothesis testing. Material on confidence intervals, prediction intervals, tolerance intervals, and maximum likelihood estimation in Chapter 9 offeis the instructor considerable flexibility regarding what might be excluded in a one- semester course. A section on Bayes estimation that was available in the seventh edition in Chapter 9 has been removed. More attention will be given to this topic in the "New to This Edition" section that follows. Chapters 11 through 17 contain ample material for a second semester. Simple and multiple linear regression are contained in Chapters 8 and 12, respectively. Chapter 12 also contains material on logistic regression, which finds applications in many areas of engineering and the biological sciences. The material covered in multiple linear regression is quite extensive and thus provides flexibility for the

15. Preface xvii instructor. Among the "special topics" to which an instructor has access are the special case of orthogonal rcgressors, categorical or indicator variables, sequential methods for model selection, study of residuals and violation of assumptions, cross validation and the use of PRESS and C, and, of course, logistic regression. Chap- ters 13 through 17 contain topics in analysis of variance, design of experiments, nonparametric statistics, and quality control. Chapter 15 treats two-level factorials (with and without blocking) and fractional factorials, and again flexibility is abun- dant because of the many "special topics" offered in this chapter. Topics beyond the standard 2k and fractional 2* designs include blocking and partial confound- ing, special higher fractions and screening designs, Plackett-Burman designs, and robust parameter design. All chapters contain a large number of exercises, considerably more than what was offered in the seventh edition. More information on exercises will be given in the "New To This Edition" section. Case Studies and Computer Software The topical material in two-sample hypothesis testing, multiple linear regression, analysis of variance, and the use of two-level factorial experiments is supplemented by case studies that feature computer printout and graphical material. Both SAS and MINFTAB are featured. The use of the computer printout underscores our feeling that the students should have the experience of reading and interpreting computer printout and graphics, even if that which is featured in the text is not what is used by the instructor. Exposure to more than one type of software can broaden the experience base for the student. There is no reason to believe that the software in the course will be that which he or she will be called upon to use in practice following graduation. Many examples and case studies in the text are supplemented, where appropriate, by various types of residual plots, quantile plots, normal probability plots, and others. This is particularly prevalent in the material used in Chapters 11 through 15. New to This Edition General 1. There arc 15 20% new problem sets incorporated, with many new applications demonstrated in engineering as well as biological, physical, and computer science. 2. There is new and end-ofL chapter review material where appropriate. This material emphasizes key ideas as well as risks and hazards that the user of material covered in the chapter must be aware of. This material will also provide demonstration of how it is influenced by material in other chapters. 3. A new mini (and optional) chapter on Bayesian statistics has been incorporated. The chapter will be a practical offering with applications emphasized in many fields. 4. There are extensive additional changes made throughout, based on need per- ceived by authors and reviewers. The following outlines some specifics.

16. xviii Preface Chapter 1: Introduction to Statistics and Data Analysis Chapter 1 contains a substantial amount of new material. There is new exposition on the difference between discrete and continuous measurements. Many illustrations are given with particular real life applications of discrete measurements (e.g., numbers of radioactive particles, the number of personnel responsible for a particular port facility, and the number of oil tankers arriving each day at a port city). Special attention is given to situations associated with binary data. Examples are given in the biomedical field as well as quality control. New concepts (for this text) are discussed in Chapter 1 which deal with properties of a distribution or sample other than those that characterize central tendency and variability. Quartiles and, more generally, quantiles are defined and discussed. The importance of experimental design and the advantages that it offers is expanded beyond that of the seventh addition. In this development important notions that are treated include randomization, reduction of process variability, and interaction among factors. The readers are exposed in the first chapter to different types of statistical studies: the designed experiment, the observational study, and the retrospective study. Examples are given of each type and advantages and disadvantages are discussed. The chapter continues to emphasize graphical procedures and where they apply. Nineteen new exercises were added to Chapter 1. Some make use of data from studies conducted at the Virginia Tech consulting center and some are taken from engineering journals and others involve historical data. This chapter now contains 30 exercises. Chapter 2: Probability There are new examples and new exposition to better demonstrate the notion of conditional probability. Chapter 2 offers 136 total exercises. All new exercises involve direct applications in science and engineering. Chapter 3: Random Variables and Probability Distributions There is new exposition on the notion of "dummy" variables that, play an important role in the Bernoulli and binomial distributions. There are many more exercises with new applications. The new review at the end of the chapter introduces the connection between material in Chapter 3 with the concept of distribution parameters and specific probability distributions discussed in future chapters. Topics for new exercises include particle size distribution for missile fuel, measurement errors in scientific systems, studies of time to failure for manufactured washing machines, the production of electron tubes on an assembly line, arrival time problems at certain big city intersections, shelf life of a product, passenger congestion problems in airports, problems with impurities in batches of chemical product, failure in systems of electronic components working in parallel, and many others. There are now 82 exercises in this chapter.

17. Preface x j x Chapter 4: Mathematical Expectation Several more exercises were added to Chapter 4. Rules for expectations and variances of linear functions were expanded to cover approximations for nonlinear functions. Examples are given to illustrate the use of these rules. The review at the end of Chapter 4 reveals possible difficulties and hazards with practical applications of the material since most examples and exercises assume parameters (mean and variance) are known and in true applications these parameters would be estimated. Reference is made to Chapter 9, where estimation is discussed. There are now 103 exercises in this chapter. Chapter 5: Some Discrete Probability Distributions New exercises representing various applications of the Poisson distribution have been added. Additional exposition has been added that deals with the Poisson probability function. New exercises include real life applications of the Poisson, binomial, and hypergeometric distributions. Topics for new exercises include flaws in manufactured copper wire, highway potholes in need of repair, patient traffic in an urban hos- pital, airport luggage screening, homeland security detection of incoming missiles, and many others. In addition, plots are given that provide the reader with a clear indication about the nature of both the Poisson and the binomial distribution as parameters change. There are now 105 exercises in this chapter. Chapter 6: Some Continuous Probability Distributions Many more examples and exercises dealing in both the exponential and the gamma distribution have been added. The "lack of memory" property of the exponential distribution is now discussed at length and related to the bond between the exponential and Poisson distributions. The section on the Weibull distribution is greatly improved and expanded. The extensions presented focus on the measuring and interpretation of the failure rate or "hazard rate" and how knowledge of the parameters of the Weibull allow the user to learn how machines wear or even get stronger over time. More exercises are given that involve the Weibull and lognormal distributions. Caution is expressed in the review much like that in Chapter 5. In practical situations, guesses or estimates of process parameters of the gamma distribution in, say, failure rate problems or parameters of either a gamma or Weibull distribution, may be unstable, thereby introducing errors in calculations. There are now 84 exercises in this chapter. Chapter 7: Functions of Random Variables (optional) No major changes are included in this optional chapter. Chapter 8: Fundamental Distributions and Data Description There is additional exposition on the central limit theorem as well as the general concept of sampling distributions. There are many new exercises. The summary

18. xx Preface provides important information on t. 2 -. and F. including how they are used and what assumptions are involved. More attention is given in Chapter 8 to normal probability plotting. In addition, the central limit theorem is discussed in more detail in order that the reader can gain more insight about what size n must be before normality can be invoked. Plots are given to illustrate this. Additional exposition is given regarding the normal approximation to the binomial distribution and how it works in practical situations. The presentation presents an intuitive argument that connects the normal approximation of the binomial to the central limit theorem. The number of exercises in this chapter is now 75. Chapter 9: One- and Two-Sample Estimation Problems Many new applications are revealed in new exercises in this chapter. The summary gives rationale and hazards associated with the so-called large sample confidence interval. The importance of the assumption of normality and the conditions under which it is assumed are discussed. Early in the chapter the development of confidence intervals offers a pragmatic discussion about why one must begin with the "known er' case. It is suggested that these kinds of situations do not actually occur in practice but consideration of the known s case initially provides a structure that allows the more useful "unknown CT" to be understood more easily by students. One-sided bounds of all types are now presented and discussion is given as to when they are used as opposed to the two-sided counterparts. New examples are given which require the use of the one-sided intervals. These include confidence intervals, prediction intervals, and tolerance intervals. The concept of a mean squared error of an estimator is discussed. Thus the notion of bias and variance can be brought together in the general comparison of estimators. Twenty-seven new exercises are included in Chapter 9. There are now 111 exercises in this chapter. Chapter 10: One- and Two-Sided Tests of Hypotheses We have an entirely restructured exposition on the introduction to hypothesis testing. It is designed to help the student have a clear picture of what is being accomplished and not being accomplished in hypothesis testing. The notion that we rarely, if ever, "accept the null hypothesis'' is discussed with illustrations. There is also a thorough discussion with examples, of how one should structure or set up the null and alternative hypotheses. The notion that rejection implies "sample evidence refutes HQ" and that HQ is actually the logical complement to Hi is discussed precisely with several examples. Much is said about the concept of "fail to reject HQ'1 and what it means in practical situations. The summary produces "misconceptions and hazards" which reveals problems in drawing the wrong conclusions when the analyst "fails to reject" the null hypothesis. In addition, "robustness" is discussed, which deals with the nature of the sensitivity of various tests of hypotheses to the assumption of normality. There are now 115 exercises in this chapter.

19. Preface xxi Chapter 11: Simple Linear Regression Many new exercises are added in simple linear regression. Special exposition is given to the pitfalls in the use of R2 , the coefficient of determination. Much additional emphasis is given to graphics and diagnostics dealing in regression. The summary deals with hazards that one may encounter if diagnostics are not used. It is emphasized that diagnostics provide "checks" on the validity of assumptions. These diagnostics include data plots, plots of student.ized residuals, and normal probability plots of residuals. An important presentation is made early in the chapter about the nature of linear models in science and engineering. It is pointed out that these are often empirical models that are simplifications of more complicated and unknown struc- tures. More emphasis is given in this chapter on data plotting. "Regression through the origin'' is discussed in an exercise. More discussion is given on what it means when H0: /? = 0 is rejected or not rejected. Plots are used for illustration. There are now 68 exercises in this chapter. Chapter 12: Multiple Linear Regression Additional treatment is given in this chapter on the pitfalls of R2 . The discussion centers around the need to compromise between the attempt to achieve a "good fit" to the data and the inevitable loss in error degrees of freedom that is experienced when one "overfits." In that regard the "adjusted R2 "' is defined and discussed with examples. In addition, the CV (coefficient of variation) is discussed and interpreted as a measure that can be used to compare competing models. Several new exercises are present to provide the reader experience in comparing competing models using real data sets. Additional treatment is given to the topic of "categorical regressors" with graphical tools used to support the underlying concepts. Additional exercises are given to illustrate practical uses of logistic regression in both industrial and biomedical research areas. There are now 72 exercises in this chapter. Chapter 13: One-Factor Experiments: General The discussion of Tukey's test on multiple comparisons is expanded considerably. More is presented on the notion of error rate and o>values in the context of simul- taneous confidence intervals. A new and important section is given on "Data Transformation in Analysis of Variance." A contrast is made with the discussion in Chapters 11 and 12 dealing with transformation to produce a good fit in regression. A brief presentation is given regarding the robustness of analysis of variance to the assumption of homogeneous variance. This discussion is connected to previous sections on diagnostic plots to detect violations in assumptions. Additional mention is made of the root causes of violation of the homogeneous variance assumption and how it is often a natural occurrence when the variance is a function of the mean. Transformations are discussed that can be used to ac- commodate the problem. Examples and exercises are used for illustration. Several new exercises were added. The total number of exercises in this chapter is 67.

20. xxn Preface Chapter 14: Factorial Experiments (Two or More Factors) Considerable attention is given to the concept, of interaction and interaction plots quite early in the chapter. Examples are given in which scientific interpretations of interaction are given using graphics. New exercises highlight the use of graphics including diagnostic plots of residuals. Several new exercises appear in this chapter. All include experimental data from chemical and biological sciences and all include emphasis on graphical analysis. There are 43 exercises in this chapter. Chapter 15: 2k Factorial Experiments and Fractions Early in this chapter new material has been added to highlight and illustrate the role of two-level designs as screening experiments. In this regard they are often part of a sequential plan in which the scientist or engineer is attempting to learn about the process, assess the role of the candidate factors, and give insight that will aid in determining the most fruitful region of experimentation. The notion of fractional factorial designs is motivated early. The motivation of the notion of "effects" and the graphical procedures that are used in determining "active effects" are discussed in more detail with examples. The chapter uses considerably more graphical illustrations and geometric displays to motivate the concepts for both full and fractional factorials. In addition, graphical depictions are used to illustrate the available lack-of-fit information when one augments the two-level design with center runs. In the development and discussion of fractional factorial designs, the procedure for constructing the fraction is greatly simplified and made much more intuitively appealing. "Added columns" that are selected according to the desired alias structure are used with several examples. We feel as if the reader can now gain a better understanding of what is gained (and lost) by using fractions. This represents a major simplification from the previous edition. For the first time a substantial table is given that allows the reader to construct two-level designs of resolution HI and IV. Eighteen new exercises were added to this chapter. The total number of exercises in this chapter is now 50. Chapter 16: Nonparametric Statistics No major changes are included. The total number of exercises is 41. Chapter 17: Statistical Quality Control No major changes are included. The total number of exercises is 10. Chapter 18: Bayesian Statistics (optional) This chapter is completely new in the eighth edition. The material on Bayesian statistics in the seventh edition (in Chapter 9) was removed in favor of featuring this subject in a new self-contained chapter. This chapter treats the pragmatic and highly useful elements of Bayesian statistics of which students in science and engineering should be aware. The chapter

21. Preface xxiii presents the important concept of subjective probability in conjunction with the notion that in many applications population parameters are truly not constant but should be treated as random variables. Point and interval estimation is treated from a Bayesian point of view and practical examples are displayed, This chapter is relatively short (ten pages) and contains 9 examples and 11 exercises. Acknowledgements We are indebted to those colleagues who reviewed the previous editions of this book and provided many helpful suggestions for this edition. They are: Andre Adler, Illinois institute of Technology. Georgiana Baker, University of South Carolina-, Barbara Bonnie, University of Minnesota-. Nirmal Devi, Embry Riddle; Ruxu Du, University of Miami; Stephanie Edwards. Demidji State University. Charles McAl- lister, Louisiana State University; Judith Miller, Georgetown University, Timothy Raymond, Bucknell University; Dennis Webster, Louisiana State University; Blake Whitten, University of Iowa; Michael Zabarankin, Stevens Institute of Technology. We would like to thank the editorial and production services provided by nu- merous people from Prentice Hall, especially the editor in chief Sally Yagan, production editor Lynn Savino Wcndel, and copy editor Patricia Daly. Many useful comments, suggestions and proof-readings by Richard Charnigo. Jr., Michael An- derson, Joleen Beltrami and George Lobcll are greatly appreciated. We thank the Virginia Tech Statistical Consulting Center which was the source of many real-life data sets. In addition we thank Linda Douglas who worked hard in helping the preparation of the manuscript. R.H.M. S.L.M. K.Y.

22. Chapter 1 Introduction to Statistics and Data Analysis 1.1 Overview: Statistical Inference, Samples, Populations, and Experimental Design Beginning in the 1980s and continuing into the twenty-first century: an inordi- nate amount of attention has been focused on improvement of quality in American industry. Much has been said and written about the Japanese "industrial mira- cle," which began in the middle of the twentieth century. The Japanese were able to succeed where we and other countries had failed-namely, to create an atmo- sphere that allows the production of high-quality products. Much of the success of the Japanese has been attributed to the use of statistical methods and statistical thinking among management personnel. Use of Scientific Data The use of statistical methods in manufacturing, development of food products, computer software, pharmaceutical, and many other areas involves the gathering of information or scientific data. Of course, the gathering of data is nothing new. It has been done for well over a thousand years. Data have been collected, summarized, reported, and stored for perusal. However, there is a profound distinction between collection of scientific information and inferential statistics. It is the latter that has received rightful attention in recent decades. The offspring of inferential statistics has been a large "toolbox" of statistical methods employed by statistical practitioners. These statistical methods are designed to contribute to the process of making scientific: judgments in the face of uncertainty and variation. The product density of a particular material from a manufacturing process will not always be the same. Indeed, if the process involved is a batch process rather than continuous, there will be variation in material density between not only the batches (batch-to-batch variation) that come off the line, but also within-batch variation. Statistical methods are used to analyze data from a process such as this one in order to gain more sense of where in the prr cess changes may be made to improve the quality of the process. In this, qur"

23. Chapter 1 Introduction to Statistics and Data Analysis may well be defined in relation to closeness to a target density value in harmony with what portion of the time this closeness criterion is met. An engineer may be concerned with a specific instrument that is used to measure sulfur monoxide in the air during pollution studies. If the engineer has doubts about the effectiveness of the instrument, there are two sources of variation that must be dealt with. The first is the variation in sulfur monoxide values that are found at the same locale on the same day. The second is the variation between values observed and the true sulfur monoxide that is in the air at the time. If either of these two sources of variation is exceedingly large (according to some standard set by the engineer), the instrument may need to be replaced. In a biomedical study of a new drug that reduces hypertension, 85% of patients experienced relief while it is generally recognized that the current or "old drug" brings relief to 80% of patients that have chronic hypertension. However, the new drug is more expensive to make and may result in certain side effects. Should the new drug be adopted? This is a problem that is encountered (often with much more complexity) frequently by pharmaceutical firms in harmony with the FDA (Federal Drug Administration). Again, the consideration of variation needs to be taken into account. The "85%" value is based on a certain number of patients chosen for the study. Perhaps if the study were repeated with new patients the observed number of "successes" would be 75%! It is the natural variation from study to study that must be taken into account in the decision process. Clearly this variation is important since variation from patient to patient is endemic to the problem. Variability in Scientific Data In the problems discussed above the statistical methods used involve dealing with variability and in each case the variability to be studied is that encountered in scientific data. If the observed product density in the process is always the same and is always on target, there would be no need for statistical methods. If the device for measuring sulfur monoxide always gives the same value and the value is accurate (i.e., it is correct), no statistical analysis is needed. If there was no patient-to-patient variability inherent in the response to the drug (i.e., it either always brings relief or not), life would be simple for scientists in the pharmaceutical firms and FDA and no statistician would be needed in the decision process. Inferential statistics has produced an enormous number of analytical methods that allow for analysis of data from systems like those described above. This reflects the true nature of the science that we call inferential statistics, namely that of using techniques that allow us to go beyond merely reporting data but, rather, allow the drawing of conclusions (or inferences) about the scientific system. Statisticians make use of fundamental laws of probability and statistical inference to draw conclusions about scientific systems. Information is gathered in the form of samples, or collections of observations. The process of sampling is introduced in Chapter 2 and the discussion continues throughout the entire book. Samples are collected from populations that are collections of all individuals or individual items of a particular type. At times a population signifies a scientific system. For example, a manufacturer of computer boards may wish to eliminate defects. A sampling process may involve collecting information on 50 computer boards sampled randomly from the process. Here, the population is all computer

24. 1.1 Overview: Statistical Inference, Samples, Populations and Experimental Design 3 boards manufactured by the firm over a specific period of time. In a drug experiment, a sample of patients is taken and each is given a specific drug to reduce blood pressure. The interest is focused on drawing conclusions about the population of those who suffer from hypertension. If an improvement is made in the computer board process and a second sample of boards is collected, any conclusions drawn regarding the effectiveness of the change in process should extend to the entire population of computer boards produced under the "improved process." Often, it is very important to collect scientific data in a systematic way, with planning being high on the agenda. At times the planning is, by necessity, quite limited. We often focus only on certain properties or characteristics of the items or objects in the population. This characteristic has particular engineering or, say, biological importance to the "customer," the scientist or engineer who seeks to learn about the population. For example, in one of the illustrations above the quality of the process had to do with the product density of the output of a process. An engineer may need to study the effect of process conditions, temperature, humidity, amount of a particular ingredient, and so on. He or she can systematically move these factors to whatever levels are suggested according to whatever prescription or experimental design is desired. However, a forest scientist who is interested in a study of factors that influence wood density in a certain kind of tree cannot necessarily design an experiment. In this case it may require an observational study in which data are collected in the field but factor levels could not be pre- selected. Both of these types of studies lend themselves to methods of statistical inference. In the former, the quality of the inferences will depend on proper planning of the experiment. In the latter, the scientist is at the mercy of what can be gathered. For example, it is sad if an agronomist is interested in studying the effect of rainfall on plant yield and the data are gathered during a drought. One should gain an insight into the importance of statistical thinking by managers and the use of statistical inference by scientific personnel. Research scientists gain much from scientific data. Data provide understanding of scientific phenom- ena. Product and process engineers learn more in their off-line efforts to improve the process. They also gain valuable insight by gathering production data (on- line monitoring) on a regular basis. This allows for determination of necessary modifications in order to keep the process at a desired level of quality. There are times when a scientific practitioner wishes only to gain some sort of summary of a set of data represented in the sample. In other words, no inferential statistics are used. Rather a set of single-number statistics or descriptive statistics is helpful. These numbers give a sense of center of location of the data, variability in the data, and the general nature of the distribution of observations in the sample. Though no specific statistical methods leading to statistical inference are incorporated, much can be learned. At times, descriptive statistics are accompanied by graphics. Modern statistical software packages allow for computation of means, medians, standard deviations, and other single-number statistics as well as produce graphs that show a "footprint" of the nature of the sample. Definitions and illustrations of the single-number statistics, as well as descriptions of graphical methods including histograms, stem-and-leaf plots, dot plots, and box plots, will be given in sections that follow.

25. Chapter 1 Introduction to Statistics and Data Analysis 1.2 The Role of Probability In this book, Chapters 2 to 6 deal with fundamental notions of probability. A thorough grounding in these concepts allows the reader to have a better understanding of statistical inference. Without some formalism in probability, the student cannot appreciate the true interpretation of data analysis through modern statistical methods. It is quite natural to study probability prior to studying statistical inference. Elements of probability allow us to quantify the strength or "confidence" in our conclusions. In this sense, concepts in probability form a major component that supplements statistical methods and help gauge the strength of the statistical inference. The discipline of probability, then, provides the transition between descriptive statistics and inferential methods. Elements of probability allow the conclusion to be put into the language that the science or engineering practitioners require. An example follows that enables the reader to understand the notion of a P-value, which often provides the "bottom line" in the interpretation of results from the use of statistical methods. Example 1.1:1 Suppose that an engineer encounters data from a manufacturing process in which 100 items are sampled and 10 are found to be defective. It is expected and antic- ipated that occasionally there will be defective items. Obviously these 100 items represent the sample. However, it has been determined that in the long run, the company can oidy tolerate 5% defective in the process. Now, the elements of probability allow the engineer to determine how conclusive the sample information is regarding the nature of the process. In this case the population conceptually represents all possible items from the process. Suppose we learn that if the process is acceptable, that is, if it does produce items no more than 5% of which are defective, there is a probability of 0.0282 of obtaining 10 or more defective items in a random sample of 100 items from the process. This small probability suggests that the process does, indeed, have a long-run percent defective that exceeds 5%. In other words, under the condition of an acceptable process, the sample information obtained would rarely occur. However, it did occur! Clearly, though, it would occur with a much higher probability if the process defective rate exceeded 5% by a significant amount. J From this example it becomes clear that the elements of probability aid in the translation of sample information into something conclusive or inconclusive about the scientific system. In fact, what was learned likely is alarming information to the engineer or manager. Statistical methods (which we will actually detail in Chapter 10) produced a P-value of 0.0282. The result suggests that the process very likely is not acceptable. The concept of a P-value is dealt with at length in succeeding chapters. The example that follows provides a second illustration. Example 1.2:1 Often the nature of the scientific study will dictate the role that probability and deductive reasoning play in statistical inference. Exercise 9.40 on page 297 provides data associated with a study conducted at the Virginia Polytechnic Institute and State University on the development, of a relationship between the roots of trees and the action of a fungus. Minerals are transferred from the fungus to the trees and sugars from the trees to the fungus. Two samples of 10 northern red oak seedlings

26. 1.2 The Role of Probability are planted in a greenhouse, one containing seedlings treated with nitrogen and one containing no nitrogen. All other environmental conditions are held constant. All seedlings contain the fungus Pisolithus tinctorus. More details are supplied in Chapter 9. The stem weights in grams were recorded after the end of 140 days. The data are given in Table 1.1. Table 1.1: Data Set for Example 1.2 No Nitrogen 0.32 0.53 0.28 0.37 0.47 0.43 0.36 0.42 0.38 0.43 Nitrogen 0.26 0.43 0.47 0.49 0.52 0.75 0.79 0.86 0.62 0.46 In this example there are two samples from two separate populations. The purpose of the experiment is to determine if the use of nitrogen has an influence on the growth of the roots. The study is a comparative study (i.e.. we seek to compare the two populations with regard to a certain important characteristic). It is instructive to plot the data as shown in Figure 1.1. The o values represent the "with nitrogen" data and the x values represent the "without nitrogen" data. Now, the purpose of this experiment is to determine whether the use of nitrogen has an influence on the growth of the roots. Notice that the general appearance of the data might suggest to the reader that, on average, the use of nitrogen increases the stem weight. Four nitrogen observations are considerably larger than any of the no-nitrogen observations. Most of the no-nitrogen observations appear to be below the center of the data. The appearance of the data set would seem to indicate that nitrogen is effective. But how can this be quantified? How can all of the apparent visual evidence be summarized in some sense? As in the preceding example, the fundamentals of probability can be used. The conclusions may be summarized in a probability statement or P-value. Wc will not show here the statistical inference that produces the summary probability. As in Example 1.1, these methods will be discussed in Chapter 10. The issue revolves around the "probability that data like these could be observed" given that nitrogen has no effect, in other words, given that both samples were generated from the same population. Suppose that this probability is small, say 0.03. That would certainly be strong evidence that the use of nitrogen does indeed influence (apparently increases) average stem weight of the red oak seedlings. J How Do Probability and Statistical Inference Work Together? It. is important for the reader to gain the clear distinction between the discipline of probability, a science in its own right, and the discipline of inferential statistics. As

27. Chapter 1 Introduction to Statistics and Data Analysis o 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 Figure 1.1: Stern weight data.. we have already indicated, the use or application of concepts in probability allows real-life interpretation of the results of statistical inference. As a result, it can be said that statistical inference makes use of concepts in probability. One can glean from the two examples above that, the sample information is available; to the analyst and, with the aid of statistical methods and elements of probability, conclusions are made about, some feature of the population (the process does not appear to be acceptable in Example 1.1 and nitrogen does influence average stem weights in Example 1.2). Thus for a statistical problem the sample along with inferential statistics allows us to draw conclusions about the population, with inferential statistics making clear use of elements of probability. This reasoning is inductive in nature. Now as we move into Chapter 2 and beyond, the reader will note' thai unlike our two examples here, we will not focus on solving statistical problems. Many examples will be given in which no sample is involved. There will be a population clearly described with all features of the population known. Then questions of importance will focus on the nature of data that might hypothetical]}' be drawn from the population. Thus one can say that problems in probability allow us to draw conclusions about characteristics of hypothetical data taken from the population based on known features of the population. This type of reasoning is deductive in nature. Figure 1.2 shows the fundamental relationship between probability and inferential statistics. Probability Population Sample Statistical Inference Figure 1.2: Fundamental relationship between probability and inferential statistics. Now, in the grand scheme of things, which is more important, the field of probability or the field of statistics? They are both very important and clearly arc complementary. The only certainty concerning the pedagogy of the two disciplines lies in the fact that if statistics is to be taught: at more than merely a "cookbook" level, then the discipline of probability must be taught first. This rule stems from the fact that nothing can be learned about a population from a sample until the analyst learns the rudiments of uncertainty in that sample. For example, consider Example 1.1. The question centers around whether or not the population, defined

28. 1.3 Sampling Procedures; Collection of Data by the process, is no more than 5% defective. In other words the conjecture is that on the average 5 out of 100 items are defective. Now, the sample contains 100 items and 10 are defective. Does this support the conjecture or refute it? On the surface it would appear to be a refutation of the conjecture because. 10 out of 100 seem to be "a bit much." But without elements of probability, how do we know'.' Only through the study of materia] in future chapters will we learn that under the condition that the process is acceptable (5% defective), the probability of obtaining 10 or more defective items in a sample of 100 is 0.0282. We have given two examples where the elements of probability provide a summary that the scientist or engineer can use as evidence on which to build a decision. The bridge between the data and the conclusion is, of course, based on foundations of statistical inference, distribution theory, and sampling distributions discussed in future; chapters. 1.3 Sampling Procedures; Collection of Data In Section 1.1 we discussed very briefly the notion of sampling and the sampling process. While sampling appears to be a simple concept, the complexity of the questions that must be answered about the population or populations necessitates that the sampling process be very complex at times. While the notion of sampling is discussed in a technical way in Chapter 8, we shall endeavor here to give: some common sense notions of sampling, This is a natural transition to a discussion of the concept of variability. Simple Random Sampling The importance of proper sampling revolves around the degree of confidence with which the analyst is able to answer the questions being asked. Let us assume that only a single population exists in the problem. Recall that in Example 1.2 two populations were involved. Simple random sampling implies that any particular sample: of a specified sample size has the same chance of being selected as any other sample of the same size. The term sample size simply means the number of elements in the sample. Obviously, a table of random numbers can be utilized in sample selection in many instances. The virtue of simple random sampling is that it aids in the elimination of the problem of having the sample reflect a different (possibly more confined) population than the one about which inferences need to be made. For example, a sample is to be chosen to answer certain questions regarding political preferences in a. certain state in the United States. The sample involves the choice of, say, 1000 families and a survey is to be conducted. Now, suppose it turns out that random sampling is not used. Rather, all or nearly all of the 1000 families chosen live in an urban setting. It is believed that political preferences in rural areas differ from those in urban areas. In other words, the sample drawn actually confined the population and thus the inferences need to be confined to the "limited population," and in this case confining may be undesirable. If, indeed, the inferences need to be made about the state as a whole, the sample of size 1000 described here is often referred to as a. biased sample.

29. Chapter 1 Introduction to Statistics and Data Analysis As we hinted earlier, simple random sampling is not always appropriate. Which alternative approach is used depends on the complexity of the problem. Often, for example, the sampling units are not homogeneous and naturally divide themselves into nonoverlapping groups that are homogeneous. These groups are called strata, and a procedure called stratified random sampling involves random selection of a sample within each stratum. The purpose is to be sure that each of the strata is neither over- or undcrrepresented. For example, suppose a sample survey is conducted in order to gather preliminary opinions regarding a bond referendum that is being considered in a certain city. The city is subdivided into several ethnic groups which represent natural strata. In order not to disregard or overrepreseut any group, separate random samples of families could be chosen from each group. Experimental Design The concept of randomness or random assignment plays a huge role in the area of experimental design, which was introduced very briefly in Section 1.1 and is an important staple in almost any area of engineering or experimental science. This will be discussed at length in Chapters 13 through 15. However, it is instructive to give a brief presentation here in the context of random sampling. A set of so-called treatments or treatment combinations becomes the populations to be studied or compared in some sense. An example: is the "nitrogen" versus "no-nitrogen" treatments in Example 1.2. Another simple example would be "placebo" versus "active drug," or in a corrosion fatigue study we might have treatment combinations that involve specimen that are coated or uncoated as well as conditions of low or high humidity to which the specimen are exposed. In fact, there are four treatment or factor combinations (i.e., 4 populations), and many scientific questions may be asked and answered through statistical and inferential methods. Consider first the situation in Example 1.2. There arc 20 diseased seedlings involved in the experiment. It. is easy to see from the data themselves that the seedlings arc different from each other. Within the nitrogen group (or the no-nitrogen group) there is considerable variability in the stem weights. This variability is due to what is generally called the experimental unit. This is a very important concept in inferential statistics, in fact one whose description will not end in this chapter. The nature of the variability is very important. If it is too large, stemming from a condition of excessive non-homogeneity in experimental units, the variability will "wash out" any detectable difference between the two populations. Recall that in this case that did not occur. The dot plot in Figure 1.1 and P-value indicated a clear distinction between these two conditions. What role do those experimental units play in the data- taking process itself? The common-sense and, indeed, quite standard approach is to assign the 20 seedlings or experimental units randomly to the two treatments or conditions. In the drug study we may decide to use a total of 200 available patients, patients that clearly will be different in some sense. They are the experimental units. However, they all may have the same chronic condition for which the drug is a potential treatment. Then in a so-called completely randomized design 100 patients are assigned randomly to placebo and 100 to the active drug. Again, it is these experimental units within a group or treatment that produce the variability in data results (i.e., variability in the measured result).

30. 1.3 Sampling Procedures; Collection of Data 9 say blood pressure, or whatever drug efficacy value is important. In the corrosion fatigue study the experimental units are the specimen that are the subjects of the corrosion. Why Assign Experimental Units Randomly? What is the possible negative impact of not randomly assigning experimental units to the treatments or treatment combinations? This is seen most clearly in the case of the drug study. Among the characteristics of the patients that produce variability in the results are age, gender, weight, and others. Suppose merely by chance the placebo group contains a sample of people that are predominately heavier than those in the treatment group. Perhaps heavier individuals have a tendency to have a higher blood pressure. This clearly biases the result and, indeed, any result obtained through the application of statistical inference may have little to do with the drug but more to do with differences in weights among the two samples of patients. We should emphasize the attachment of importance to the term variability. Excessive variability among experimental units "camouflages" scientific findings. In future sections we attempt to characterize and quantify measures of variability. In sections that follow we introduce and discuss specific quantities that can be computed in samples; the quantities give a sense of the nature of the sample with respect to center of location of the data and variability in the data, A discussion of several of these single number measures serves to provide a preview of wdiat statistical information will be important components of the statistical methods that are used in Chapters 8 through 15. These measures that help characterize the nature of the data set fall into the category of descriptive statistics. This material is a prelude to a brief presentation of pictorial and graphical methods that go even further in characterization of the data set. The reader should understand that the statistical methods illustrated here will be used throughout the text. In order to offer the reader a clearer picture of what is involved in experimental design studies, we offer Example 1.3. Example 1.3:1 A corrosion study was made in order to determine whether corrosion of an alu- minum metal coated with a corrosion retardation substance reduced the amount of corrosion. The coating is a protectant that is advertised to minimize fatigue damage in this type of material. Also of interest is the influence of humidity on the amount of corrosion. A corrosion measurement can be expressed in thousands of cycles to failure. Two levels of coating, no coating and chemical corrosion coating, were used. In addition, the two relative humidity levels are 20% relative humidity and 80% relative humidity. The experiment involves four treatment combinations that axe listed in the table that follows. There are eight experimental units to be used and they are alu- minum specimens prepared, with two assigned randomly to each of four treatment combinations. The data are presented in Table 1.2. The corrosion data are averages of 2 specimens. A plot of the averages is pictured in Figure 1.3. A relatively large value of cycles to failure represents a small amount of corrosion. As one might expect, an increase in humidity appears

31. 10 Chapter 1 Introduction to Statistics and Data Analysis Table 1.2: Data for Example 1.3 Average Corrosion in Coating Humidity Thousands of Cycles to Failure Uncoated Chemical Corrosion 20% 80% 20% 80% 975 350 1750 1550 2000 O 1000

32. 1.4 Measures of Location: The Sample Mean and Median 11 (1) random assignment of treatment combinations (coating/humidity) to experimental units (specimens) (2) the use of sample averages (average corrosion values) in summarizing sample information (3) the need for consideration of measures of variability in the analysis of any sample or sets of samples This example suggests the need for what follows in Sections 1.4 and 1.5, namely, descriptive statistics that indicate measures of center of location in a set of data, and those that measure variability. 1.4 Measures of Location: The Sample Mean and Median Location measures in a data set are designed to provide the analyst some quantita- tive measure of where the data center is in a sample. In Example 1.2 it appears as if the center of the nitrogen sample clearly exceeds that of the no-nitrogen sample. One obvious and very useful measure is the sample mean. The mean is simply a numerical average. Definition 1.1: Suppose that the observations in a sample are x denoted by x is _ _ v^ x% _ an + %i + f-i n. n I . . - C 2 , . . + X , xn. The sample mean, There are other measures of central tendency that are discussed in detail in future chapters. One important measure is the sample median. The purpose of the sample median is to reflect the central tendency of the sample in such a way that it is uninfluenced by extreme values or outliers. Given that the observations in a sample are xj, X2> , xn, arranged in increasing order of magnitude, the sample median is - _ J r ("+i)/2: if n is odd, X = $(xn/2 + En/2+l)5 n n i s c v c n - For example, suppose the data set is the following: 1.7, 2.2, 3.9, 3.11, and 14.7. The sample mean and median are, respectively, = 5.12, = 3.9. Clearly, the mean is influenced considerably by the presence of the extreme obser- vation, 14.7, whereas the median places emphasis on the true "center" of the data set. In the case of the two-sample data set of Example 1.2, the two measures of

33. 12 Chapter 1 Introduction to Statistics and Data Analysis central tendency for the individual samples are X (no nitrogen) = 0.399 gram, . , . , 0.38 + 0.42 x (no nitrogen) = 0.400 gram, X (nitrogen) = 0.5G5 gram, 0,19 + 0.52 x (nitrogen) = = 0.505 gram. Clearly there is a difference in concept between the mean and median. It may be of interest to the reader with an engineering background that the sample mean is the centroid of the data in a sample. In a sense it is the point at which a fulcrum can be placed to balance a system of "weights" which are the locations of the individual data. This is shown in Figure 1.4 with regard to the "with nitrogen" sample. x = 0.565 i - + - H H I 1 +- 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 Figure 1.4: Sample mean as a centroid of the "with nitrogen" stem weight. In future chapters, the basis for the computation of x is that of an estimate of the population mean. As we indicated earlier, the purpose of statistical inference is to draw conclusions about population characteristics or parameters and estimation is a very important feature of statistical inference. The median and mean can be quite different from each other. Note, however, that in the case of the stem weight data the sample mean value for no-nitrogen is quite similar to the median value. Other Measures of Locations There are several other methods of quantifying the center of location of the data in the sample. We will not deal with them at this point. For the most part, alternatives to the sample mean are designed to produce values that represent compromises between the mean and the median. Rarely do we make use of these other measures. However, it is instructive to discuss one class of estimators, namely the class of trimmed means. A trimmed mean is computed by "trimming away" a certain percent of both the largest and smallest set of values. For example, the 10% trimmed mean is found by eliminating the largest 10% and smallest 10% and computing the average of the remaining values. For example, in the case of the stem weight data we would eliminate the largest and smallest since the sample size is 10 for each sample. So for the without-nitrogen group the 10% trimmed mean is given by 0.32 + 0.37 + 0.47 + 0.36 + 0.42 + 0.38 + 0.43 xtr(10) = 5 = 0.39(50.

34. Exercises 13 Exercises and for the 10% trimmed mean for the with-nitrogen group we have 0.43 + 0.47 + 0.49 + 0.52 + 0.75 + 0.79 + 0.62 + 0.46 ^trcio) 8 = 0.56625. Note that in this case, as expected, the trimmed means are close to both the mean and median for the individual samples. The trimmed mean approach is, of course, more insensitive to outliers than the sample mean but not as insensitive as the median. On the other hand the trimmed mean approach makes use of more information. Note that the sample median is, indeed, a special case of the trimmed mean in which all of the sample data are eliminated apart from the middle one or two observations. 1.1 The following measurements were recorded for the drying time, in hours, of a certain brand of latex paint. 3.4 2.5 4.8 2.9 3.6 2.8 3.3 5.6 3.7 2.8 4.4 4.0 5.2 3.0 4.8 Assume that the measurements are a simple random sample. (a) What is the sample size for the above sample? (b) Calculate the sample mean for this data. (c) Calculate the sample median. (d) Plot the data by way of a dot plot. (e) Compute the 20% trimmed mean for the above data set. 1.2 According to the journal Chemical Engineering, an important property of a fiber is its water absorbency. A random sample of 20 pieces of cotton fiber is taken and the absorbency on each piece was measured. The following are the absorbency values: 18.71 21.41 20.72 21.81 19.29 22.43 20.17 23.71 19.44 20.50 18.92 20.33 23.00 22.85 19.25 21.77 22.11 19.77 18.04 21.12. (a) Calculate the sample mean and median for the above sample values. (b) Compute the 10% trimmed mean. (c) Do a dot plot of the absorbency data. 1.3 A certain polymer is used for evacuation systems for aircraft. It is important that the polymer be re- sistant to the aging process. Twenty specimens of the polymer were used in an experiment. Ten were assigned randomly to be exposed to the accelerated batch aging process that involved exposure to high temperatures for 10 days. Measurements of tensile strength of 218 229 215 204 217 228 211 201 225 221 209 205 the specimens were made and the following data were recorded on tensile strength in psi. No aging: 227 222 218 216 Aging: 219 214 218 203 (a) Do a dot plot of the data. (b) From your plot, does it appear as if the aging process has had an effect on the tensile strength of this polymer? Explain. (c) Calculate the sample mean tensile strength of the two samples. (d) Calculate the median for both. Discuss the similarity or lack of similarity between the mean and median of each group. 1.4 In a study conducted by the Department of Me> chanical Engineering at Virginia Tech, the steel rods supplied by two different companies were compared. Ten sample springs were made out of the steel rods supplied by each company and a measure of flexibility was recorded for each. The data are as follows: Company A: 9.3 8.8 6.8 8.7 8.5 6.7 8.0 6.5 9.2 7.0 Company B: 11.0 9.8 9.9 10.2 10.1 9.7 11.0 11.1 10.2 9.6 (a) Calculate the sample mean and median for the data for the two companies. (b) Plot the data for the two companies on the same line and give your impression. 1.5 Twenty adult males between the ages of 30 and 40 were involved in a study to evaluate the effect of a specific health regimen involving diet and exercise on the blood cholesterol. Ten were randomly selected to be a control group and ten others were assigned to take

35. Control group: Treatment group: 7 5 - 6 12 3 22 5 37 - 4 7 9 5 11 9 4 3 2.07 2.05 2.52 1.99 2.14 2.18 2.15 2.42 2.22 2.09 2.49 2.08 2.03 2.14 2.03 2.42 2.21 2.11 2.37 2.29 2.03 2.02 2.05 2.01 14 Chapter 1 Introduction to Statistics and Data Analysis part in the regimen as the treatment group for a period to be a function of curing temperature. A study was of 6 months. The following data show the reduction in carried out in which samples of 12 specimens of the rub- cholesterol experienced for the time period for the 20 ber were prepared using curing temperatures of 20 C subjects: and 45 C. The data below show the tensile strength values in megapascals. 20 C: 45 C: (a) Do a dot plot of the data for both groups on the same graph. (a) Show a dot plot of the data with both low and high (b) Compute the mean, median, and 10% trimmed temperature tensile strength values. means for both groups. (b) Compute sample mean tensile strength for both (c) Explain why the difference in the mean suggests samples. one conclusion about the effect of the regimen, (c) Docs it appear as if curing temperature has an in- while the difference in medians or trimmed means fluence on tensile strength based on the plot? Com- suggests a different conclusion. ment further. (d) Does anything else appear to be influenced by an 1.6 The tensile strength of silicone rubber is thought increase in cure temperature? Explain. 1.5 Measures of Variability Sample variability plays an important role in data analysis. Process and product variability is a fact of life in engineering and scientific: systems: The control or reduction of process variability is often a source of major difficulty. More and more process engineers and managers are learning that product quality, and as a result, profits that are derived from manufactured products are very much a function of process variability. As a result, much of Chapters 9 through 15 deal with data analysis and modeling procedures in which sample variability plays a major role. Even in small data analysis problems, the success of a particular statistical method may depend on the magnitude of the variability among the observations in the sample. Measures of location in a sample do not provide a proper summary of the nature of a data set. For instance, in Example 1.2 we cannot conclude that the use of nitrogen enhances growth without taking sample variability into account. While the details of the analysis of this type of data set are deferred to Chapter 9, it should be clear from Figure 1.1 that variability among the "no-nitrogen" observations and variability among the "with-nitrogen" observations are certainly of some consequence. In fact, it appears that the variability within the nitrogen sample is larger than that of the no-nitrogen sample. Perhaps there is something about the inclusion of nitrogen that not only increases the stem height (x of 0.565 gram compared to an x of 0.399 gram for the no-nitrogen sample) but also increases the variability in stem height (i.e., renders the stem height more inconsistent). As another example, contrast the two data sets below. Each contains two samples and the difference in the means is roughly the same for the two samples: Data set B seems to provide a much sharper contrast between the two populations from which the samples were taken. If the purpose of such an experiment, is to detect distinction between the two populations, the task is accomplished in the case of data set B. However, in data set A the large variability within the two samples creates difficulty. In fact, it is not clear that there is a distinction bet-ween

36. 1.5 Measures of Variability 15 Data set A: Data set B: X X X X X X O X X O O X X X O O O O O O O O i i 1 I xx x0 X X X X X X X X X X X 0 0 0 0 0 0 0 0 0 0 0 1 1 J. J_ *x X0 the two populations. Sample Range and Sample Standard Deviation Just as there are many measures of central tendency or location there are many measures of spread or variability. Perhaps the simplest one is the sample range ^ m a x - ^ m i n . The range can be very useful and is discussed at length in Chapter 17 on statistical quality control. The sample measure of spread that is used most often is the sample standard deviation. We again let Xi,X2,...,xn denote sample values; Definition 1.2: The sample variance, denoted by s2 , is given by (Xi - x)2 =i = i n - 1 The sample standard deviation, denoted by s, is the positive square root of s2 , that is, s = Vs2 . It should be clear to the reader that the sample standard deviation is, in fact, a measure of variability. Large variability in a data set produces relatively large values of (x x)2 and thus a large sample variance. The quantity n 1 is often called the degrees of freedom associated with the variance estimate. In this simple example the degrees of freedom depict the number of independent pieces of information available for computing variability. For example, suppose that we wish to compute the sample variance and standard deviation of the data set (5, 17, 6, 4). The sample average is x = 8. The computation of the variance involves: (5 - 8)2 + (17 - 8)2 + (6 - 8)2 + (4 - 8)2 = (-3)2 + 92 + (-2)2 + (-4)2 . The quantities inside parentheses sum to zero. In general, Yl (x i ~ &) = 0 (see t=t Exercise 1.16 on page 28). Then the computation of a sample variance does not involve n independent squared deviations from the mean x. In fact, since the last value of x x is determined by the initial n 1 of them, we say that these are n 1 "pieces of information" that produce s2 . Thus there are n 1 degrees of freedom rather than n degrees of freedom for computing a sample variance.

37. 16 Chapter 1 Introduction to Statistics and Data Analysis Example 1.4:1 In an example discussed extensively in Chapter 10, an engineer is interested in testing the "bias" in a pH meter. Data arc collected on the meter by measuring the pH of a neutral substance (pH 7.0). A sample of size 10 is taken with results given by 7.07 7.00 7.10 6.97 7.00 7.03 7.01 7.01 6.98 7.08. The sample mean x is given by . = 7 . 0 7 + 7 . 0 0 + 7 . 1 0 + - + 7.08 = ? ^ The sample variance s2 is given by s2 =I[(7.07 - 7.025)2 + (7.00 - 7.025)2 + (7.10 - 7.025)2 + + (7.08 - 7.025)2 ] = 0.001939. As a result, the sample standard deviation is given by s = /0.00193 = 0.044. So the sample standard deviation is 0.0440 with n 1 = 9 degrees of freedom. J Units for Standard Deviation and Variance It should be apparent from Definition 1.2 that the variance is a measure of the average squared deviation from the mean x. We use the term average squared deviation even though the definition makes use of a division by degrees of freedom n 1 rather than n. Of course, if n is large the difference in the denominator is inconsequential. As a result, the sample variance possesses units that are the square of the units in the observed data whereas the sample standard deviation is found in linear units. As an example, consider the data of Example 1.2. The stem weights are measured in grams. As a result, the sample standard deviations are in grams and the variances are measured in grams . In fact, the individual standard deviations are 0.0728 grams for the no-nitrogen case and 0.1867 grams for the nitrogen group. Note that the variability as characterized by the standard deviation does indicate considerably larger variability in the nitrogen sample. This condition was noted from Figure 1.1. Which Variability Measure Is More Important? As we indicated earlier the sample range has applications in the area of statistical quality control. It may appear to the reader that the use of both the sample variance and the sample standard deviation is redundant. Both measures reflect the same concept in measuring variability but the sample standard deviation measures variability in linear units whereas the sample variance is measured in squared units. Both play huge roles in the use of statistical methods. Much of what is

38. Exercises ] 7 accomplished in the context: of statistical inference involves drawing conclusions about, characteristics of populations. Among these characteristics arc constants which are1 called population parameters. Two important parameters are the population m e a n and the population variance. The sample variance plays an explicit role in the statistical methods used to draw inferences about the population variance. The sample; standard deviation has an important role along with the sample mean in inferences that are made about the population mean. In general, the variance is considered more in inferential theory, while the standard deviation is used more in applications. Exercises 1.7 Consider the drying time data for Exercise 1.1 1.10 For the data of Exercise 1.4 on page 13. com- on page 13. Compute the sample-variance and sample pute both the mean and variance in 'flexibility" for standard deviation, berth company A and company B. 1.8 Compute the sample variance: and standard devi- i . n Consider the data in Exercise 1.5 on page 13. ation for the water absorbency data of Exercise 1.2 on Compute the sample variance and the sample standard page 13. deviation for both the control and treatment groups. 1.9 Exercise 1.3 on page 13 showed samples of tensile 1.12 Rjr Exercise 1.6 em page 14, compute the sample strength data, one for specimens that were exposed to standard deviation in tensile strength for the samples an aging process and one in which there was no ag- separately for the two temperatures. Does it. appear as ing of the specimens. Calculate the sample variance as if ar, ic r c a so in temperature influences the variability well as standard deviation in tensile strength for both m (ensile strength? Explain. samples. 1.6 Discrete and Continuous Data Statistical inference through the analysis of observational studies or designed experiments is used in many scientific areas. The data gathered may be d i s c r e t e or continuous, depending on the area of application. For example1 , a chemical engineer may be interested in conducting an experiment that will lead to conditions where yield is maximized. Here, of course, the yield may be in percent, or grams/pound, measured on a continuum. On the other hand, a toxicologist conducting a combination drug experiment may encounter data that are binary in nature (i.e., the patient either responds or not). Great distinctions arc made between discrete and continuous data in the probability theory that allow us to draw statistical inferences. Often applications of statistical inference are found when the data are count, data. For example, an engineer may be interested in studying the number of radioactive particles passing through a counter in, say, 1 millisecond. Personnel responsible for the1 efficiency of a port facility may be interested in the characteristics of the number of oil tankers arriving each day at a certain port: city, In Chapter 5, several distinct scenarios, leading to different ways of handling data arc discussed for situations with count data. Special attention even at this early stage of the textbook should be paid to some details associated with binary data. Applications requiring statistical analysis of

39. 18 Chapter 1 Introduction to Statistics and Data Analysis binary data are voluminous. Often the measure that is used in the analysis is the sample proportion. Obviously the binary situation involves two categories. If there are n units involved in the data and x is defined as the number that fall into category 1, then n x fall into category 2. Thus, x/n is the sample proportion in category 1 and 1 x/n is the sample proportion in category 2. In the biomedical application, 50 patients may represent the sample units and if 20 out of 50 experienced an improvement in a stomach ailment (common to all 50) after all were given the drug, then |g = 0.4 is the sample proportion for which the drug was a success and 1 0.4 = 0.6 is the sample proportion for which the drug was not successful. Actually the basic numerical measurement for binary data is generally denoted by either 0 or 1. For example, in our medical example, a successful result is denoted by a 1 and a nonsuccess a 0. As a result, the sample proportion is actually a sample mean of the ones and zeros. For the successful category, xi + x2 + + x50 _ 1 + 1 + 0 + - - + 0 + 1 _ 20 50 ~ 50 ~ 50 ~ ' ' What Kinds of Problems Are Solved in Binary Data Situations? The kinds of problems facing scientists and engineers dealing in binary data are not a great deal unlike those seen where continuous measurements are of interest. However, different techniques are used since the statistical properties of sample proportions are quite different from sample means that result from averages taken from continuous populations. Consider the example data in Exercise 1.6 on page 14. The statistical problem underlying this illustration focuses on whether an in- tervention, say, on increase in cure temperature, will alter the population mean tensile strength associated with the silicone rubber process. On the other hand, in a quality control area suppose an automobile tire manufacturer reports that a shipment of 5000 tires selected randomly from the process results in 100 of them showing blemishes. Here the sample proportion is ^j = 0.02. Following a change in the process designed to reduce blemishes, a second sample of 5000 is taken and 90 tires are blemished. The sample proportion has been reduced to ggj, = 0.018. The question arises, "Is the decrease in the sample proportion from 0.02 to 0.018 substantial enough to suggest a real improvement in the population proportion?" Both of these illustrations require the use of the statistical properties of sample averages-one from samples from a continuous population, and the other from samples from a discrete (binary) population. In both cases the sample mean is an estimate of a population parameter, a population mean in the first illustration (i.e., mean tensile strength), and a population proportion (i.e., proportion of blemished tires in the population), in the second case. So here we have sample estimates used to draw scientific conclusions regarding population parameters. As we indicated in Section 1.4, this is the general theme in many practical problems using statistical inference.

40. 1.7 Statistical Modeling, Scientific Inspection, and Graphical Diagnostics 19 1.7 Statistical Modeling, Scientific Inspection, and Graphical Diagnostics Often the end result of a statistical analysis is the estimation of parameters of a postulated model. This is natural for scientists and engineers since they often deal in modeling. A statistical model is not deterministic but, rather, must entail some probabilistic: aspects. A model form is often the foundation of assumptions that are made by the analyst. For example, in our Example 1.2 the scientist may wish to draw some level of distinction between the "nitrogen" and "no-nitrogen" populations through the sample information. The analysis may require a certain model for the data, for example, that the two samples conic: from normal or Gaussian distr

probability and statistics_for_engineers_and_scientists_8th_edition_by_ronald_e._walpole

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