1588490822-walpole-probability-statistics-for-engineers-scientists-9th-edition.pdf

Probability & Statistics for Engineers & Scientists This page intentionally left blank Probability & Statistics for Engineers & Scientists NINTH EDITION Ronald E. Walpole Roanoke College Raymond H. Myers Virginia Tech Sharon L. Myers Radford University Keying Ye University of Texas at San Antonio Prentice Hall Editor in Chief: Deirdre Lynch Acquisitions Editor: Christopher Cummings Executive Content Editor: Christine O’Brien Associate Editor: Christina Lepre Senior Managing Editor: Karen Wernholm Senior Production Project Manager: Tracy Patruno Design Manager: Andrea Nix Cover Designer: Heather Scott Digital Assets Manager: Marianne Groth Associate Media Producer: Vicki Dreyfus Marketing Manager: Alex Gay Marketing Assistant: Kathleen DeChavez Senior Author Support/Technology Specialist: Joe Vetere Rights and Permissions Advisor: Michael Joyce Senior Manufacturing Buyer: Carol Melville Production Coordination: Liﬂand et al. Bookmakers Composition: Keying Ye Cover photo: Marjory Dressler/Dressler Photo-Graphics Many of the designations used by manufacturers and sellers to distinguish their products are claimed as trademarks. Where those designations appear in this book, and Pearson was aware of a trademark claim, the designations have been printed in initial caps or all caps. Library of Congress Cataloging-in-Publication Data Probability & statistics for engineers & scientists/Ronald E. Walpole... [et al.] — 9th ed. p. cm. ISBN 978-0-321-62911-1 1. Engineering—Statistical methods. 2. Probabilities. I. Walpole, Ronald E. TA340.P738 2011 519.02’462–dc22 2010004857 Copyright c 2012, 2007, 2002 Pearson Education, Inc. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. Printed in the United States of America. For information on obtaining permission for use of material in this work, please submit a written request to Pearson Education, Inc., Rights and Contracts Department, 501 Boylston Street, Suite 900, Boston, MA 02116, fax your request to 617-671-3447, or e-mail at http://www.pearsoned.com/legal/permissions.htm. 1 2 3 4 5 6 7 8 9 10—EB—14 13 12 11 10 ISBN 10: 0-321-62911-6 ISBN 13: 978-0-321-62911-1 This book is dedicated to Billy and Julie R.H.M. and S.L.M. Limin, Carolyn and Emily K.Y. This page intentionally left blank Contents Preface.......................................................... xv 1 Introduction to Statistics and Data Analysis........... 1 1.1 Overview: Statistical Inference, Samples, Populations, and the Role of Probability.............................................. 1 1.2 Sampling Procedures; Collection of Data........................ 7 1.3 Measures of Location: The Sample Mean and Median........... 11 Exercises................................................... 13 1.4 Measures of Variability.......................................... 14 Exercises................................................... 17 1.5 Discrete and Continuous Data................................... 17 1.6 Statistical Modeling, Scientiﬁc Inspection, and Graphical Diag- nostics.......................................................... 18 1.7 General Types of Statistical Studies: Designed Experiment, Observational Study, and Retrospective Study.................. 27 Exercises................................................... 30 2 Probability.................................................. 35 2.1 Sample Space................................................... 35 2.2 Events.......................................................... 38 Exercises................................................... 42 2.3 Counting Sample Points......................................... 44 Exercises................................................... 51 2.4 Probability of an Event......................................... 52 2.5 Additive Rules.................................................. 56 Exercises................................................... 59 2.6 Conditional Probability, Independence, and the Product Rule... 62 Exercises................................................... 69 2.7 Bayes’ Rule..................................................... 72 Exercises................................................... 76 Review Exercises............................................ 77 viii Contents 2.8 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters............................................... 79 3 Random Variables and Probability Distributions...... 81 3.1 Concept of a Random Variable.................................. 81 3.2 Discrete Probability Distributions............................... 84 3.3 Continuous Probability Distributions............................ 87 Exercises................................................... 91 3.4 Joint Probability Distributions.................................. 94 Exercises................................................... 104 Review Exercises............................................ 107 3.5 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters............................................... 109 4 Mathematical Expectation................................ 111 4.1 Mean of a Random Variable..................................... 111 Exercises................................................... 117 4.2 Variance and Covariance of Random Variables................... 119 Exercises................................................... 127 4.3 Means and Variances of Linear Combinations of Random Variables 128 4.4 Chebyshev’s Theorem........................................... 135 Exercises................................................... 137 Review Exercises............................................ 139 4.5 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters............................................... 142 5 Some Discrete Probability Distributions................ 143 5.1 Introduction and Motivation.................................... 143 5.2 Binomial and Multinomial Distributions......................... 143 Exercises................................................... 150 5.3 Hypergeometric Distribution.................................... 152 Exercises................................................... 157 5.4 Negative Binomial and Geometric Distributions................. 158 5.5 Poisson Distribution and the Poisson Process.................... 161 Exercises................................................... 164 Review Exercises............................................ 166 5.6 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters............................................... 169 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................................................... 193 6.6 Gamma and Exponential Distributions.......................... 194 6.7 Chi-Squared Distribution........................................ 200 6.8 Beta Distribution............................................... 201 6.9 Lognormal Distribution......................................... 201 6.10 Weibull Distribution (Optional)................................. 203 Exercises................................................... 206 Review Exercises............................................ 207 6.11 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.................... 218 Exercises................................................... 222 8 Fundamental Sampling Distributions and Data Descriptions........................................ 225 8.1 Random Sampling.............................................. 225 8.2 Some Important Statistics....................................... 227 Exercises................................................... 230 8.3 Sampling Distributions.......................................... 232 8.4 Sampling Distribution of Means and the Central Limit Theorem. 233 Exercises................................................... 241 8.5 Sampling Distribution of S 2..................................... 243 8.6 t-Distribution................................................... 246 8.7 F -Distribution.................................................. 251 8.8 Quantile and Probability Plots.................................. 254 Exercises................................................... 259 Review Exercises............................................ 260 8.9 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters............................................... 262 x Contents 9 One- and Two-Sample Estimation Problems............ 265 9.1 Introduction.................................................... 265 9.2 Statistical Inference............................................. 265 9.3 Classical Methods of Estimation................................. 266 9.4 Single Sample: Estimating the Mean............................ 269 9.5 Standard Error of a Point Estimate............................. 276 9.6 Prediction Intervals............................................. 277 9.7 Tolerance Limits................................................ 280 Exercises................................................... 282 9.8 Two Samples: Estimating the Diﬀerence between Two Means... 285 9.9 Paired Observations............................................. 291 Exercises................................................... 294 9.10 Single Sample: Estimating a Proportion......................... 296 9.11 Two Samples: Estimating the Diﬀerence between Two Proportions 300 Exercises................................................... 302 9.12 Single Sample: Estimating the Variance......................... 303 9.13 Two Samples: Estimating the Ratio of Two Variances........... 305 Exercises................................................... 307 9.14 Maximum Likelihood Estimation (Optional)..................... 307 Exercises................................................... 312 Review Exercises............................................ 313 9.15 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters............................................... 316 10 One- and Two-Sample Tests of Hypotheses............. 319 10.1 Statistical Hypotheses: General Concepts....................... 319 10.2 Testing a Statistical Hypothesis................................. 321 10.3 The Use of P -Values for Decision Making in Testing Hypotheses. 331 Exercises................................................... 334 10.4 Single Sample: Tests Concerning a Single Mean................. 336 10.5 Two Samples: Tests on Two Means............................. 342 10.6 Choice of Sample Size for Testing Means........................ 349 10.7 Graphical Methods for Comparing Means....................... 354 Exercises................................................... 356 10.8 One Sample: Test on a Single Proportion........................ 360 10.9 Two Samples: Tests on Two Proportions........................ 363 Exercises................................................... 365 10.10 One- and Two-Sample Tests Concerning Variances.............. 366 Exercises................................................... 369 10.11 Goodness-of-Fit Test............................................ 370 10.12 Test for Independence (Categorical Data)....................... 373 Contents xi 10.13 Test for Homogeneity........................................... 376 10.14 Two-Sample Case Study........................................ 379 Exercises................................................... 382 Review Exercises............................................ 384 10.15 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters............................................... 386 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................................................... 398 11.4 Properties of the Least Squares Estimators...................... 400 11.5 Inferences Concerning the Regression Coeﬃcients................ 403 11.6 Prediction...................................................... 408 Exercises................................................... 411 11.7 Choice of a Regression Model................................... 414 11.8 Analysis-of-Variance Approach.................................. 414 11.9 Test for Linearity of Regression: Data with Repeated Observations 416 Exercises................................................... 421 11.10 Data Plots and Transformations................................. 424 11.11 Simple Linear Regression Case Study............................ 428 11.12 Correlation..................................................... 430 Exercises................................................... 435 Review Exercises............................................ 436 11.13 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters............................................... 442 12 Multiple Linear Regression and Certain Nonlinear Regression Models........................... 443 12.1 Introduction.................................................... 443 12.2 Estimating the Coeﬃcients...................................... 444 12.3 Linear Regression Model Using Matrices........................ 447 Exercises................................................... 450 12.4 Properties of the Least Squares Estimators...................... 453 12.5 Inferences in Multiple Linear Regression......................... 455 Exercises................................................... 461 12.6 Choice of a Fitted Model through Hypothesis Testing........... 462 12.7 Special Case of Orthogonality (Optional)........................ 467 Exercises................................................... 471 12.8 Categorical or Indicator Variables............................... 472 xii Contents Exercises................................................... 476 12.9 Sequential Methods for Model Selection......................... 476 12.10 Study of Residuals and Violation of Assumptions (Model Check- ing)............................................................. 482 12.11 Cross Validation, Cp , and Other Criteria for Model Selection.... 487 Exercises................................................... 494 12.12 Special Nonlinear Models for Nonideal Conditions............... 496 Exercises................................................... 500 Review Exercises............................................ 501 12.13 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters............................................... 506 13 One-Factor Experiments: General........................ 507 13.1 Analysis-of-Variance Technique.................................. 507 13.2 The Strategy of Experimental Design............................ 508 13.3 One-Way Analysis of Variance: Completely Randomized Design (One-Way ANOVA)............................................. 509 13.4 Tests for the Equality of Several Variances...................... 516 Exercises................................................... 518 13.5 Single-Degree-of-Freedom Comparisons.......................... 520 13.6 Multiple Comparisons........................................... 523 Exercises................................................... 529 13.7 Comparing a Set of Treatments in Blocks....................... 532 13.8 Randomized Complete Block Designs............................ 533 13.9 Graphical Methods and Model Checking........................ 540 13.10 Data Transformations in Analysis of Variance................... 543 Exercises................................................... 545 13.11 Random Eﬀects Models......................................... 547 13.12 Case Study..................................................... 551 Exercises................................................... 553 Review Exercises............................................ 555 13.13 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters............................................... 559 14 Factorial Experiments (Two or More Factors).......... 561 14.1 Introduction.................................................... 561 14.2 Interaction in the Two-Factor Experiment....................... 562 14.3 Two-Factor Analysis of Variance................................ 565 Exercises................................................... 575 14.4 Three-Factor Experiments....................................... 579 Exercises................................................... 586 Contents xiii 14.5 Factorial Experiments for Random Eﬀects and Mixed Models.... 588 Exercises................................................... 592 Review Exercises............................................ 594 14.6 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters............................................... 596 15 2k Factorial Experiments and Fractions................. 597 15.1 Introduction.................................................... 597 15.2 The 2k Factorial: Calculation of Eﬀects and Analysis of Variance 598 15.3 Nonreplicated 2k Factorial Experiment.......................... 604 Exercises................................................... 609 15.4 Factorial Experiments in a Regression Setting................... 612 15.5 The Orthogonal Design......................................... 617 Exercises................................................... 625 15.6 Fractional Factorial Experiments................................ 626 15.7 Analysis of Fractional Factorial Experiments.................... 632 Exercises................................................... 634 15.8 Higher Fractions and Screening Designs......................... 636 15.9 Construction of Resolution III and IV Designs with 8, 16, and 32 Design Points................................................... 637 15.10 Other Two-Level Resolution III Designs; The Plackett-Burman Designs......................................................... 638 15.11 Introduction to Response Surface Methodology.................. 639 15.12 Robust Parameter Design....................................... 643 Exercises................................................... 652 Review Exercises............................................ 653 15.13 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters............................................... 654 16 Nonparametric Statistics.................................. 655 16.1 Nonparametric Tests............................................ 655 16.2 Signed-Rank Test............................................... 660 Exercises................................................... 663 16.3 Wilcoxon Rank-Sum Test....................................... 665 16.4 Kruskal-Wallis Test............................................. 668 Exercises................................................... 670 16.5 Runs Test....................................................... 671 16.6 Tolerance Limits................................................ 674 16.7 Rank Correlation Coeﬃcient.................................... 674 Exercises................................................... 677 Review Exercises............................................ 679 xiv Contents 17 Statistical Quality Control................................ 681 17.1 Introduction.................................................... 681 17.2 Nature of the Control Limits.................................... 683 17.3 Purposes of the Control Chart.................................. 683 17.4 Control Charts for Variables.................................... 684 17.5 Control Charts for Attributes................................... 697 17.6 Cusum Control Charts.......................................... 705 Review Exercises............................................ 706 18 Bayesian Statistics......................................... 709 18.1 Bayesian Concepts.............................................. 709 18.2 Bayesian Inferences............................................. 710 18.3 Bayes Estimates Using Decision Theory Framework............. 717 Exercises................................................... 718 Bibliography.................................................... 721 Appendix A: Statistical Tables and Proofs.................. 725 Appendix B: Answers to Odd-Numbered Non-Review Exercises.................................................. 769 Index........................................................... 785 Preface General Approach and Mathematical Level Our emphasis in creating the ninth edition is less on adding new material and more on providing clarity and deeper understanding. This objective was accomplished in part by including new end-of-chapter material that adds connective tissue between chapters. We aﬀectionately call these comments at the end of the chapter “Pot Holes.” They are very useful to remind students of the big picture and how each chapter ﬁts into that picture, and they aid the student in learning about limitations and pitfalls that may result if procedures are misused. A deeper understanding of real-world use of statistics is made available through class projects, which were added in several chapters. These projects provide the opportunity for students alone, or in groups, to gather their own experimental data and draw inferences. In some cases, the work involves a problem whose solution will illustrate the meaning of a concept or provide an empirical understanding of an important statistical result. Some existing examples were expanded and new ones were introduced to create “case studies,” in which commentary is provided to give the student a clear understanding of a statistical concept in the context of a practical situation. In this edition, we continue to emphasize a balance between theory and appli- cations. Calculus and other types of mathematical support (e.g., linear algebra) are used at about the same level as in previous editions. The coverage of an- alytical tools in statistics is enhanced with the use of calculus when discussion centers on rules and concepts in probability. Probability distributions and sta- tistical inference are highlighted in Chapters 2 through 10. Linear algebra and matrices are very lightly applied in Chapters 11 through 15, where linear regres- sion and analysis of variance are covered. Students using this text should have had the equivalent of one semester of diﬀerential and integral calculus. Linear algebra is helpful but not necessary so long as the section in Chapter 12 on mul- tiple linear regression using matrix algebra is not covered by the instructor. As in previous editions, a large number of exercises that deal with real-life scientiﬁc and engineering applications are available to challenge the student. The many data sets associated with the exercises are available for download from the website http://www.pearsonhighered.com/datasets. xv xvi Preface Summary of the Changes in the Ninth Edition Class projects were added in several chapters to provide a deeper understand- ing of the real-world use of statistics. Students are asked to produce or gather their own experimental data and draw inferences from these data. More case studies were added and others expanded to help students under- stand the statistical methods being presented in the context of a real-life situ- ation. For example, the interpretation of conﬁdence limits, prediction limits, and tolerance limits is given using a real-life situation. “Pot Holes” were added at the end of some chapters and expanded in others. These comments are intended to present each chapter in the context of the big picture and discuss how the chapters relate to one another. They also provide cautions about the possible misuse of statistical techniques presented in the chapter. Chapter 1 has been enhanced to include more on single-number statistics as well as graphical techniques. New fundamental material on sampling and experimental design is presented. Examples added to Chapter 8 on sampling distributions are intended to moti- vate P -values and hypothesis testing. This prepares the student for the more challenging material on these topics that will be presented in Chapter 10. Chapter 12 contains additional development regarding the eﬀect of a single regression variable in a model in which collinearity with other variables is severe. Chapter 15 now introduces material on the important topic of response surface methodology (RSM). The use of noise variables in RSM allows the illustration of mean and variance (dual response surface) modeling. The central composite design (CCD) is introduced in Chapter 15. More examples are given in Chapter 18, and the discussion of using Bayesian methods for statistical decision making has been enhanced. Content and Course Planning This text is designed for either a one- or a two-semester course. A reasonable plan for a one-semester course might include Chapters 1 through 10. This would result in a curriculum that concluded with the fundamentals of both estimation and hypothesis testing. Instructors who desire that students be exposed to simple linear regression may wish to include a portion of Chapter 11. For instructors who desire to have analysis of variance included rather than regression, the one- semester course may include Chapter 13 rather than Chapters 11 and 12. Chapter 13 features one-factor analysis of variance. Another option is to eliminate portions of Chapters 5 and/or 6 as well as Chapter 7. With this option, one or more of the discrete or continuous distributions in Chapters 5 and 6 may be eliminated. These distributions include the negative binomial, geometric, gamma, Weibull, beta, and log normal distributions. Other features that one might consider re- moving from a one-semester curriculum include maximum likelihood estimation, Preface xvii prediction, and/or tolerance limits in Chapter 9. A one-semester curriculum has built-in ﬂexibility, depending on the relative interest of the instructor in regression, analysis of variance, experimental design, and response surface methods (Chapter 15). There are several discrete and continuous distributions (Chapters 5 and 6) that have applications in a variety of engineering and scientiﬁc areas. Chapters 11 through 18 contain substantial material that can be added for the second semester of a two-semester course. The material on simple and multiple linear regression is in Chapters 11 and 12, respectively. Chapter 12 alone oﬀers a substantial amount of ﬂexibility. Multiple linear regression includes such “special topics” as categorical or indicator variables, sequential methods of model selection such as stepwise regression, the study of residuals for the detection of violations of assumptions, cross validation and the use of the PRESS statistic as well as Cp , and logistic regression. The use of orthogonal regressors, a precursor to the experimental design in Chapter 15, is highlighted. Chapters 13 and 14 oﬀer a relatively large amount of material on analysis of variance (ANOVA) with ﬁxed, random, and mixed models. Chapter 15 highlights the application of two-level designs in the context of full and fractional factorial experiments (2k ). Special screening designs are illustrated. Chapter 15 also features a new section on response surface methodology (RSM) to illustrate the use of experimental design for ﬁnding optimal process conditions. The ﬁtting of a second order model through the use of a central composite design is discussed. RSM is expanded to cover the analysis of robust parameter design type problems. Noise variables are used to accommodate dual response surface models. Chapters 16, 17, and 18 contain a moderate amount of material on nonparametric statistics, quality control, and Bayesian inference. Chapter 1 is an overview of statistical inference presented on a mathematically simple level. It has been expanded from the eighth edition to more thoroughly cover single-number statistics and graphical techniques. It is designed to give students a preliminary presentation of elementary concepts that will allow them to understand more involved details that follow. Elementary concepts in sampling, data collection, and experimental design are presented, and rudimentary aspects of graphical tools are introduced, as well as a sense of what is garnered from a data set. Stem-and-leaf plots and box-and-whisker plots have been added. Graphs are better organized and labeled. The discussion of uncertainty and variation in a system is thorough and well illustrated. There are examples of how to sort out the important characteristics of a scientiﬁc process or system, and these ideas are illustrated in practical settings such as manufacturing processes, biomedical studies, and studies of biological and other scientiﬁc systems. A contrast is made between the use of discrete and continuous data. Emphasis is placed on the use of models and the information concerning statistical models that can be obtained from graphical tools. Chapters 2, 3, and 4 deal with basic probability as well as discrete and contin- uous random variables. Chapters 5 and 6 focus on speciﬁc discrete and continuous distributions as well as relationships among them. These chapters also highlight examples of applications of the distributions in real-life scientiﬁc and engineering studies. Examples, case studies, and a large number of exercises edify the student concerning the use of these distributions. Projects bring the practical use of these distributions to life through group work. Chapter 7 is the most theoretical chapter xviii Preface in the text. It deals with transformation of random variables and will likely not be used unless the instructor wishes to teach a relatively theoretical course. Chapter 8 contains graphical material, expanding on the more elementary set of graphi- cal tools presented and illustrated in Chapter 1. Probability plotting is discussed and illustrated with examples. The very important concept of sampling distribu- tions is presented thoroughly, and illustrations are given that involve the central limit theorem and the distribution of a sample variance under normal, independent (i.i.d.) sampling. The t and F distributions are introduced to motivate their use in chapters to follow. New material in Chapter 8 helps the student to visualize the importance of hypothesis testing, motivating the concept of a P -value. Chapter 9 contains material on one- and two-sample point and interval esti- mation. A thorough discussion with examples points out the contrast between the diﬀerent types of intervals—conﬁdence intervals, prediction intervals, and toler- ance intervals. A case study illustrates the three types of statistical intervals in the context of a manufacturing situation. This case study highlights the diﬀerences among the intervals, their sources, and the assumptions made in their develop- ment, as well as what type of scientiﬁc study or question requires the use of each one. A new approximation method has been added for the inference concerning a proportion. Chapter 10 begins with a basic presentation on the pragmatic mean- ing of hypothesis testing, with emphasis on such fundamental concepts as null and alternative hypotheses, the role of probability and the P -value, and the power of a test. Following this, illustrations are given of tests concerning one and two sam- ples under standard conditions. The two-sample t-test with paired observations is also described. A case study helps the student to develop a clear picture of what interaction among factors really means as well as the dangers that can arise when interaction between treatments and experimental units exists. At the end of Chapter 10 is a very important section that relates Chapters 9 and 10 (estimation and hypothesis testing) to Chapters 11 through 16, where statistical modeling is prominent. It is important that the student be aware of the strong connection. Chapters 11 and 12 contain material on simple and multiple linear regression, respectively. Considerably more attention is given in this edition to the eﬀect that collinearity among the regression variables plays. A situation is presented that shows how the role of a single regression variable can depend in large part on what regressors are in the model with it. The sequential model selection procedures (for- ward, backward, stepwise, etc.) are then revisited in regard to this concept, and the rationale for using certain P -values with these procedures is provided. Chap- ter 12 oﬀers material on nonlinear modeling with a special presentation of logistic regression, which has applications in engineering and the biological sciences. The material on multiple regression is quite extensive and thus provides considerable ﬂexibility for the instructor, as indicated earlier. At the end of Chapter 12 is com- mentary relating that chapter to Chapters 14 and 15. Several features were added that provide a better understanding of the material in general. For example, the end-of-chapter material deals with cautions and diﬃculties one might encounter. It is pointed out that there are types of responses that occur naturally in practice (e.g. proportion responses, count responses, and several others) with which stan- dard least squares regression should not be used because standard assumptions do not hold and violation of assumptions may induce serious errors. The suggestion is Preface xix made that data transformation on the response may alleviate the problem in some cases. Flexibility is again available in Chapters 13 and 14, on the topic of analysis of variance. Chapter 13 covers one-factor ANOVA in the context of a completely randomized design. Complementary topics include tests on variances and multiple comparisons. Comparisons of treatments in blocks are highlighted, along with the topic of randomized complete blocks. Graphical methods are extended to ANOVA to aid the student in supplementing the formal inference with a pictorial type of in- ference that can aid scientists and engineers in presenting material. A new project is given in which students incorporate the appropriate randomization into each plan and use graphical techniques and P -values in reporting the results. Chapter 14 extends the material in Chapter 13 to accommodate two or more factors that are in a factorial structure. The ANOVA presentation in Chapter 14 includes work in both random and ﬁxed eﬀects models. Chapter 15 oﬀers material associated with 2k factorial designs; examples and case studies present the use of screening designs and special higher fractions of the 2k. Two new and special features are the presentations of response surface methodology (RSM) and robust parameter design. These topics are linked in a case study that describes and illustrates a dual response surface design and analysis featuring the use of process mean and variance response surfaces. Computer Software Case studies, beginning in Chapter 8, feature computer printout and graphical material generated using both SAS and MINITAB. The inclusion of the computer reﬂects our belief that students should have the experience of reading and inter- preting computer printout and graphics, even if the software in the text is not that which 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 used in the course will be that which the student will be called upon to use in practice following graduation. Examples and case studies in the text are supplemented, where appropriate, by various types of residual plots, quantile plots, normal probability plots, and other plots. Such plots are particularly prevalent in Chapters 11 through 15. Supplements Instructor’s Solutions Manual. This resource contains worked-out solutions to all text exercises and is available for download from Pearson Education’s Instructor Resource Center. Student Solutions Manual ISBN-10: 0-321-64013-6; ISBN-13: 978-0-321-64013-0. Featuring complete solutions to selected exercises, this is a great tool for students as they study and work through the problem material. PowerPoint R Lecture Slides ISBN-10: 0-321-73731-8; ISBN-13: 978-0-321-73731- 1. These slides include most of the ﬁgures and tables from the text. Slides are available to download from Pearson Education’s Instructor Resource Center. xx Preface StatCrunch eText. This interactive, online textbook includes StatCrunch, a pow- erful, web-based statistical software. Embedded StatCrunch buttons allow users to open all data sets and tables from the book with the click of a button and immediately perform an analysis using StatCrunch. StatCrunch TM. StatCrunch is web-based statistical software that allows users to perform complex analyses, share data sets, and generate compelling reports of their data. Users can upload their own data to StatCrunch or search the library of over twelve thousand publicly shared data sets, covering almost any topic of interest. Interactive graphical outputs help users understand statistical concepts and are available for export to enrich reports with visual representations of data. Additional features include A full range of numerical and graphical methods that allow users to analyze and gain insights from any data set. Reporting options that help users create a wide variety of visually appealing representations of their data. An online survey tool that allows users to quickly build and administer surveys via a web form. StatCrunch is available to qualiﬁed adopters. For more information, visit our website at www.statcrunch.com or contact your Pearson representative. Acknowledgments We are indebted to those colleagues who reviewed the previous editions of this book and provided many helpful suggestions for this edition. They are David Groggel, Miami University; Lance Hemlow, Raritan Valley Community College; Ying Ji, University of Texas at San Antonio; Thomas Kline, University of Northern Iowa; Sheila Lawrence, Rutgers University; Luis Moreno, Broome County Community College; Donald Waldman, University of Colorado—Boulder; and Marlene Will, Spalding University. We would also like to thank Delray Schulz, Millersville Uni- versity; Roxane Burrows, Hocking College; and Frank Chmely for ensuring the accuracy of this text. We would like to thank the editorial and production services provided by nu- merous people from Pearson/Prentice Hall, especially the editor in chief Deirdre Lynch, acquisitions editor Christopher Cummings, executive content editor Chris- tine O’Brien, production editor Tracy Patruno, and copyeditor Sally Liﬂand. Many useful comments and suggestions by proofreader Gail Magin are greatly appreci- ated. We thank the Virginia Tech Statistical Consulting Center, which was the source of many real-life data sets. R.H.M. S.L.M. K.Y. Chapter 1 Introduction to Statistics and Data Analysis 1.1 Overview: Statistical Inference, Samples, Populations, and the Role of Probability Beginning in the 1980s and continuing into the 21st century, an inordinate amount of attention has been focused on improvement of quality in American industry. Much has been said and written about the Japanese “industrial miracle,” which began in the middle of the 20th century. The Japanese were able to succeed where we and other countries had failed–namely, to create an atmosphere 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, energy sources, pharmaceuticals, 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 de- signed 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 not only variation in ma- terial density among the batches that come oﬀ the line (batch-to-batch variation), 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 process changes may be made to improve the quality of the process. In this process, qual- 1 2 Chapter 1 Introduction to Statistics and Data Analysis ity 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 amount of 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 drug, or “old” drug, brings relief to 80% of pa- tients 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 conjunction 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 were always the same and were 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 were 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. Statistics researchers have 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, using techniques that allow us to go beyond merely reporting data to drawing 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, which are collections of all individ- uals 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 1.1 Overview: Statistical Inference, Samples, Populations, and the Role of Probability 3 computer boards manufactured by the ﬁrm over a speciﬁc period of time. 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 pro- cess should extend to the entire population of computer boards produced under the “improved process.” 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. 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. Each 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. This case may require an observational study in which data are collected in the ﬁeld but factor levels can not be preselected. 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. The importance of statistical thinking by managers and the use of statistical inference by scientiﬁc personnel is widely acknowledged. Research scientists gain much from scientiﬁc data. Data provide understanding of scientiﬁc phenomena. Product and process engineers learn a great deal 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 them to determine 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, inferential statistics is not required. Rather, a set of single-number statistics or descriptive statistics is helpful. These numbers give a sense of center of the 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 production of graphs that show a “footprint” of the nature of the sample. Deﬁnitions and illustrations of the single-number statistics and graphs, including histograms, stem-and-leaf plots, scatter plots, dot plots, and box plots, will be given in sections that follow. 4 Chapter 1 Introduction to Statistics and Data Analysis 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 under- standing of statistical inference. Without some formalism of probability theory, the student cannot appreciate the true interpretation from data analysis through modern statistical methods. It is quite natural to study probability prior to study- ing 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 helps us 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 will enable 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: 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 only tolerate 5% defective in the process. Now, the elements of prob- ability 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 de- fective, 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 rate of defective items that exceeds 5%. In other words, under the condition of an acceptable process, the sample in- formation 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. 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: 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 294 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 were planted in a greenhouse, one containing seedlings treated with nitrogen and 1.1 Overview: Statistical Inference, Samples, Populations, and the Role of Probability 5 the other containing seedlings with no nitrogen. All other environmental conditions were held constant. All seedlings contained 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 Nitrogen 0.32 0.26 0.53 0.43 0.28 0.47 0.37 0.49 0.47 0.52 0.43 0.75 0.36 0.79 0.42 0.86 0.38 0.62 0.43 0.46 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: A dot plot of stem weight data. 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 the dot plot of Figure 1.1. The ◦ values represent the “nitrogen” data and the × values represent the “no-nitrogen” data. 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 ob- servations 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 summa- rized 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. We 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. 6 Chapter 1 Introduction to Statistics and Data Analysis How Do Probability and Statistical Inference Work Together? It is important for the reader to understand the clear distinction between the discipline of probability, a science in its own right, and the discipline of inferen- tial statistics. As 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 made available to the analyst and, with the aid of statistical methods and elements of probability, conclusions are drawn about some feature of the population (the pro- cess does not appear to be acceptable in Example 1.1, and nitrogen does appear to inﬂuence average stem weights in Example 1.2). Thus for a statistical problem, the sample along with inferential statistics allows us to draw conclu- sions 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 that, unlike what we do in 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 im- portance will focus on the nature of data that might hypothetically be drawn from the population. Thus, one can say that elements 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 are 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 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 1.2 Sampling Procedures; Collection of Data 7 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 material in future chapters will we learn the conditions under which 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 sum- mary 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. 1.2 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. 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, 8 Chapter 1 Introduction to Statistics and Data Analysis 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- nor underrepresented. 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 overrepresent 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 treat- ments 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 specimens that are coated or uncoated as well as conditions of low or high humidity to which the specimens 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 are 20 diseased seedlings involved in the exper- iment. It is easy to see from the data themselves that the seedlings are 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 in- ferential 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 nonhomogeneity 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 treat- ments 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 ran- domized design, 100 patients are assigned randomly to the 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), say blood pressure, or whatever drug eﬃcacy value is important. In the corrosion fatigue study, the experimental units are the specimens that are the subjects of the corrosion. 1.2 Sampling Procedures; Collection of Data 9 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, and weight. 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 and 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 what statistical information will be important components of the statistical methods that are used in future chapters. 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: A corrosion study was made in order to determine whether coating an aluminum metal 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 are listed in the table that follows. There are eight experimental units used, and they are aluminum specimens prepared; two are assigned randomly to each of the four treatment combinations. The data are presented in Table 1.2. The corrosion data are averages of two 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 to make the corrosion worse. The use of the chemical corrosion coating procedure appears to reduce corrosion. In this experimental design illustration, the engineer has systematically selected the four treatment combinations. In order to connect this situation to concepts with which the reader has been exposed to this point, it should be assumed that the 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 20% 975 Uncoated 80% 350 20% 1750 Chemical Corrosion 80% 1550 2000 Chemical Corrosion Coating Average Corrosion 1000 Uncoated 0 0 20% 80% Humidity Figure 1.3: Corrosion results for Example 1.3. conditions representing the four treatment combinations are four separate popula- tions and that the two corrosion values observed for each population are important pieces of information. The importance of the average in capturing and summariz- ing certain features in the population will be highlighted in Section 1.3. While we might draw conclusions about the role of humidity and the impact of coating the specimens from the ﬁgure, we cannot truly evaluate the results from an analyti- cal point of view without taking into account the variability around the average. Again, as we indicated earlier, if the two corrosion values for each treatment com- bination are close together, the picture in Figure 1.3 may be an accurate depiction. But if each corrosion value in the ﬁgure is an average of two values that are widely dispersed, then this variability may, indeed, truly “wash away” any information that appears to come through when one observes averages only. The foregoing example illustrates these concepts: (1) random assignment of treatment combinations (coating, humidity) to experi- mental 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 1.3 Measures of Location: The Sample Mean and Median 11 This example suggests the need for what follows in Sections 1.3 and 1.4, namely, descriptive statistics that indicate measures of center of location in a set of data, and those that measure variability. 1.3 Measures of Location: The Sample Mean and Median Measures of location are designed to provide the analyst with some quantitative values of where the center, or some other location, of data is located. 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 x1 , x2 ,... , xn. The sample mean, denoted by x̄, is n xi x1 + x 2 + · · · + x n x̄ = =. i=1 n n 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. Deﬁnition 1.2: Given that the observations in a sample are x1 , x2 ,... , xn , arranged in increasing order of magnitude, the sample median is x(n+1)/2 , if n is odd, x̃ = 1 2 (xn/2 + xn/2+1 ), if n is even. As an 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, x̄ = 5.12, x̃ = 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 central tendency for the individual samples are x̄ (no nitrogen) = 0.399 gram, 0.38 + 0.42 x̃ (no nitrogen) = = 0.400 gram, 2 x̄ (nitrogen) = 0.565 gram, 0.49 + 0.52 x̃ (nitrogen) = = 0.505 gram. 2 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 12 Chapter 1 Introduction to Statistics and Data Analysis 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 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 x̄ is that of an estimate of the population mean. As we indicated earlier, the purpose of statistical infer- ence 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 the 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 + 0.43 + 0.36 + 0.4

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