TESTBANK FOR PROBABILITY AND STATISTICS FOR ENGINEERING AND THE SCIENCES, 8TH EDITION BY DEVORE 

Test Bank For Probability and Statistics for Engineering and the Sciences, 8th edition by Devore. (Complete Download) All Chapters 1-16 Contents 1 Overview and Descriptive Statistics Introduction 1 1.1 Populations, Samples, and Processes 2 1.2 Pictorial and Tabular Methods in Descriptive Statistics 12 1.3 Measures of Location 28 1.4 Measures of Variability 35 Supplementary Exercises 46 Bibliography 49 2 Probability Introduction 50 2.1 Sample Spaces and Events 51 2.2 Axioms, Interpretations, and Properties of Probability 55 2.3 Counting Techniques 64 2.4 Conditional Probability 73 2.5 Independence 83 Supplementary Exercises 88 Bibliography 91 3 Discrete Random Variables and Probability Distributions Introduction 92 3.1 Random Variables 93 3.2 Probability Distributions for Discrete Random Variables 96 3.3 Expected Values 106 3.4 The Binomial Probability Distribution 114 3.5 Hypergeometric and Negative Binomial Distributions 122 3.6 The Poisson Probability Distribution 128 Supplementary Exercises 133 Bibliography 136 vii Copyright 2010 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.viii Contents 4 Continuous Random Variables and Probability Distributions Introduction 137 4.1 Probability Density Functions 138 4.2 Cumulative Distribution Functions and Expected Values 143 4.3 The Normal Distribution 152 4.4 The Exponential and Gamma Distributions 165 4.5 Other Continuous Distributions 171 4.6 Probability Plots 178 Supplementary Exercises 188 Bibliography 192 5 Joint Probability Distributions and Random Samples Introduction 193 5.1 Jointly Distributed Random Variables 194 5.2 Expected Values, Covariance, and Correlation 206 5.3 Statistics and Their Distributions 212 5.4 The Distribution of the Sample Mean 223 5.5 The Distribution of a Linear Combination 230 Supplementary Exercises 235 Bibliography 238 6 Point Estimation Introduction 239 6.1 Some General Concepts of Point Estimation 240 6.2 Methods of Point Estimation 255 Supplementary Exercises 265 Bibliography 266 7 Statistical Intervals Based on a Single Sample Introduction 267 7.1 Basic Properties of Confidence Intervals 268 7.2 Large-Sample Confidence Intervals for a Population Mean and Proportion 276 Copyright 2010 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.Contents 7.3 Intervals Based on a Normal Population Distribution 285 7.4 Confidence Intervals for the Variance and Standard Deviation of a Normal Population 294 Supplementary Exercises 297 Bibliography 299 8 Tests of Hypotheses Based on a Single Sample Introduction 300 8.1 Hypotheses and Test Procedures 301 8.2 Tests About a Population Mean 310 8.3 Tests Concerning a Population Proportion 323 8.4 P-Values 328 8.5 Some Comments on Selecting a Test 339 Supplementary Exercises 342 Bibliography 344 9 Inferences Based on Two Samples Introduction 345 9.1 z Tests and Confidence Intervals for a Difference Between Two Population Means 346 9.2 The Two-Sample t Test and Confidence Interval 357 9.3 Analysis of Paired Data 365 9.4 Inferences Concerning a Difference Between Population Proportions 375 9.5 Inferences Concerning Two Population Variances 382 Supplementary Exercises 386 Bibliography 390 10 The Analysis of Variance Introduction 391 10.1 Single-Factor ANOVA 392 10.2 Multiple Comparisons in ANOVA 402 10.3 More on Single-Factor ANOVA 408 Supplementary Exercises 417 Bibliography 418 ix Copyright 2010 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.x Contents 11 Multifactor Analysis of Variance Introduction 419 11.1 Two-Factor ANOVA with Kij ⫽ 1 420 11.2 Two-Factor ANOVA with Kij ⬎ 1 433 11.3 Three-Factor ANOVA 442 11.4 2p Factorial Experiments 451 Supplementary Exercises 464 Bibliography 467 12 Simple Linear Regression and Correlation Introduction 468 12.1 The Simple Linear Regression Model 469 12.2 Estimating Model Parameters 477 12.3 Inferences About the Slope Parameter ␤1 490 12.4 Inferences Concerning and the Prediction of Future Y Values 499 mY #x* 12.5 Correlation 508 Supplementary Exercises 518 Bibliography 522 13 Nonlinear and Multiple Regression Introduction 523 13.1 Assessing Model Adequacy 524 13.2 Regression with Transformed Variables 531 13.3 Polynomial Regression 543 13.4 Multiple Regression Analysis 553 13.5 Other Issues in Multiple Regression 574 Supplementary Exercises 588 Bibliography 593 14 Goodness-of-Fit Tests and Categorical Data Analysis Introduction 594 14.1 Goodness-of-Fit Tests When Category Probabilities Are Completely Specified 595 Copyright 2010 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.Contents 14.2 Goodness-of-Fit Tests for Composite Hypotheses 602 14.3 Two-Way Contingency Tables 613 Supplementary Exercises 621 Bibliography 624 15 Distribution-Free Procedures Introduction 625 15.1 The Wilcoxon Signed-Rank Test 626 15.2 The Wilcoxon Rank-Sum Test 634 15.3 Distribution-Free Confidence Intervals 640 15.4 Distribution-Free ANOVA 645 Supplementary Exercises 649 Bibliography 650 16 Quality Control Methods Introduction 651 16.1 General Comments on Control Charts 652 16.2 Control Charts for Process Location 654 16.3 Control Charts for Process Variation 663 16.4 Control Charts for Attributes 668 16.5 CUSUM Procedures 672 16.6 Acceptance Sampling 680 Supplementary Exercises 686 Bibliography 687 Appendix Tables A.1 Cumulative Binomial Probabilities A-2 A.2 Cumulative Poisson Probabilities A-4 A.3 Standard Normal Curve Areas A-6 A.4 The Incomplete Gamma Function A-8 A.5 Critical Values for t Distributions A-9 A.6 Tolerance Critical Values for Normal Population Distributions A-10 A.7 Critical Values for Chi-Squared Distributions A-11 A.8 t Curve Tail Areas A-12 A.9 Critical Values for F Distributions A-14 A.10 Critical Values for Studentized Range Distributions A-20 Copyright 2010 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. xixii Contents A.11 Chi-Squared Curve Tail Areas A-21 A.12 Critical Values for the Ryan-Joiner Test of Normality A-23 A.13 Critical Values for the Wilcoxon Signed-Rank Test A-24 A.14 Critical Values for the Wilcoxon Rank-Sum Test A-25 A.15 Critical Values for the Wilcoxon Signed-Rank Interval A-26 A.16 Critical Values for the Wilcoxon Rank-Sum Interval A-27 A.17 ␤ Curves for t Tests A-28 Answers to Selected Odd-Numbered Exercises A-29 Glossary of Symbols/Abbreviations G-1 Index I-1 Copyright 2010 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.Preface Purpose The use of probability models and statistical methods for analyzing data has become common practice in virtually all scientific disciplines. This book attempts to provide a comprehensive introduction to those models and methods most likely to be encoun- tered and used by students in their careers in engineering and the natural sciences. Although the examples and exercises have been designed with scientists and engi- neers in mind, most of the methods covered are basic to statistical analyses in many other disciplines, so that students of business and the social sciences will also profit from reading the book. Approach Students in a statistics course designed to serve other majors may be initially skeptical of the value and relevance of the subject matter, but my experience is that students can be turned on to statistics by the use of good examples and exercises that blend their every- day experiences with their scientific interests. Consequently, I have worked hard to find examples of real, rather than artificial, data—data that someone thought was worth col- lecting and analyzing. Many of the methods presented, especially in the later chapters on statistical inference, are illustrated by analyzing data taken from published sources, and many of the exercises also involve working with such data. Sometimes the reader may be unfamiliar with the context of a particular problem (as indeed I often was), but I have found that students are more attracted by real problems with a somewhat strange context than by patently artificial problems in a familiar setting. Mathematical Level The exposition is relatively modest in terms of mathematical development. Substantial use of the calculus is made only in Chapter 4 and parts of Chapters 5 and 6. In particu- lar, with the exception of an occasional remark or aside, calculus appears in the inference part of the book only—in the second section of Chapter 6. Matrix algebra is not used at all. Thus almost all the exposition should be accessible to those whose mathematical background includes one semester or two quarters of differential and integral calculus. Content Chapter 1 begins with some basic concepts and terminology—population, sample, descriptive and inferential statistics, enumerative versus analytic studies, and so on— and continues with a survey of important graphical and numerical descriptive methods. A rather traditional development of probability is given in Chapter 2, followed by prob- ability distributions of discrete and continuous random variables in Chapters 3 and 4, respectively. Joint distributions and their properties are discussed in the first part of Chapter 5. The latter part of this chapter introduces statistics and their sampling distri- butions, which form the bridge between probability and inference. The next three chapters cover point estimation, statistical intervals, and hypothesis testing based on a single sample. Methods of inference involving two independent samples and paired data are presented in Chapter 9. The analysis of variance is the subject of Chapters 10 and 11 (single-factor and multifactor, respectively). Regression makes its initial appearance in Chapter 12 (the simple linear regression model and correlation) and xiii Copyright 2010 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in ...

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