## Probability & Statistics for Engineers & Scientists, MyStatLab, Global Edition

(2016)

### Abstract

For junior/senior undergraduates taking probability and statistics as applied to engineering, science, or computer science.

This classic text provides a rigorous introduction to basic probability theory and statistical inference, with a unique balance between theory and methodology. Interesting, relevant applications use real data from actual studies, showing how the concepts and methods can be used to solve problems in the field. This revision focuses on improved clarity and deeper understanding.

This latest edition is also available in as an enhanced Pearson eText. This exciting new version features an embedded version of StatCrunch, allowing students to analyze data sets while reading the book.

MyStatLab™ is not included. Students, if MyStatLab is a recommended/mandatory component of the course, please ask your instructor for the correct ISBN and course ID. MyStatLab should only be purchased when required by an instructor. Instructors, contact your Pearson representative for more information.

MyStatLab is an online homework, tutorial, and assessment program designed to work with this text to engage students and improve results. Within its structured environment, students practice what they learn, test their understanding, and pursue a personalized study plan that helps them absorb course material and understand difficult concepts.

Section Title Page Action Price
Cover Cover
Title Page 1
Dedication 3
Contents 5
Preface 13
Acknowledgments 17
Acknowledgments for the Global Edition 18
1 Introduction to Statistics and Data Analysis 21
1.1 Overview: Statistical Inference, Samples, Populations, and the Role of Probability 21
1.2 Sampling Procedures; Collection of Data 27
1.3 Measures of Location: The Sample Mean and Median 31
Exercises 33
1.4 Measures of Variability 34
Exercises 37
1.5 Discrete and Continuous Data 37
1.6 Statistical Modeling, Scientific Inspection, and Graphical Diagnostics 38
1.7 General Types of Statistical Studies: Designed Experiment, Observational Study, and Retrospective Study 47
Exercises 50
2 Probability 55
2.1 Sample Space 55
2.2 Events 58
Exercises 62
2.3 Counting Sample Points 64
Exercises 71
2.4 Probability of an Event 72
Exercises 79
2.6 Conditional Probability, Independence, and the Product Rule 82
Exercises 89
2.7 Bayes' Rule 92
Exercises 96
Review Exercises 97
2.8 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters 99
3 Random Variables and Probability Distributions 101
3.1 Concept of a Random Variable 101
3.2 Discrete Probability Distributions 104
3.3 Continuous Probability Distributions 107
Exercises 111
3.4 Joint Probability Distributions 114
Exercises 124
Review Exercises 127
3.5 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters 129
4 Mathematical Expectation 131
4.1 Mean of a Random Variable 131
Exercises 137
4.2 Variance and Covariance of Random Variables 139
Exercises 147
4.3 Means and Variances of Linear Combinations of Random Variables 148
4.4 Chebyshev's Theorem 155
Exercises 157
Review Exercises 159
4.5 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters 162
5 Some Discrete Probability Distributions 163
5.1 Introduction and Motivation 163
5.2 Binomial and Multinomial Distributions 163
Exercises 170
5.3 Hypergeometric Distribution 172
Exercises 177
5.4 Negative Binomial and Geometric Distributions 178
5.5 Poisson Distribution and the Poisson Process 181
Exercises 184
Review Exercises 186
5.6 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters 189
6 Some Continuous Probability Distributions 191
6.1 Continuous Uniform Distribution 191
6.2 Normal Distribution 192
6.3 Areas under the Normal Curve 196
6.4 Applications of the Normal Distribution 202
Exercises 205
6.5 Normal Approximation to the Binomial 207
Exercises 213
6.6 Gamma and Exponential Distributions 214
6.7 Chi-Squared Distribution 220
6.8 Beta Distribution 221
6.9 Lognormal Distribution 221
6.10 Weibull Distribution (Optional) 223
Exercises 226
Review Exercises 227
6.11 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters 229
7 Functions of Random Variables (Optional) 231
7.1 Introduction 231
7.2 Transformations of Variables 231
7.3 Moments and Moment-Generating Functions 238
Exercises 242
8 Fundamental Sampling Distributions and Data Descriptions 245
8.1 Random Sampling 245
8.2 Some Important Statistics 247
Exercises 250
8.3 Sampling Distributions 252
8.4 Sampling Distribution of Means and the Central Limit Theorem 253
Exercises 261
8.5 Sampling Distribution of S2 263
8.6 t-Distribution 266
8.7 F-Distribution 271
8.8 Quantile and Probability Plots 274
Exercises 279
Review Exercises 280
8.9 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters 282
9 One- and Two-Sample Estimation Problems 285
9.1 Introduction 285
9.2 Statistical Inference 285
9.3 Classical Methods of Estimation 286
9.4 Single Sample: Estimating the Mean 289
9.5 Standard Error of a Point Estimate 296
9.6 Prediction Intervals 297
9.7 Tolerance Limits 300
Exercises 302
9.8 Two Samples: Estimating the Difference between Two Means 305
9.9 Paired Observations 311
Exercises 314
9.10 Single Sample: Estimating a Proportion 316
9.11 Two Samples: Estimating the Difference between Two Proportions 320
Exercises 322
9.12 Single Sample: Estimating the Variance 323
9.13 Two Samples: Estimating the Ratio of Two Variances 325
Exercises 327
9.14 Maximum Likelihood Estimation (Optional) 327
Exercises 332
Review Exercises 333
9.15 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters 336
10 One- and Two-Sample Tests of Hypotheses 339
10.1 Statistical Hypotheses: General Concepts 339
10.2 Testing a Statistical Hypothesis 341
10.3 The Use of P-Values for Decision Making in Testing Hypotheses 351
Exercises 354
10.4 Single Sample: Tests Concerning a Single Mean 356
10.5 Two Samples: Tests on Two Means 362
10.6 Choice of Sample Size for Testing Means 369
10.7 Graphical Methods for Comparing Means 374
Exercises 376
10.8 One Sample: Test on a Single Proportion 380
10.9 Two Samples: Tests on Two Proportions 383
Exercises 385
10.10 One- and Two-Sample Tests Concerning Variances 386
Exercises 389
10.11 Goodness-of-Fit Test 390
10.12 Test for Independence (Categorical Data) 393
10.13 Test for Homogeneity 396
10.14 Two-Sample Case Study 399
Exercises 402
Review Exercises 404
10.15 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters 406
11 Simple Linear Regression and Correlation 409
11.1 Introduction to Linear Regression 409
11.2 The Simple Linear Regression Model 410
11.3 Least Squares and the Fitted Model 414
Exercises 418
11.4 Properties of the Least Squares Estimators 420
11.5 Inferences Concerning the Regression Coefficients 423
11.6 Prediction 428
Exercises 431
11.7 Choice of a Regression Model 434
11.8 Analysis-of-Variance Approach 434
11.9 Test for Linearity of Regression: Data with Repeated Observations 436
Exercises 441
11.10 Data Plots and Transformations 444
11.11 Simple Linear Regression Case Study 448
11.12 Correlation 450
Exercises 455
Review Exercises 456
11.13 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters 462
12 Multiple Linear Regression and Certain Nonlinear Regression Models 463
12.1 Introduction 463
12.2 Estimating the Coefficients 464
12.3 Linear Regression Model Using Matrices 467
Exercises 470
12.4 Properties of the Least Squares Estimators 473
12.5 Inferences in Multiple Linear Regression 475
Exercises 481
12.6 Choice of a Fitted Model through Hypothesis Testing 482
12.7 Special Case of Orthogonality (Optional) 487
Exercises 491
12.8 Categorical or Indicator Variables 492
Exercises 496
12.9 Sequential Methods for Model Selection 496
12.10 Study of Residuals and Violation of Assumptions (Model Checking) 502
12.11 Cross Validation, Cp, and Other Criteria for Model Selection 507
Exercises 514
12.12 Special Nonlinear Models for Nonideal Conditions 516
Exercises 520
Review Exercises 521
12.13 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters 526
13 One-Factor Experiments: General 527
13.1 Analysis-of-Variance Technique 527
13.2 The Strategy of Experimental Design 528
13.3 One-Way Analysis of Variance: Completely Randomized Design (One-Way ANOVA) 529
13.4 Tests for the Equality of Several Variances 536
Exercises 538
13.5 Single-Degree-of-Freedom Comparisons 540
13.6 Multiple Comparisons 543
Exercises 549
13.7 Comparing a Set of Treatments in Blocks 552
13.8 Randomized Complete Block Designs 553
13.9 Graphical Methods and Model Checking 560
13.10 Data Transformations in Analysis of Variance 563
Exercises 565
13.11 Random Effects Models 567
13.12 Case Study 571
Exercises 573
Review Exercises 575
13.13 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters 579
14 Factorial Experiments (Two or More Factors) 581
14.1 Introduction 581
14.2 Interaction in the Two-Factor Experiment 582
14.3 Two-Factor Analysis of Variance 585
Exercises 595
14.4 Three-Factor Experiments 599
Exercises 606
14.5 Factorial Experiments for Random Effects and Mixed Models 608
Exercises 612
Review Exercises 614
14.6 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters 616
15 2k Factorial Experiments and Fractions 617
15.1 Introduction 617
15.2 The 2k Factorial: Calculation of Effects and Analysis of Variance 618
15.3 Nonreplicated 2k Factorial Experiment 624
Exercises 629
15.4 Factorial Experiments in a Regression Setting 632
15.5 The Orthogonal Design 637
Exercises 645
15.6 Fractional Factorial Experiments 646
15.7 Analysis of Fractional Factorial Experiments 652
Exercises 654
15.8 Higher Fractions and Screening Designs 656
15.9 Construction of Resolution III and IV Designs with 8, 16, and 32 Design Points 657
15.10 Other Two-Level Resolution III Designs; The Plackett-Burman Designs 658
15.11 Introduction to Response Surface Methodology 659
15.12 Robust Parameter Design 663
Exercises 672
Review Exercises 673
15.13 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters 674
16 Nonparametric Statistics 675
16.1 Nonparametric Tests 675
16.2 Signed-Rank Test 680
Exercises 683
16.3 Wilcoxon Rank-Sum Test 685
16.4 Kruskal-Wallis Test 688
Exercises 690
16.5 Runs Test 691
16.6 Tolerance Limits 694
16.7 Rank Correlation Coefficient 694
Exercises 697
Review Exercises 699
17 Statistical Quality Control 701
17.1 Introduction 701
17.2 Nature of the Control Limits 703
17.3 Purposes of the Control Chart 703
17.4 Control Charts for Variables 704
17.5 Control Charts for Attributes 717
17.6 Cusum Control Charts 725
Review Exercises 726
18 Bayesian Statistics 729
18.1 Bayesian Concepts 729
18.2 Bayesian Inferences 730
18.3 Bayes Estimates Using Decision Theory Framework 737
Exercises 738
Bibliography 741
Appendix A: Statistical Tables and Proofs 745
Appendix B: Answers to Odd-Numbered Non-Review Exercises 789
Index 805
A 805
B 805
C 805
D 806
E 806
F 807
G 807
H 807
I 807
J 807
K 807
L 807
M 808
N 808
O 809
P 809
Q 809
R 810
S 810
T 811
U 811
V 811
W 811
X 811