Miller & Freund's Probability and Statistics for Engineers, Global Edition

(2017)

Abstract

For an introductory, one or two semester, or sophomore-junior level course in Probability and Statistics or Applied Statistics for engineering, physical science, and mathematics students.

An Applications-Focused Introduction to Probability and Statistics

Miller & Freund's Probability and Statistics for Engineers is rich in exercises and examples, and explores both elementary probability and basic statistics, with an emphasis on engineering and science applications. Much of the data has been collected from the author's own consulting experience and from discussions with scientists and engineers about the use of statistics in their fields. In later chapters, the text emphasizes designed experiments, especially two-level factorial design. The Ninth Edition includes several new datasets and examples showing application of statistics in scientific investigations, familiarizing students with the latest methods, and readying them to become real-world engineers and scientists.

Section Title Page Action Price
Cover Cover
Title Page 1
Contents 3
Preface 7
Chapter 1 Introduction 11
1.1 Why Study Statistics? 11
1.2 Modern Statistics 12
1.3 Statistics and Engineering 12
1.4 The Role of the Scientist and Engineer in Quality Improvement 13
1.5 A Case Study: Visually Inspecting Data to Improve Product Quality 13
1.6 Two Basic Concepts—Population and Sample 15
Review Exercises 20
Key Terms 21
Chapter 2 Organization and Description of Data 22
2.1 Pareto Diagrams and Dot Diagrams 22
2.2 Frequency Distributions 24
2.3 Graphs of Frequency Distributions 27
2.4 Stem-and-Leaf Displays 31
2.5 Descriptive Measures 34
2.6 Quartiles and Percentiles 39
2.7 The Calculation of x and s 44
2.8 A Case Study: Problems with Aggregating Data 49
Review Exercises 52
Key Terms 54
Chapter 3 Probability 56
3.1 Sample Spaces and Events 56
3.2 Counting 60
3.3 Probability 67
3.4 The Axioms of Probability 69
3.5 Some Elementary Theorems 72
3.6 Conditional Probability 78
3.7 Bayes’ Theorem 84
Review Exercises 91
Key Terms 93
Chapter 4 Probability Distributions 94
4.1 Random Variables 94
4.2 The Binomial Distribution 98
4.3 The Hypergeometric Distribution 103
4.4 The Mean and the Variance of a Probability Distribution 107
4.5 Chebyshev’s Theorem 114
4.6 The Poisson Distribution and Rare Events 118
4.7 Poisson Processes 122
4.8 The Geometric and Negative Binomial Distribution 124
4.9 The Multinomial Distribution 127
4.10 Simulation 128
Review Exercises 132
Key Terms 133
Chapter 5 Probability Densities 134
5.1 Continuous Random Variables 134
5.2 The Normal Distribution 140
5.3 The Normal Approximation to the Binomial Distribution 148
5.4 Other Probability Densities 151
5.5 The Uniform Distribution 151
5.6 The Log-Normal Distribution 152
5.7 The Gamma Distribution 155
5.8 The Beta Distribution 157
5.9 TheWeibull Distribution 158
5.10 Joint Distributions—Discrete and Continuous 161
5.11 Moment Generating Functions 174
5.12 Checking If the Data Are Normal 180
5.13 Transforming Observations to Near Normality 182
5.14 Simulation 184
Review Exercises 188
Key Terms 190
Chapter 6 Sampling Distributions 193
6.1 Populations and Samples 193
6.2 The Sampling Distribution of the Mean (σ known) 197
6.3 The Sampling Distribution of the Mean (σ unknown) 205
6.4 The Sampling Distribution of the Variance 207
6.5 Representations of the Normal Theory Distributions 210
6.6 The Moment Generating Function Method to Obtain Distributions 213
6.7 Transformation Methods to Obtain Distributions 215
Review Exercises 221
Key Terms 222
Chapter 7 Inferences Concerning a Mean 223
7.1 Statistical Approaches to Making Generalizations 223
7.2 Point Estimation 224
7.3 Interval Estimation 229
7.4 Maximum Likelihood Estimation 236
7.5 Tests of Hypotheses 242
7.6 Null Hypotheses and Tests of Hypotheses 244
7.7 Hypotheses Concerning One Mean 249
7.8 The Relation between Tests and Confidence Intervals 256
7.9 Power, Sample Size, and Operating Characteristic Curves 257
Review Exercises 263
Key Terms 265
Chapter 8 Comparing Two Treatments 266
8.1 Experimental Designs for Comparing Two Treatments 266
8.2 Comparisons—Two Independent Large Samples 267
8.3 Comparisons—Two Independent Small Samples 272
8.4 Matched Pairs Comparisons 280
8.5 Design Issues—Randomization and Pairing 285
Review Exercises 287
Key Terms 288
Chapter 9 Inferences Concerning Variances 290
9.1 The Estimation of Variances 290
9.2 Hypotheses Concerning One Variance 293
9.3 Hypotheses Concerning Two Variances 295
Review Exercises 299
Key Terms 300
Chapter 10 Inferences Concerning Proportions 301
10.1 Estimation of Proportions 301
10.2 Hypotheses Concerning One Proportion 308
10.3 Hypotheses Concerning Several Proportions 310
10.4 Analysis of r x c Tables 318
10.5 Goodness of Fit 322
Review Exercises 325
Key Terms 326
Chapter 11 Regression Analysis 327
11.1 The Method of Least Squares 327
11.2 Inferences Based on the Least Squares Estimators 336
11.3 Curvilinear Regression 350
11.4 Multiple Regression 356
11.5 Checking the Adequacy of the Model 361
11.6 Correlation 366
11.7 Multiple Linear Regression (Matrix Notation) 377
Review Exercises 382
Key Terms 385
Chapter 12 Analysis of Variance 386
12.1 Some General Principles 386
12.2 Completely Randomized Designs 389
12.3 Randomized-Block Designs 402
12.4 Multiple Comparisons 410
12.5 Analysis of Covariance 415
Review Exercises 422
Key Terms 424
Chapter 13 Factorial Experimentation 425
13.1 Two-Factor Experiments 425
13.2 Multifactor Experiments 432
13.3 The Graphic Presentation of 22 and 23 Experiments 441
13.4 Response Surface Analysis 456
Review Exercises 459
Key Terms 463
Chapter 14 Nonparametric Tests 464
14.1 Introduction 464
14.2 The Sign Test 464
14.3 Rank-Sum Tests 466
14.4 Correlation Based on Ranks 469
14.5 Tests of Randomness 472
14.6 The Kolmogorov-Smirnov and Anderson-Darling Tests 475
Review Exercises 478
Key Terms 479
Chapter 15 The Statistical Content of Quality-Improvement Programs 480
15.1 Quality-Improvement Programs 480
15.2 Starting a Quality-Improvement Program 482
15.3 Experimental Designs for Quality 484
15.4 Quality Control 486
15.5 Control Charts for Measurements 488
15.6 Control Charts for Attributes 493
15.7 Tolerance Limits 499
Review Exercises 501
Key Terms 503
Chapter 16 Application to Reliability and Life Testing 504
16.1 Reliability 504
16.2 Failure-Time Distribution 506
16.3 The Exponential Model in Life Testing 510
16.4 The Weibull Model in Life Testing 513
Review Exercises 518
Key Terms 519
Appendix A Bibliography 521
Appendix B Statistical Tables 522
Appendix C Using the R Software Program 529
Introduction to R 529
Entering Data 529
Arithmetic Operations 530
Descriptive Statistics 530
Probability Distributions 531
Normal Probability Calculations 531
Sampling Distributions 531
Confidence Intervals and Tests of Means 532
Regression 532
One-Way Analysis of Variance (ANOVA) 533
Appendix D Answers to Odd-Numbered Exercises 534
Index 541
A 541
B 541
C 541
D 541
E 542
F 542
G 542
H 542
I 543
J 543
K 543
L 543
M 543
N 544
O 544
P 544
Q 544
R 544
S 545
T 546
U 546
V 546
W 546
X 546
Z 546