Ross Introduction to Probability and Statistics for Engineers and Scientists

4. Auflage 2009
ISBN: 978-0-08-091937-9
Verlag: Elsevier Science & Techn.
Format: EPUB
Kopierschutz: 6 - ePub Watermark

E-Book, Englisch, 680 Seiten

This updated text provides a superior introduction to applied probability and statistics for engineering or science majors. Ross emphasizes the manner in which probability yields insight into statistical problems; ultimately resulting in an intuitive understanding of the statistical procedures most often used by practicing engineers and scientists. Real data sets are incorporated in a wide variety of exercises and examples throughout the book, and this emphasis on data motivates the probability coverage.
As with the previous editions, Ross' text has remendously clear exposition, plus real-data examples and exercises throughout the text. Numerous exercises, examples, and applications
apply probability theory to everyday statistical problems and situations.
Also Available: Student Solutions Manual for Introduction to Probability and Statistics for Engineers and Scientists, 4e ISBN:9780123705280
New to the 4th Edition:
- New Chapter on Simulation, Bootstrap Statistical Methods, and Permutation Tests
- 20% New Updated problem sets and applications, that demonstrate updated applications to engineering as well as biological, physical and computer science
- New Real data examples that use significant real data from actual studies across life science, engineering, computing and business
- New End of Chapter review material that emphasizes key ideas as well as the risks associated with practical application of the material

Sheldon M. Ross is a professor in the Department of Industrial Engineering and Operations Research at the University of Southern California. He received his Ph.D. in statistics at Stanford University in 1968. He has published many technical articles and textbooks in the areas of statistics and applied probability. Among his texts are A First Course in Probability, Introduction to Probability Models, Stochastic Processes, and Introductory Statistics. Professor Ross is the founding and continuing editor of the journal Probability in the Engineering and Informational Sciences. He is a Fellow of the Institute of Mathematical Statistics, and a recipient of the Humboldt US Senior Scientist Award.

Autoren/Hrsg.

Ross, Sheldon M.

Inhaltsverzeichnis

1;Front Cover;1
2;Introduction to Probability and Statistics for Engineers and Scientists;4
3;Copyright Page;5
4;Table of Contents;8
5;Preface;14
6;Chapter 1. Introduction to Statistics;18
6.1;1.1 Introduction;18
6.2;1.2 Data Collection and Descriptive Statistics;18
6.3;1.3 Inferential Statistics and Probability Models;19
6.4;1.4 Populations and Samples;20
6.5;1.5 A Brief History of Statistics;20
6.6;Problems;24
7;Chapter 2. Descriptive Statistics;26
7.1;2.1 Introduction;26
7.2;2.2 Describing Data Sets;26
7.2.1;2.2.1 Frequency Tables and Graphs;27
7.2.2;2.2.2 Relative Frequency Tables and Graphs;27
7.2.3;2.2.3 Grouped Data, Histograms, Ogives, and Stem and Leaf Plots;31
7.3;2.3 Summarizing Data Sets;34
7.3.1;2.3.1 Sample Mean, Sample Median, and Sample Mode;34
7.3.2;2.3.2 Sample Variance and Sample Standard Deviation;39
7.3.3;2.3.3 Sample Percentiles and Box Plots;41
7.4;2.4 Chebyshev’s Inequality;44
7.5;2.5 Normal Data Sets;48
7.6;2.6 Paired Data Sets and the Sample Correlation Coefficient;50
7.7;Problems;58
8;Chapter 3. Elements of Probability;72
8.1;3.1 Introduction;72
8.2;3.2 Sample Space and Events;73
8.3;3.3 Venn Diagrams and the Algebra of Events;75
8.4;3.4 Axioms of Probability;76
8.5;3.5 Sample Spaces Having Equally Likely Outcomes;78
8.6;3.6 Conditional Probability;84
8.7;3.7 Bayes’ Formula;87
8.8;3.8 Independent Events;93
8.9;Problems;97
9;Chapter 4. Random Variables and Expectation;106
9.1;4.1 Random Variables;106
9.2;4.2 Types of Random Variables;109
9.3;4.3 Jointly Distributed Random Variables;112
9.3.1;4.3.1 Independent Random Variables;118
9.3.2;*4.3.2 Conditional Distributions;122
9.4;4.4 Expectation;124
9.5;4.5 Properties of the Expected Value;128
9.5.1;4.5.1 Expected Value of Sums of Random Variables;132
9.6;4.6 Variance;135
9.7;4.7 Covariance and Variance of Sums of Random Variables;138
9.8;4.8 Moment Generating Functions;142
9.9;4.9 Chebyshev’s Inequality and the Weak Law of Large Numbers;144
9.10;Problems;147
10;Chapter 5. Special Random Variables;158
10.1;5.1 The Bernoulli and Binomial Random Variables;158
10.1.1;5.1.1 Computing the Binomial Distribution Function;164
10.2;5.2 The Poisson Random Variable;165
10.2.1;5.2.1 Computing the Poisson Distribution Function;172
10.3;5.3 The Hypergeometric Random Variable;173
10.4;5.4 The Uniform Random Variable;177
10.5;5.5 Normal Random Variables;185
10.6;5.6 Exponential Random Variables;193
10.6.1;*5.6.1 The Poisson Process;197
10.7;*5.7 The Gamma Distribution;200
10.8;5.8 Distributions Arising from the Normal;203
10.8.1;5.8.1 The Chi-Square Distribution;203
10.8.2;5.8.2 The t-Distribution;207
10.8.3;5.8.3 The F-Distribution;209
10.9;*5.9 The Logistics Distribution;210
10.10;Problems;212
11;Chapter 6. Distributions of Sampling Statistics;220
11.1;6.1 Introduction;220
11.2;6.2 The Sample Mean;221
11.3;6.3 The Central Limit Theorem;223
11.3.1;6.3.1 Approximate Distribution of the Sample Mean;229
11.3.2;6.3.2 How Large a Sample Is Needed?;231
11.4;6.4 The Sample Variance;232
11.5;6.5 Sampling Distributions from a Normal Population;233
11.5.1;6.5.1 Distribution of the Sample Mean;234
11.5.2;6.5.2 Joint Distribution of X and S2;234
11.6;6.6 Sampling from a Finite Population;236
11.7;Problems;240
12;Chapter 7. Parameter Estimation;248
12.1;7.1 Introduction;248
12.2;7.2 Maximum Likelihood Estimators;249
12.2.1;*7.2.1 Estimating Life Distributions;257
12.3;7.3 Interval Estimates;259
12.3.1;7.3.1 Confidence Interval for a Normal Mean When the Variance Is Unknown;265
12.3.2;7.3.2 Confidence Intervals for the Variance of a Normal Distribution;270
12.4;7.4 Estimating the Difference in Means of Two Normal Populations;272
12.5;7.5 Approximate Confidence Interval for the Mean of a Bernoulli Random Variable;279
12.6;*7.6 Confidence Interval of the Mean of the Exponential Distribution;284
12.7;*7.7 Evaluating a Point Estimator;285
12.8;*7.8 The Bayes Estimator;291
12.9;Problems;296
13;Chapter 8. Hypothesis Testing;310
13.1;8.1 Introduction;310
13.2;8.2 Significance Levels;311
13.3;8.3 Tests Concerning the Mean of a Normal Population;312
13.3.1;8.3.1 Case of Known Variance;312
13.3.2;8.3.2 Case of Unknown Variance: The t-Test;324
13.4;8.4 Testing the Equality of Means of Two Normal Populations;331
13.4.1;8.4.1 Case of Known Variances;331
13.4.2;8.4.2 Case of Unknown Variances;333
13.4.3;8.4.3 Case of Unknown and Unequal Variances;337
13.4.4;8.4.4 The Paired t-Test;338
13.5;8.5 Hypothesis Tests Concerning the Variance of a Normal Population;340
13.6;8.5.1 Testing for the Equality of Variances of Two Normal Populations;341
13.7;8.6 Hypothesis Tests in Bernoulli Populations;342
13.7.1;8.6.1 Testing the Equality of Parameters in Two Bernoulli Populations;346
13.8;8.7 Tests Concerning the Mean of a Poisson Distribution;349
13.8.1;8.7.1 Testing the Relationship Between Two Poisson Parameters;350
13.9;Problems;353
14;Chapter 9. Regression;370
14.1;9.1 Introduction;370
14.2;9.2 Least Squares Estimators of the Regression Parameters;372
14.3;9.3 Distribution of the Estimators;374
14.4;9.4 Statistical Inferences about the Regression Parameters;380
14.4.1;9.4.1 Inferences Concerning ß;381
14.4.2;9.4.2 Inferences Concerning a;389
14.4.3;9.4.3 Inferences Concerning the Mean Response a+ßx0;390
14.4.4;9.4.4 Prediction Interval of a Future Response;392
14.4.5;9.4.5 Summary of Distributional Results;394
14.5;9.5 The Coefficient of Determination and the Sample Correlation Coefficient;395
14.6;9.6 Analysis of Residuals: Assessing the Model;397
14.7;9.7 Transforming to Linearity;400
14.8;9.8 Weighted Least Squares;403
14.9;9.9 Polynomial Regression;410
14.10;*9.10 Multiple Linear Regression;413
14.10.1;9.10.1 Predicting Future Responses;424
14.11;9.11 Logistic Regression Models for Binary Output Data;429
14.12;Problems;432
15;Chapter 10. Analysis of Variance;458
15.1;10.1 Introduction;458
15.2;10.2 An Overview;459
15.3;10.3 One-Way Analysis of Variance;461
15.3.1;10.3.1 Multiple Comparisons of Sample Means;469
15.3.2;10.3.2 One-Way Analysis of Variance with Unequal Sample Sizes;471
15.4;10.4 Two-Factor Analysis of Variance:Introduction and Parameter Estimation;473
15.5;10.5 Two-Factor Analysis of Variance:Testing Hypotheses;477
15.6;10.6 Two-Way Analysis of Variance with Interaction;482
15.7;Problems;490
16;Chapter 11. Goodness of Fit Tests and Categorical Data Analysis;502
16.1;11.1 Introduction;502
16.2;11.2 Goodness of Fit Tests When All Parameters are Specified;503
16.2.1;11.2.1 Determining the Critical Region by Simulation;509
16.3;11.3 Goodness of Fit Tests When Some Parameters are Unspecified;512
16.4;11.4 Tests of Independence in Contingency Tables;514
16.5;11.5 Tests of Independence in Contingency Tables Having Fixed Marginal Totals;518
16.6;*11.6 The Kolmogorov–Smirnov Goodness of Fit Test for Continuous Data;523
16.7;Problems;527
17;Chapter 12. Nonparametric Hypothesis Tests;534
17.1;12.1 Introduction;534
17.2;12.2 The Sign Test;534
17.3;12.3 The Signed Rank Test;538
17.4;12.4 The Two-Sample Problem;544
17.4.1;*12.4.1 The Classical Approximation and Simulation;548
17.5;12.5 The Runs Test for Randomness;552
17.6;Problems;556
18;Chapter 13. Quality Control;564
18.1;13.1 Introduction;564
18.2;13.2 Control Charts for Average Values:The X-Control Chart;565
18.2.1;13.2.1 Case of Unknown µ and s;568
18.3;13.3 S-Control Charts;573
18.4;13.4 Control Charts for the Fraction Defective;576
18.5;13.5 Control Charts for Number of Defects;578
18.6;13.6 Other Control Charts for Detecting Changes in the Population Mean;582
18.6.1;13.6.1 Moving-Average Control Charts;582
18.6.2;13.6.2 Exponentially Weighted Moving-Average Control Charts;584
18.6.2.1;13.6.3 Cumulative Sum Control Charts;590
18.7;Problems;592
19;Chapter 14*. Life Testing;600
19.1;14.1 Introduction;600
19.2;14.2 Hazard Rate Functions;600
19.3;14.3 The Exponential Distribution in Life Testing;603
19.3.1;14.3.1 Simultaneous Testing — Stopping at the rth Failure;603
19.3.2;14.3.2 Sequential Testing;609
19.3.3;14.3.3 Simultaneous Testing — Stopping by a Fixed Time;613
19.3.4;14.3.4 The Bayesian Approach;615
19.4;14.4 A Two-Sample Problem;617
19.5;14.5 The Weibull Distribution in Life Testing;619
19.5.1;14.5.1 Parameter Estimation by Least Squares;621
19.6;Problems;623
20;Chapter 15. Simulation, Bootstrap Statistical Methods, and Permutation Tests;630
20.1;15.1 Introduction;630
20.2;15.2 Random Numbers;631
20.2.1;15.2.1 The Monte Carlo Simulation Approach;633
20.3;15.3 The Bootstrap Method;634
20.4;15.4 Permutation Tests;641
20.4.1;15.4.1 Normal Approximations in Permutation Tests;644
20.4.2;15.4.2 Two-Sample Permutation Tests;648
20.5;15.5 Generating Discrete Random Variables;649
20.6;15.6 Generating Continuous Random Variables;651
20.6.1;15.6.1 Generating a Normal Random Variable;653
20.7;15.7 Determining the Number of Simulation Runs in a Monte Carlo Study;654
20.8;Problems;655
21;Appendix of Tables;658
22;Index;664