Ross Introduction to Probability and Statistics for Engineers and Scientists

3. Auflage 2004
ISBN: 978-0-08-047031-3
Verlag: Elsevier Science & Techn.
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 640 Seiten, Web PDF

Introduction to Probability and Statistics for Engineers and Scientists, Third Edition, provides an introduction to applied probability and statistics for engineering or science majors . This updated text 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. The Third Edition includes new exercises, examples, homework problems, updated statistical material, and more. New exercises and data examples include: the one-sided Chebyshev inequality for data; logistics distribution and logistic regression; estimation and testing in proofreader problems; and product form estimates of life distributions. Real data sets are incorporated in a wide variety of exercises and examples throughout the book, and the enclosed CD-ROM includes unique, easy-to-use software that automates the required computations. This book is intended primarily for undergraduates in engineering and the sciences, and would be of particular interest to students in Industrial Engineering, Operations Research, Statistics, Mathematics, Computer Science, Electrical Engineering, Civil Engineering, Chemical Engineering, and Quantitative Business. It could also be of value in a graduate introductory course in probability and statistics.New in this edition:
* New exercises and data examples including:
- The One-sided Chebyshev Inequality for Data
- The Logistics Distribution and Logistic Regression
- Estimation and Testing in proofreader problems
- Product Form Estimates of Life Distributions
- Observational Studies
* Updated statistical material
* New, contemporary applications
Hallmark features:
* Reflects Sheldon Ross's masterfully clear exposition
* Contains numerous examples, exercises, and homework problems
* Unique, easy-to-use software automates required computations
* Applies probability theory to everyday statistical problems and situations
* Careful development of probability, modeling, and statistical procedures leads to intuitive understanding
* Instructor's Solutions Manual is available to adopters

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.

Zielgruppe

Academic/professional/technical: Undergraduate. Academic/professional/technical: Postgraduate. Academic/professional/technical: Research and professional

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;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
7;Chapter 2. Descriptive Statistics;26
7.1;2.1 Introduction;26
7.2;2.2 Describing Data Sets;26
7.3;2.3 Summarizing Data Sets;34
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
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
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
9.4;4.4 Expectation;124
9.5;4.5 Properties of the Expected Value;128
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;143
9.9;4.9 Chebyshev's Inequality and the Weak Law of Large Numbers;144
10;Chapter 5. Special Random Variables;158
10.1;5.1 The Bernoulli and Binomial Random Variables;158
10.2;5.2 The Poisson Random Variable;165
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;192
10.7;5.7 The Gamma Distribution;199
10.8;5.8 Distributions Arising From the Normal;202
10.9;5.9 The Logistics Distribution;209
11;Chapter 6. Distributions of Sampling Statistics;218
11.1;6.1 Introduction;218
11.2;6.2 The Sample Mean;219
11.3;6.3 The Central Limit Theorem;221
11.4;6.4 The Sample Variance;230
11.5;6.5 Sampling Distributions From a Normal Population;231
11.6;6.6 Sampling From a Finite Population;234
12;Chapter 7. Parameter Estimation;246
12.1;7.1 Introduction;246
12.2;7.2 Maximum Likelihood Estimators;247
12.3;7.3 Interval Estimates;257
12.4;7.4 Estimating the Difference in Means of Two Normal Populations;270
12.5;7.5 Approximate Confidence Interval for the Mean of a Bernoulli Random Variable;277
12.6;7.6 Confidence Interval of the Mean of the Exponential Distribution;282
12.7;7.7 Evaluating a Point Estimator;283
12.8;7.8 The Bayes Estimator;289
13;Chapter 8. Hypothesis Testing;308
13.1;8.1 Introduction;308
13.2;8.2 Significance Levels;309
13.3;8.3 Tests Concerning the Mean of a Normal Population;310
13.4;8.4 Testing the Equality of Means of Two Normal Populations;329
13.5;8.5 Hypothesis Tests Concerning the Variance of a Normal Population;338
13.6;8.6 Hypothesis Tests in Bernoulli Populations;340
13.7;8.7 Tests Concerning the Mean of a Poisson Distribution;347
14;Chapter 9. Regression;368
14.1;9.1 Introduction;368
14.2;9.2 Least Squares Estimators of the Regression Parameters;370
14.3;9.3 Distribution of the Estimators;372
14.4;9.4 Statistical Inferences About the Regression Parameters;378
14.5;9.5 The Coefficient of Determination and the Sample Correlation Coefficient;393
14.6;9.6 Analysis of Residuals: Assessing the Model;395
14.7;9.7 Transforming to Linearity;398
14.8;9.8 Weighted Least Squares;401
14.9;9.9 Polynomial Regression;408
14.10;9.10 Multiple Linear Regression;411
14.11;9.11 Logistic Regression Models for Binary Output Data;427
15;Chapter 10. Analysis of Variance;456
15.1;10.1 Introduction;456
15.2;10.2 An Overview;457
15.3;10.3 One-Way Analysis of Variance;459
15.4;10.4 Two-Factor Analysis of Variance: Introduction and Parameter Estimation;471
15.5;10.5 Two-Factor Analysis of Variance: Testing Hypotheses;475
15.6;10.6 Two-Way Analysis of Variance With Interaction;480
16;Chapter 11. Goodness of Fit Tests and Categorical Data Analysis;500
16.1;11.1 Introduction;500
16.2;11.2 Goodness of Fit Tests When All Parameters are Specified;501
16.3;11.3 Goodness of Fit Tests When Some Parameters are Unspecified;510
16.4;11.4 Tests of Independence in Contingency Tables;512
16.5;11.5 Tests of Independence in Contingency Tables Having Fixed Marginal Totals;516
16.6;11.6 The Kolmogorov–smirnov Goodness of Fit Test for Continuous Data;521
17;Chapter 12. Nonparametric Hypothesis Tests;532
17.1;12.1 Introduction;532
17.2;12.2 The Sign Test;532
17.3;12.3 The Signed Rank Test;536
17.4;12.4 The Two-Sample Problem;542
17.5;12.5 The Runs Test for Randomness;550
18;Chapter 13. Quality Control;562
18.1;13.1 Introduction;562
18.2;13.2 Control Charts for Average Values: The X -Control Chart;563
18.3;13.3 S-Control Charts;571
18.4;13.4 Control Charts for the Fraction Defective;574
18.5;13.5 Control Charts for Number of Defects;576
18.6;13.6 Other Control Charts for Detecting Changes in the Population Mean;580
19;Chapter 14. Life Testing;598
19.1;14.1 Introduction;598
19.2;14.2 Hazard Rate Functions;598
19.3;14.3 The Exponential Distribution in Life Testing;601
19.4;14.4 A Two-Sample Problem;615
19.5;14.5 The Weibull Distribution in Life Testing;617
20;Appendix of Tables;628
21;Index;634