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Introduction to Probability and Statistics for Engineers and Scientists. Edition No. 6

  • ID: 5029521
  • Book
  • November 2020
  • Region: Global
  • 700 Pages
  • Elsevier Science and Technology
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Introduction to Probability and Statistics for Engineers and Scientists, Sixth Edition, uniquely emphasizes how probability informs statistical problems, thus helping readers develop an intuitive understanding of the statistical procedures commonly used by practicing engineers and scientists. Utilizing real data from actual studies across life science, engineering, computing and business, this useful introduction supports reader comprehension through a wide variety of exercises and examples. End-of-chapter reviews of materials highlight key ideas, also discussing the risks associated with the practical application of each material. In the new edition, coverage includes information on Big Data and the use of R.

This book is intended for upper level undergraduate and graduate students taking a probability and statistics course in engineering programs as well as those across the biological, physical and computer science departments. It is also appropriate for scientists, engineers and other professionals seeking a reference of foundational content and application to these fields.

  • Provides the author's uniquely accessible and engaging approach as tailored for the needs of Engineers and Scientists
  • Features examples that use significant real data from actual studies across life science, engineering, computing and business
  • Includes new coverage to support the use of R
  • Offers new chapters on big data techniques
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1. Introduction to Statistics 2. Descriptive Statistics 3. Elements of Probability 4. Random Variables and Expectation 5. Special Random Variables 6. Distributions of Sampling Statistics 7. Parameter Estimation 8. Hypothesis Testing 9. Regression 10. Analysis of Variance 11. Goodness of Fit Tests and Categorical Data Analysis 12. Non parametric Hypothesis Tests 13. Quality Control 14. LifeTesting 15. Simulation, Bootstrap Statistical Methods, and Permutation Tests

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Ross, Sheldon M.
Dr. Sheldon M. Ross is a professor in the Department of Industrial and Systems Engineering at the University of Southern California. He received his PhD 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, a Fellow of INFORMS, and a recipient of the Humboldt US Senior Scientist Award.
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