Probability Theory And Statistics With Real World Applications
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| Author | : Malempati M. Rao |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 537 |
| Release | : 2006-06-03 |
| Genre | : Mathematics |
| ISBN | : 0387277315 |
This is a revised and expanded edition of a successful graduate and reference text. The book is designed for a standard graduate course on probability theory, including some important applications. The new edition offers a detailed treatment of the core area of probability, and both structural and limit results are presented in detail. Compared to the first edition, the material and presentation are better highlighted; each chapter is improved and updated.
| Author | : Peter Zörnig |
| Publisher | : Walter de Gruyter GmbH & Co KG |
| Total Pages | : 372 |
| Release | : 2024-09-02 |
| Genre | : Mathematics |
| ISBN | : 3111332276 |
The idea of the book is to present a text that is useful for both students of quantitative sciences and practitioners who work with univariate or multivariate probabilistic models. Since the text should also be suitable for self-study, excessive formalism is avoided though mathematical rigor is retained. A deeper insight into the topics is provided by detailed examples and illustrations. The book covers the standard content of a course in probability and statistics. However, the second edition includes two new chapters about distribution theory and exploratory data analysis. The first-mentioned chapter certainly goes beyond the standard material. It is presented to reflect the growing practical importance of developing new distributions. The second new chapter studies intensively one- and bidimensional concepts like assymetry, kurtosis, correlation and determination coefficients. In particular, examples are intended to enable the reader to take a critical look at the appropriateness of the geometrically motivated concepts.
| Author | : Mary C. Meyer |
| Publisher | : SIAM |
| Total Pages | : 720 |
| Release | : 2019-06-24 |
| Genre | : Mathematics |
| ISBN | : 1611975786 |
This book develops the theory of probability and mathematical statistics with the goal of analyzing real-world data. Throughout the text, the R package is used to compute probabilities, check analytically computed answers, simulate probability distributions, illustrate answers with appropriate graphics, and help students develop intuition surrounding probability and statistics. Examples, demonstrations, and exercises in the R programming language serve to reinforce ideas and facilitate understanding and confidence. The books Chapter Highlights provide a summary of key concepts, while the examples utilizing R within the chapters are instructive and practical. Exercises that focus on real-world applications without sacrificing mathematical rigor are included, along with more than 200 figures that help clarify both concepts and applications. In addition, the book features two helpful appendices: annotated solutions to 700 exercises and a Review of Useful Math. Written for use in applied masters classes, Probability and Mathematical Statistics: Theory, Applications, and Practice in R is also suitable for advanced undergraduates and for self-study by applied mathematicians and statisticians and qualitatively inclined engineers and scientists.
| Author | : Michael J. Evans |
| Publisher | : Macmillan |
| Total Pages | : 704 |
| Release | : 2004 |
| Genre | : Mathematics |
| ISBN | : 9780716747420 |
Unlike traditional introductory math/stat textbooks, Probability and Statistics: The Science of Uncertainty brings a modern flavor based on incorporating the computer to the course and an integrated approach to inference. From the start the book integrates simulations into its theoretical coverage, and emphasizes the use of computer-powered computation throughout.* Math and science majors with just one year of calculus can use this text and experience a refreshing blend of applications and theory that goes beyond merely mastering the technicalities. They'll get a thorough grounding in probability theory, and go beyond that to the theory of statistical inference and its applications. An integrated approach to inference is presented that includes the frequency approach as well as Bayesian methodology. Bayesian inference is developed as a logical extension of likelihood methods. A separate chapter is devoted to the important topic of model checking and this is applied in the context of the standard applied statistical techniques. Examples of data analyses using real-world data are presented throughout the text. A final chapter introduces a number of the most important stochastic process models using elementary methods. *Note: An appendix in the book contains Minitab code for more involved computations. The code can be used by students as templates for their own calculations. If a software package like Minitab is used with the course then no programming is required by the students.
| Author | : Narayanaswamy Balakrishnan |
| Publisher | : John Wiley & Sons |
| Total Pages | : 548 |
| Release | : 2021-11-24 |
| Genre | : Mathematics |
| ISBN | : 1118548558 |
INTRODUCTION TO PROBABILITY Discover practical models and real-world applications of multivariate models useful in engineering, business, and related disciplines In Introduction to Probability: Multivariate Models and Applications, a team of distinguished researchers delivers a comprehensive exploration of the concepts, methods, and results in multivariate distributions and models. Intended for use in a second course in probability, the material is largely self-contained, with some knowledge of basic probability theory and univariate distributions as the only prerequisite. This textbook is intended as the sequel to Introduction to Probability: Models and Applications. Each chapter begins with a brief historical account of some of the pioneers in probability who made significant contributions to the field. It goes on to describe and explain a critical concept or method in multivariate models and closes with two collections of exercises designed to test basic and advanced understanding of the theory. A wide range of topics are covered, including joint distributions for two or more random variables, independence of two or more variables, transformations of variables, covariance and correlation, a presentation of the most important multivariate distributions, generating functions and limit theorems. This important text: Includes classroom-tested problems and solutions to probability exercises Highlights real-world exercises designed to make clear the concepts presented Uses Mathematica software to illustrate the text’s computer exercises Features applications representing worldwide situations and processes Offers two types of self-assessment exercises at the end of each chapter, so that students may review the material in that chapter and monitor their progress Perfect for students majoring in statistics, engineering, business, psychology, operations research and mathematics taking a second course in probability, Introduction to Probability: Multivariate Models and Applications is also an indispensable resource for anyone who is required to use multivariate distributions to model the uncertainty associated with random phenomena.
| Author | : Bhisham C. Gupta |
| Publisher | : John Wiley & Sons |
| Total Pages | : 896 |
| Release | : 2013-04-29 |
| Genre | : Mathematics |
| ISBN | : 1118464044 |
Introducing the tools of statistics and probability from the ground up An understanding of statistical tools is essential for engineers and scientists who often need to deal with data analysis over the course of their work. Statistics and Probability with Applications for Engineers and Scientists walks readers through a wide range of popular statistical techniques, explaining step-by-step how to generate, analyze, and interpret data for diverse applications in engineering and the natural sciences. Unique among books of this kind, Statistics and Probability with Applications for Engineers and Scientists covers descriptive statistics first, then goes on to discuss the fundamentals of probability theory. Along with case studies, examples, and real-world data sets, the book incorporates clear instructions on how to use the statistical packages Minitab® and Microsoft® Office Excel® to analyze various data sets. The book also features: • Detailed discussions on sampling distributions, statistical estimation of population parameters, hypothesis testing, reliability theory, statistical quality control including Phase I and Phase II control charts, and process capability indices • A clear presentation of nonparametric methods and simple and multiple linear regression methods, as well as a brief discussion on logistic regression method • Comprehensive guidance on the design of experiments, including randomized block designs, one- and two-way layout designs, Latin square designs, random effects and mixed effects models, factorial and fractional factorial designs, and response surface methodology • A companion website containing data sets for Minitab and Microsoft Office Excel, as well as JMP ® routines and results Assuming no background in probability and statistics, Statistics and Probability with Applications for Engineers and Scientists features a unique, yet tried-and-true, approach that is ideal for all undergraduate students as well as statistical practitioners who analyze and illustrate real-world data in engineering and the natural sciences.
| Author | : Henk Tijms |
| Publisher | : Cambridge University Press |
| Total Pages | : 407 |
| Release | : 2007-07-26 |
| Genre | : Mathematics |
| ISBN | : 1139465457 |
In this fully revised second edition of Understanding Probability, the reader can learn about the world of probability in an informal way. The author demystifies the law of large numbers, betting systems, random walks, the bootstrap, rare events, the central limit theorem, the Bayesian approach and more. This second edition has wider coverage, more explanations and examples and exercises, and a new chapter introducing Markov chains, making it a great choice for a first probability course. But its easy-going style makes it just as valuable if you want to learn about the subject on your own, and high school algebra is really all the mathematical background you need.
| Author | : I︠U︡riĭ Anatolʹevich Rozanov |
| Publisher | : Springer |
| Total Pages | : 280 |
| Release | : 1995-10-31 |
| Genre | : Mathematics |
| ISBN | : |
The second part (Chapters 4-6) provides a foundation of stochastic analysis, gives information on basic models of random processes and tools to study them. Here a certain familiarity with elements of functional analysis is necessary. Important material is presented in the form of examples to keep readers involved. Audience: This is a concise textbook for a graduate level course, with carefully selected topics representing the most important areas of modern probability, random processes and statistics.
| Author | : Bert E. Fristedt |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 775 |
| Release | : 2013-11-21 |
| Genre | : Mathematics |
| ISBN | : 1489928375 |
Students and teachers of mathematics and related fields will find this book a comprehensive and modern approach to probability theory, providing the background and techniques to go from the beginning graduate level to the point of specialization in research areas of current interest. The book is designed for a two- or three-semester course, assuming only courses in undergraduate real analysis or rigorous advanced calculus, and some elementary linear algebra. A variety of applications—Bayesian statistics, financial mathematics, information theory, tomography, and signal processing—appear as threads to both enhance the understanding of the relevant mathematics and motivate students whose main interests are outside of pure areas.
| Author | : Larry Rabinowitz |
| Publisher | : CRC Press |
| Total Pages | : 208 |
| Release | : 2016-11-03 |
| Genre | : Mathematics |
| ISBN | : 1498771335 |
Elementary Probability with Applications, Second Edition shows students how probability has practical uses in many different fields, such as business, politics, and sports. In the book, students learn about probability concepts from real-world examples rather than theory. The text explains how probability models with underlying assumptions are used to model actual situations. It contains examples of probability models as they relate to: Bloc voting Population genetics Doubling strategies in casinos Machine reliability Airline management Cryptology Blood testing Dogs resembling owners Drug detection Jury verdicts Coincidences Number of concert hall aisles 2000 U.S. presidential election Points after deuce in tennis Tests regarding intelligent dogs Music composition Based on the author’s course at The College of William and Mary, the text can be used in a one-semester or one-quarter course in discrete probability with a strong emphasis on applications. By studying the book, students will appreciate the subject of probability and its applications and develop their problem-solving and reasoning skills.
