Introduction to Probability

Author: Joseph K. Blitzstein
Publisher: CRC Press
ISBN: 9781466575592
Release Date: 2014-07-24
Genre: Mathematics

Developed from celebrated Harvard statistics lectures, Introduction to Probability provides essential language and tools for understanding statistics, randomness, and uncertainty. The book explores a wide variety of applications and examples, ranging from coincidences and paradoxes to Google PageRank and Markov chain Monte Carlo (MCMC). Additional application areas explored include genetics, medicine, computer science, and information theory. The print book version includes a code that provides free access to an eBook version. The authors present the material in an accessible style and motivate concepts using real-world examples. Throughout, they use stories to uncover connections between the fundamental distributions in statistics and conditioning to reduce complicated problems to manageable pieces. The book includes many intuitive explanations, diagrams, and practice problems. Each chapter ends with a section showing how to perform relevant simulations and calculations in R, a free statistical software environment.

Introduction to Probability Second Edition

Author: Joseph K. Blitzstein
Publisher: CRC Press
ISBN: 9780429766732
Release Date: 2019-02-08
Genre: Mathematics

Developed from celebrated Harvard statistics lectures, Introduction to Probability provides essential language and tools for understanding statistics, randomness, and uncertainty. The book explores a wide variety of applications and examples, ranging from coincidences and paradoxes to Google PageRank and Markov chain Monte Carlo (MCMC). Additional application areas explored include genetics, medicine, computer science, and information theory. The authors present the material in an accessible style and motivate concepts using real-world examples. Throughout, they use stories to uncover connections between the fundamental distributions in statistics and conditioning to reduce complicated problems to manageable pieces. The book includes many intuitive explanations, diagrams, and practice problems. Each chapter ends with a section showing how to perform relevant simulations and calculations in R, a free statistical software environment. The second edition adds many new examples, exercises, and explanations, to deepen understanding of the ideas, clarify subtle concepts, and respond to feedback from many students and readers. New supplementary online resources have been developed, including animations and interactive visualizations, and the book has been updated to dovetail with these resources.

Introduction to Probability with R

Author: Kenneth Baclawski
Publisher: CRC Press
ISBN: 142006522X
Release Date: 2008-01-24
Genre: Mathematics

Based on a popular course taught by the late Gian-Carlo Rota of MIT, with many new topics covered as well, Introduction to Probability with R presents R programs and animations to provide an intuitive yet rigorous understanding of how to model natural phenomena from a probabilistic point of view. Although the R programs are small in length, they are just as sophisticated and powerful as longer programs in other languages. This brevity makes it easy for students to become proficient in R. This calculus-based introduction organizes the material around key themes. One of the most important themes centers on viewing probability as a way to look at the world, helping students think and reason probabilistically. The text also shows how to combine and link stochastic processes to form more complex processes that are better models of natural phenomena. In addition, it presents a unified treatment of transforms, such as Laplace, Fourier, and z; the foundations of fundamental stochastic processes using entropy and information; and an introduction to Markov chains from various viewpoints. Each chapter includes a short biographical note about a contributor to probability theory, exercises, and selected answers. The book has an accompanying website with more information.

Statistik II f r Dummies

Author: Deborah J. Rumsey
Publisher: John Wiley & Sons
ISBN: 9783527669240
Release Date: 2013-03-25
Genre: Mathematics

Vom Absolutrang bis zum Zweifach-Varianzanalysemodell – alles, was Sie über weiterführende Statistik wissen sollten Es gibt Qualen, große Qualen und Statistik, so sehen es viele Studenten. Mit diesem Buch lernen Sie weiterführende Statistik so leicht wie möglich. Deborah Rumsey zeigt Ihnen, wie Sie Varianzanalysen und Chi-Quadrat-Tests berechnen, wie Sie mit Regressionen arbeiten, ein Modell erstellen, Korrelationen bilden, nichtparametrische Prozeduren durchführen und vieles mehr. Aber auch die Grundlagen der Statistik bleiben nicht außen vor und deshalb erklärt Ihnen die Autorin, was Sie zu Mittelwerten, Vertrauensintervallen und Co wissen sollten. So lernen Sie die Methoden, die Sie brauchen, und erhalten das Handwerkszeug, um erfolgreich Ihre Statistikprüfungen zu bestehen. Sie erfahren: • Wie Sie mit multiplen Regressionen umgehen • Was es mit dem Vorzeichentest und dem Vorzeichenrangtest auf sich hat • Wie Sie sich innerhalb der statistischen Techniken zurechtfinden • Was das richtige Regressionsmodell für Ihre Analyse ist • Wie Regression und ANOVA zusammenhängen

Elementary Applications of Probability Theory Second Edition

Author: Henry C. Tuckwell
Publisher: CRC Press
ISBN: 0412576201
Release Date: 1995-05-15
Genre: Mathematics

This book provides a clear and straightforward introduction to applications of probability theory with examples given in the biological sciences and engineering. The first chapter contains a summary of basic probability theory. Chapters two to five deal with random variables and their applications. Topics covered include geometric probability, estimation of animal and plant populations, reliability theory and computer simulation. Chapter six contains a lucid account of the convergence of sequences of random variables, with emphasis on the central limit theorem and the weak law of numbers. The next four chapters introduce random processes, including random walks and Markov chains illustrated by examples in population genetics and population growth. This edition also includes two chapters which introduce, in a manifestly readable fashion, the topic of stochastic differential equations and their applications.

Introduction to Statistical Methods for Financial Models

Author: Thomas A Severini
Publisher: CRC Press
ISBN: 9781351981903
Release Date: 2017-07-06
Genre: Business & Economics

This book provides an introduction to the use of statistical concepts and methods to model and analyze financial data. The ten chapters of the book fall naturally into three sections. Chapters 1 to 3 cover some basic concepts of finance, focusing on the properties of returns on an asset. Chapters 4 through 6 cover aspects of portfolio theory and the methods of estimation needed to implement that theory. The remainder of the book, Chapters 7 through 10, discusses several models for financial data, along with the implications of those models for portfolio theory and for understanding the properties of return data. The audience for the book is students majoring in Statistics and Economics as well as in quantitative fields such as Mathematics and Engineering. Readers are assumed to have some background in statistical methods along with courses in multivariate calculus and linear algebra.

Bayesian Ideas and Data Analysis

Author: Ronald Christensen
Publisher: CRC Press
ISBN: 9781439803554
Release Date: 2011-07-07
Genre: Mathematics

Emphasizing the use of WinBUGS and R to analyze real data, Bayesian Ideas and Data Analysis: An Introduction for Scientists and Statisticians presents statistical tools to address scientific questions. It highlights foundational issues in statistics, the importance of making accurate predictions, and the need for scientists and statisticians to collaborate in analyzing data. The WinBUGS code provided offers a convenient platform to model and analyze a wide range of data. The first five chapters of the book contain core material that spans basic Bayesian ideas, calculations, and inference, including modeling one and two sample data from traditional sampling models. The text then covers Monte Carlo methods, such as Markov chain Monte Carlo (MCMC) simulation. After discussing linear structures in regression, it presents binomial regression, normal regression, analysis of variance, and Poisson regression, before extending these methods to handle correlated data. The authors also examine survival analysis and binary diagnostic testing. A complementary chapter on diagnostic testing for continuous outcomes is available on the book’s website. The last chapter on nonparametric inference explores density estimation and flexible regression modeling of mean functions. The appropriate statistical analysis of data involves a collaborative effort between scientists and statisticians. Exemplifying this approach, Bayesian Ideas and Data Analysis focuses on the necessary tools and concepts for modeling and analyzing scientific data. Data sets and codes are provided on a supplemental website.

Stichprobenverfahren

Author: William G. Cochran
Publisher: Walter de Gruyter
ISBN: 9783110823004
Release Date: 2013-06-21
Genre: Reference


An Introduction to Statistical Inference and Its Applications with R

Author: Michael W. Trosset
Publisher: CRC Press
ISBN: 1584889489
Release Date: 2009-06-23
Genre: Mathematics

Emphasizing concepts rather than recipes, An Introduction to Statistical Inference and Its Applications with R provides a clear exposition of the methods of statistical inference for students who are comfortable with mathematical notation. Numerous examples, case studies, and exercises are included. R is used to simplify computation, create figures, and draw pseudorandom samples—not to perform entire analyses. After discussing the importance of chance in experimentation, the text develops basic tools of probability. The plug-in principle then provides a transition from populations to samples, motivating a variety of summary statistics and diagnostic techniques. The heart of the text is a careful exposition of point estimation, hypothesis testing, and confidence intervals. The author then explains procedures for 1- and 2-sample location problems, analysis of variance, goodness-of-fit, and correlation and regression. He concludes by discussing the role of simulation in modern statistical inference. Focusing on the assumptions that underlie popular statistical methods, this textbook explains how and why these methods are used to analyze experimental data.

Zeitreihenmodelle

Author: Andrew C. Harvey
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 9783486786743
Release Date: 2018-11-05
Genre: Business & Economics

Gegenstand des Werkes sind Analyse und Modellierung von Zeitreihen. Es wendet sich an Studierende und Praktiker aller Disziplinen, in denen Zeitreihenbeobachtungen wichtig sind.

Stochastic Processes

Author: Peter Watts Jones
Publisher: CRC Press
ISBN: 9781420099805
Release Date: 2009-10-09
Genre: Mathematics

Based on a highly popular, well-established course taught by the authors, Stochastic Processes: An Introduction, Second Edition discusses the modeling and analysis of random experiments using the theory of probability. It focuses on the way in which the results or outcomes of experiments vary and evolve over time. The text begins with a review of relevant fundamental probability. It then covers several basic gambling problems, random walks, and Markov chains. The authors go on to develop random processes continuous in time, including Poisson, birth and death processes, and general population models. While focusing on queues, they present an extended discussion on the analysis of associated stationary processes. The book also explores reliability and other random processes, such as branching processes, martingales, and a simple epidemic. The appendix contains key mathematical results for reference. Ideal for a one-semester course on stochastic processes, this concise, updated textbook makes the material accessible to students by avoiding specialized applications and instead highlighting simple applications and examples. The associated website contains Mathematica® and R programs that offer flexibility in creating graphs and performing computations.

Wahrscheinlichkeitsrechnung f r Dummies

Author: Deborah J. Rumsey
Publisher: John Wiley & Sons
ISBN: 9783527805495
Release Date: 2016-08-03
Genre: Mathematics

Die Wahrscheinlichkeitsrechnung wird in der Schule oft nur beiläufig behandelt, dabei handelt es sich um ein besonders spannendes und alltagstaugliches Teilgebiet der Mathematik. Für alle, die über dieses Thema noch etwas mehr erfahren wollen oder müssen, erklärt Deborah Rumsey verständlich und mit Humor, was sie unbedingt wissen sollten. Egal ob Kontingenztabelle, zentraler Grenzwertsatz, Stichproben-, Binomial- oder Poissonverteilung, in diesem Buch lernen Sie, was es ist und wie Sie es anwenden. Zu jedem Kapitel finden Sie online eine Übungsaufgabe samt Lösung, um das Gelernte zu festigen. Auch Tipps zu praktischen Anwendungen - ob bei der Arbeit oder am Pokertisch - kommen nicht zu kurz. So finden Sie in diesem Buch alles, was Sie über Wahrscheinlichkeitsrechnung unbedingt wissen sollten.

Elementare Wahrscheinlichkeitstheorie und stochastische Prozesse

Author: Kai L. Chung
Publisher: Springer-Verlag
ISBN: 9783642670336
Release Date: 2013-03-07
Genre: Mathematics

Aus den Besprechungen: "Unter den zahlreichen Einführungen in die Wahrscheinlichkeitsrechnung bildet dieses Buch eine erfreuliche Ausnahme. Der Stil einer lebendigen Vorlesung ist über Niederschrift und Übersetzung hinweg erhalten geblieben. In jedes Kapitel wird sehr anschaulich eingeführt. Sinn und Nützlichkeit der mathematischen Formulierungen werden den Lesern nahegebracht. Die wichtigsten Zusammenhänge sind als mathematische Sätze klar formuliert." #FREQUENZ#1

Web and Network Data Science

Author: Thomas W. Miller
Publisher: FT Press
ISBN: 9780133887648
Release Date: 2014-12-19
Genre: Computers

Master modern web and network data modeling: both theory and applications. In Web and Network Data Science, a top faculty member of Northwestern University’s prestigious analytics program presents the first fully-integrated treatment of both the business and academic elements of web and network modeling for predictive analytics. Some books in this field focus either entirely on business issues (e.g., Google Analytics and SEO); others are strictly academic (covering topics such as sociology, complexity theory, ecology, applied physics, and economics). This text gives today's managers and students what they really need: integrated coverage of concepts, principles, and theory in the context of real-world applications. Building on his pioneering Web Analytics course at Northwestern University, Thomas W. Miller covers usability testing, Web site performance, usage analysis, social media platforms, search engine optimization (SEO), and many other topics. He balances this practical coverage with accessible and up-to-date introductions to both social network analysis and network science, demonstrating how these disciplines can be used to solve real business problems.