probability with applications and r

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Author : Robert P. Dobrow
ISBN : 9781118589441
Genre : Mathematics
File Size : 34. 7 MB
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An introduction to probability at the undergraduate level Chance and randomness are encountered on a daily basis. Authoredby a highly qualified professor in the field, Probability: WithApplications and R delves into the theories and applicationsessential to obtaining a thorough understanding of probability. With real-life examples and thoughtful exercises from fields asdiverse as biology, computer science, cryptology, ecology, publichealth, and sports, the book is accessible for a variety ofreaders. The book’s emphasis on simulation through the use ofthe popular R software language clarifies and illustrates keycomputational and theoretical results. Probability: With Applications and R helps readersdevelop problem-solving skills and delivers an appropriate mix oftheory and application. The book includes: Chapters covering first principles, conditional probability,independent trials, random variables, discrete distributions,continuous probability, continuous distributions, conditionaldistribution, and limits An early introduction to random variables and Monte Carlosimulation and an emphasis on conditional probability,conditioning, and developing probabilistic intuition An R tutorial with example script files Many classic and historical problems of probability as well asnontraditional material, such as Benford’s law, power-lawdistributions, and Bayesian statistics A topics section with suitable material for projects andexplorations, such as random walk on graphs, Markov chains, andMarkov chain Monte Carlo Chapter-by-chapter summaries and hundreds of practicalexercises Probability: With Applications and R is an ideal text fora beginning course in probability at the undergraduate level.

Probability With Applications And R

Author : Robert P. Dobrow
ISBN : OCLC:958742412
Genre : Probabilities
File Size : 70. 69 MB
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"An introduction to probability at the undergraduate level Chance and randomness are encountered on a daily basis. Authored by a highly qualified professor in the field, Probability: With Applications and R delves into the theories and applications essential to obtaining a thorough understanding of probability. With real-life examples and thoughtful exercises from fields as diverse as biology, computer science, cryptology, ecology, public health, and sports, the book is accessible for a variety of readers. The book's emphasis on simulation through the use of the popular R software language clarifies and illustrates key computational and theoretical results."--Publisher's website.

Mathematical Statistics With Resampling And R Probability With Applications And R Set

Author : Laura M. Chihara
ISBN : 1118947436
Genre : Mathematics
File Size : 34. 40 MB
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This book bridges the latest software applications with the benefits of modern resampling techniques Resampling helps students understand the meaning of sampling distributions, sampling variability, P-values, hypothesis tests, and confidence intervals. This groundbreaking book shows how to apply modern resampling techniques to mathematical statistics. Extensively class-tested to ensure an accessible presentation, M"athematical Statistics with Resampling and R" utilizes the powerful and flexible computer language R to underscore the significance and benefits of modern resampling techniques. The book begins by introducing permutation tests and bootstrap methods, motivating classical inference methods. Striking a balance between theory, computing, and applications, the authors explore additional topics such as: Exploratory data analysis Calculation of sampling distributions The Central Limit Theorem Monte Carlo sampling Maximum likelihood estimation and properties of estimators Confidence intervals and hypothesis tests Regression Bayesian methods Throughout the book, case studies on diverse subjects such as flight delays, birth weights of babies, and telephone company repair times illustrate the relevance of the real-world applications of the discussed material. Key definitions and theorems of important probability distributions are collected at the end of the book, and a related website is also available, featuring additional material including data sets, R scripts, and helpful teaching hints. "Mathematical Statistics with Resampling and R" is an excellent book for courses on mathematical statistics at the upper-undergraduate and graduate levels. It also serves as a valuable reference for applied statisticians working in the areas of business, economics, biostatistics, and public health who utilize resampling methods in their everyday work.

Probability With R

Author : Jane Horgan
ISBN : 9781118165959
Genre : Mathematics
File Size : 55. 59 MB
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A Complete Introduction to probability AND its computer ScienceApplications USING R Probability with R serves as a comprehensive andintroductory book on probability with an emphasis oncomputing-related applications. Real examples show how probabilitycan be used in practical situations, and the freely available anddownloadable statistical programming language R illustrates andclarifies the book's main principles. Promoting a simulation- and experimentation-driven methodology,this book highlights the relationship between probability andcomputing in five distinctive parts: The R Language presents the essentials of the R language,including key procedures for summarizing and building graphicaldisplays of statistical data. Fundamentals of Probability provides the foundations ofthe basic concepts of probability and moves into applications incomputing. Topical coverage includes conditional probability,Bayes' theorem, system reliability, and the development of the mainlaws and properties of probability. Discrete Distributions addresses discrete randomvariables and their density and distribution functions as well asthe properties of expectation. The geometric, binomial,hypergeometric, and Poisson distributions are also discussed andused to develop sampling inspection schemes. Continuous Distributions introduces continuous variablesby examining the waiting time between Poisson occurrences. Theexponential distribution and its applications to reliability areinvestigated, and the Markov property is illustrated via simulationin R. The normal distribution is examined and applied tostatistical process control. Tailing Off delves into the use of Markov and Chebyshevinequalities as tools for estimating tail probabilities withlimited information on the random variable. Numerous exercises and projects are provided in each chapter,many of which require the use of R to perform routine calculationsand conduct experiments with simulated data. The author directsreaders to the appropriate Web-based resources for installing the Rsoftware package and also supplies the essential commands forworking in the R workspace. A related Web site features an activeappendix as well as a forum for readers to share findings,thoughts, and ideas. With its accessible and hands-on approach, Probability withR is an ideal book for a first course in probability at theupper-undergraduate and graduate levels for readers with abackground in computer science, engineering, and the generalsciences. It also serves as a valuable reference for computingprofessionals who would like to further understand the relevance ofprobability in their areas of practice.

Mathematical Statistics With Applications In R

Author : Kandethody M. Ramachandran
ISBN : 9780124171329
Genre : Mathematics
File Size : 64. 5 MB
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Mathematical Statistics with Applications in R, Second Edition, offers a modern calculus-based theoretical introduction to mathematical statistics and applications. The book covers many modern statistical computational and simulation concepts that are not covered in other texts, such as the Jackknife, bootstrap methods, the EM algorithms, and Markov chain Monte Carlo (MCMC) methods such as the Metropolis algorithm, Metropolis-Hastings algorithm and the Gibbs sampler. By combining the discussion on the theory of statistics with a wealth of real-world applications, the book helps students to approach statistical problem solving in a logical manner. This book provides a step-by-step procedure to solve real problems, making the topic more accessible. It includes goodness of fit methods to identify the probability distribution that characterizes the probabilistic behavior or a given set of data. Exercises as well as practical, real-world chapter projects are included, and each chapter has an optional section on using Minitab, SPSS and SAS commands. The text also boasts a wide array of coverage of ANOVA, nonparametric, MCMC, Bayesian and empirical methods; solutions to selected problems; data sets; and an image bank for students. Advanced undergraduate and graduate students taking a one or two semester mathematical statistics course will find this book extremely useful in their studies. Step-by-step procedure to solve real problems, making the topic more accessible Exercises blend theory and modern applications Practical, real-world chapter projects Provides an optional section in each chapter on using Minitab, SPSS and SAS commands Wide array of coverage of ANOVA, Nonparametric, MCMC, Bayesian and empirical methods

Probability Applications In Mechanical Design

Author : Franklin Fisher
ISBN : 9780824745400
Genre : Science
File Size : 73. 90 MB
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The authors of this text seek to clarify mechanical fatigue and design problems by applying probability and computer analysis, and further extending the uses of probability to determine mechanical reliability and achieve optimization. The work solves examples using commercially available software. It is formatted with examples and problems for use in a one-semester graduate course.

Statistik Workshop F R Programmierer

Author : Allen B. Downey
ISBN : 9783868993431
Genre : Computers
File Size : 88. 18 MB
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Wenn Sie programmieren können, beherrschen Sie bereits Techniken, um aus Daten Wissen zu extrahieren. Diese kompakte Einführung in die Statistik zeigt Ihnen, wie Sie rechnergestützt, anstatt auf mathematischem Weg Datenanalysen mit Python durchführen können. Praktischer Programmier-Workshop statt grauer Theorie: Das Buch führt Sie anhand eines durchgängigen Fallbeispiels durch eine vollständige Datenanalyse -- von der Datensammlung über die Berechnung statistischer Kennwerte und Identifikation von Mustern bis hin zum Testen statistischer Hypothesen. Gleichzeitig werden Sie mit statistischen Verteilungen, den Regeln der Wahrscheinlichkeitsrechnung, Visualisierungsmöglichkeiten und vielen anderen Arbeitstechniken und Konzepten vertraut gemacht. Statistik-Konzepte zum Ausprobieren: Entwickeln Sie über das Schreiben und Testen von Code ein Verständnis für die Grundlagen von Wahrscheinlichkeitsrechnung und Statistik: Überprüfen Sie das Verhalten statistischer Merkmale durch Zufallsexperimente, zum Beispiel indem Sie Stichproben aus unterschiedlichen Verteilungen ziehen. Nutzen Sie Simulationen, um Konzepte zu verstehen, die auf mathematischem Weg nur schwer zugänglich sind. Lernen Sie etwas über Themen, die in Einführungen üblicherweise nicht vermittelt werden, beispielsweise über die Bayessche Schätzung. Nutzen Sie Python zur Bereinigung und Aufbereitung von Rohdaten aus nahezu beliebigen Quellen. Beantworten Sie mit den Mitteln der Inferenzstatistik Fragestellungen zu realen Daten.

Probability With Applications In Engineering Science And Technology

Author : Matthew A. Carlton
ISBN : 9783319524016
Genre : Mathematics
File Size : 59. 70 MB
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This updated and revised first-course textbook in applied probability provides a contemporary and lively post-calculus introduction to the subject of probability. The exposition reflects a desirable balance between fundamental theory and many applications involving a broad range of real problem scenarios. It is intended to appeal to a wide audience, including mathematics and statistics majors, prospective engineers and scientists, and those business and social science majors interested in the quantitative aspects of their disciplines. The textbook contains enough material for a year-long course, though many instructors will use it for a single term (one semester or one quarter). As such, three course syllabi with expanded course outlines are now available for download on the book’s page on the Springer website. A one-term course would cover material in the core chapters (1-4), supplemented by selections from one or more of the remaining chapters on statistical inference (Ch. 5), Markov chains (Ch. 6), stochastic processes (Ch. 7), and signal processing (Ch. 8 – available exclusively online and specifically designed for electrical and computer engineers, making the book suitable for a one-term class on random signals and noise). For a year-long course, core chapters (1-4) are accessible to those who have taken a year of univariate differential and integral calculus; matrix algebra, multivariate calculus, and engineering mathematics are needed for the latter, more advanced chapters. At the heart of the textbook’s pedagogy are 1,100 applied exercises, ranging from straightforward to reasonably challenging, roughly 700 exercises in the first four “core” chapters alone—a self-contained textbook of problems introducing basic theoretical knowledge necessary for solving problems and illustrating how to solve the problems at hand – in R and MATLAB, including code so that students can create simulations.

Cluster Analysis For Applications

Author : Michael R. Anderberg
ISBN : 9781483191393
Genre : Mathematics
File Size : 88. 46 MB
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Cluster Analysis for Applications deals with methods and various applications of cluster analysis. Topics covered range from variables and scales to measures of association among variables and among data units. Conceptual problems in cluster analysis are discussed, along with hierarchical and non-hierarchical clustering methods. The necessary elements of data analysis, statistics, cluster analysis, and computer implementation are integrated vertically to cover the complete path from raw data to a finished analysis. Comprised of 10 chapters, this book begins with an introduction to the subject of cluster analysis and its uses as well as category sorting problems and the need for cluster analysis algorithms. The next three chapters give a detailed account of variables and association measures, with emphasis on strategies for dealing with problems containing variables of mixed types. Subsequent chapters focus on the central techniques of cluster analysis with particular reference to computational considerations; interpretation of clustering results; and techniques and strategies for making the most effective use of cluster analysis. The final chapter suggests an approach for the evaluation of alternative clustering methods. The presentation is capped with a complete set of implementing computer programs listed in the Appendices to make the use of cluster analysis as painless and free of mechanical error as is possible. This monograph is intended for students and workers who have encountered the notion of cluster analysis.

Introduction To Probability Simulation And Gibbs Sampling With R

Author : Eric A. Suess
ISBN : 9780387402734
Genre : Mathematics
File Size : 33. 58 MB
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The first seven chapters use R for probability simulation and computation, including random number generation, numerical and Monte Carlo integration, and finding limiting distributions of Markov Chains with both discrete and continuous states. Applications include coverage probabilities of binomial confidence intervals, estimation of disease prevalence from screening tests, parallel redundancy for improved reliability of systems, and various kinds of genetic modeling. These initial chapters can be used for a non-Bayesian course in the simulation of applied probability models and Markov Chains. Chapters 8 through 10 give a brief introduction to Bayesian estimation and illustrate the use of Gibbs samplers to find posterior distributions and interval estimates, including some examples in which traditional methods do not give satisfactory results. WinBUGS software is introduced with a detailed explanation of its interface and examples of its use for Gibbs sampling for Bayesian estimation. No previous experience using R is required. An appendix introduces R, and complete R code is included for almost all computational examples and problems (along with comments and explanations). Noteworthy features of the book are its intuitive approach, presenting ideas with examples from biostatistics, reliability, and other fields; its large number of figures; and its extraordinarily large number of problems (about a third of the pages), ranging from simple drill to presentation of additional topics. Hints and answers are provided for many of the problems. These features make the book ideal for students of statistics at the senior undergraduate and at the beginning graduate levels.

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