# Probability and Statistics with R, Second Edition Author: Maria Dolores Ugarte,Ana F. Militino,Alan T. Arnholt

Publisher: CRC Press

ISBN: 1466504404

Category: Mathematics

Page: 983

View: 9417

Cohesively Incorporates Statistical Theory with R Implementation Since the publication of the popular first edition of this comprehensive textbook, the contributed R packages on CRAN have increased from around 1,000 to over 6,000. Designed for an intermediate undergraduate course, Probability and Statistics with R, Second Edition explores how some of these new packages make analysis easier and more intuitive as well as create more visually pleasing graphs. New to the Second Edition Improvements to existing examples, problems, concepts, data, and functions New examples and exercises that use the most modern functions Coverage probability of a confidence interval and model validation Highlighted R code for calculations and graph creation Gets Students Up to Date on Practical Statistical Topics Keeping pace with today’s statistical landscape, this textbook expands your students’ knowledge of the practice of statistics. It effectively links statistical concepts with R procedures, empowering students to solve a vast array of real statistical problems with R. Web Resources A supplementary website offers solutions to odd exercises and templates for homework assignments while the data sets and R functions are available on CRAN.

# Statistik-Workshop für Programmierer Author: Allen B. Downey

Publisher: O'Reilly Germany

ISBN: 3868993436

Category: Computers

Page: 160

View: 3005

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 & Statistics with R for Engineers and Scientists Author: Michael Akritas

Publisher: Pearson

ISBN: 9780134995359

Category: Engineering

Page: 528

View: 9866

This title is part of the Pearson Modern Classics series. Pearson Modern Classics are acclaimed titles at a value price. Please visit www.pearsonhighered.com/math-classics-series for a complete list of titles. This text grew out of the author's notes for a course that he has taught for many years to a diverse group of undergraduates. The early introduction to the major concepts engages students immediately, which helps them see the big picture, and sets an appropriate tone for the course. In subsequent chapters, these topics are revisited, developed, and formalized, but the early introduction helps students build a true understanding of the concepts. The text utilizes the statistical software R, which is both widely used and freely available (thanks to the Free Software Foundation). However, in contrast with other books for the intended audience, this book by Akritas emphasizes not only the interpretation of software output, but also the generation of this output. Applications are diverse and relevant, and come from a variety of fields.

# Probability and Statistics for Science and Engineering with Examples in R (First Edition) Author: Hongshik Ahn

Publisher: Cognella Academic Publishing

ISBN: 9781516513987

Category: Technology & Engineering

Page: 342

View: 8554

Probability and Statistics for Science and Engineering with Examples in R teaches students how to use R software to obtain summary statistics, calculate probabilities and quantiles, find confidence intervals, and conduct statistical testing. The first chapter introduces methods for describing statistics. Over the course of the subsequent eight chapters students will learn about probability, discrete and continuous distributions, multiple random variables, point estimation and testing, and inferences based on one and two samples. The book features a comprehensive table for each type of test to help students choose appropriate statistical tests and confidence intervals. Based on years of classroom experience and extensively class-tested, Probability and Statistics for Science and Engineering with Examples in R is designed for one-semester courses in probability and statistics, and specifically for students in the natural sciences or engineering. The material is also suitable for business and economics students who have studied calculus. Hongshik Ahn holds a Ph.D. in statistics from the University of Wisconsin, Madison. Dr. Ahn is currently a professor in the Department of Applied Mathematics and Statistics at Stony Brook University. He worked at National Center for Toxicological Research, FDA before joining Stony Brook University. Recently he served as the vice president of SUNY Korea. His research interests include tree-structured regression and classification, bioinformatics, generalized linear modeling, and risk assessment. Dr. Ahn has been working on NIH grants on various biostatistical and medical researches. He has been published in three book chapters and over 60 peer-reviewed journals. Dr. Ahn also published a book entitled Mathematical Analysis of Genesis, from Shinil Books.

# A Course in Statistics with R Author: Prabhanjan N. Tattar,Suresh Ramaiah,B. G. Manjunath

Publisher: John Wiley & Sons

ISBN: 1119152739

Category: Computers

Page: 696

View: 4941

Integrates the theory and applications of statistics using R A Course in Statistics with R has been written to bridge the gap between theory and applications and explain how mathematical expressions are converted into R programs. The book has been primarily designed as a useful companion for a Masters student during each semester of the course, but will also help applied statisticians in revisiting the underpinnings of the subject. With this dual goal in mind, the book begins with R basics and quickly covers visualization and exploratory analysis. Probability and statistical inference, inclusive of classical, nonparametric, and Bayesian schools, is developed with definitions, motivations, mathematical expression and R programs in a way which will help the reader to understand the mathematical development as well as R implementation. Linear regression models, experimental designs, multivariate analysis, and categorical data analysis are treated in a way which makes effective use of visualization techniques and the related statistical techniques underlying them through practical applications, and hence helps the reader to achieve a clear understanding of the associated statistical models. Key features: Integrates R basics with statistical concepts Provides graphical presentations inclusive of mathematical expressions Aids understanding of limit theorems of probability with and without the simulation approach Presents detailed algorithmic development of statistical models from scratch Includes practical applications with over 50 data sets

# Probability & Statistics for Engineers and Scientists with R Author: Michael Akritas

Publisher: Pearson

ISBN: 0321997409

Category: Mathematics

Page: 528

View: 2941

This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book. This text grew out of the author’s notes for a course that he has taught for many years to a diverse group of undergraduates. The early introduction to the major concepts engages students immediately, which helps them see the big picture, and sets an appropriate tone for the course. In subsequent chapters, these topics are revisited, developed, and formalized, but the early introduction helps students build a true understanding of the concepts. The text utilizes the statistical software R, which is both widely used and freely available (thanks to the Free Software Foundation). However, in contrast with other books for the intended audience, this book by Akritas emphasizes not only the interpretation of software output, but also the generation of this output. Applications are diverse and relevant, and come from a variety of fields.

# Introduction to Probability and Statistics Using R Author: G. Jay Kerns

Publisher: Lulu.com

ISBN: 0557249791

Category:

Page: N.A

View: 789

# Mathematical Statistics with Resampling and R & Probability with Applications and R Set Author: Laura M. Chihara,Tim C. Hesterberg,Robert P. Dobrow

Publisher: Wiley

ISBN: 9781118947432

Category: Mathematics

Page: 960

View: 2434

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 An Introduction with Computer Science Applications

Author: Jane Horgan

Publisher: John Wiley & Sons

ISBN: 1118165950

Category: Mathematics

Page: 416

View: 9139

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.

# Exploring Probability and Statistics with Spreadsheets Author: R. Jackson,John T. Callender,J. L. Callender

Publisher: Prentice Hall PTR

ISBN: 9780133096590

Category: Mathematics

Page: 236

View: 479

This text aims to offer a new approach to teaching probability and statistics by allowing students to discover statistics, using spreadsheets to illustrate concepts and solve problems.

# Introductory Statistics with R Author: Peter Dalgaard

Publisher: Springer Science & Business Media

ISBN: 9780387790534

Category: Mathematics

Page: 364

View: 9451

This book provides an elementary-level introduction to R, targeting both non-statistician scientists in various fields and students of statistics. The main mode of presentation is via code examples with liberal commenting of the code and the output, from the computational as well as the statistical viewpoint. Brief sections introduce the statistical methods before they are used. A supplementary R package can be downloaded and contains the data sets. All examples are directly runnable and all graphics in the text are generated from the examples. The statistical methodology covered includes statistical standard distributions, one- and two-sample tests with continuous data, regression analysis, one-and two-way analysis of variance, regression analysis, analysis of tabular data, and sample size calculations. In addition, the last four chapters contain introductions to multiple linear regression analysis, linear models in general, logistic regression, and survival analysis.

# Introduction to Probability with R Author: Kenneth Baclawski

Publisher: CRC Press

ISBN: 9781420065220

Category: Mathematics

Page: 384

View: 6752

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.

# Probability With Applications and R

Author: Robert P. Dobrow

Publisher: John Wiley & Sons

ISBN: 1118589440

Category: Mathematics

Page: 520

View: 1493

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.

# Mathematical Statistics with Applications in R Author: Kandethody M. Ramachandran,Chris P. Tsokos

Publisher: Elsevier

ISBN: 012417132X

Category: Mathematics

Page: 826

View: 7705

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

# Learning Statistics with R Author: Daniel Navarro

Publisher: Lulu.com

ISBN: 1326189727

Category:

Page: N.A

View: 5932

# An Introduction to Analysis of Financial Data with R Author: Ruey S. Tsay

Publisher: John Wiley & Sons

ISBN: 1119013461

Category: Business & Economics

Page: 416

View: 8173

# Robust Statistics Theory and Methods (with R)

Author: Ricardo A. Maronna,R. Douglas Martin,Victor J. Yohai,Matías Salibián-Barrera

Publisher: John Wiley & Sons

ISBN: 1119214661

Category: Mathematics

Page: 464

View: 5620

A new edition of this popular text on robust statistics, thoroughly updated to include new and improved methods and focus on implementation of methodology using the increasingly popular open-source software R. Classical statistics fail to cope well with outliers associated with deviations from standard distributions. Robust statistical methods take into account these deviations when estimating the parameters of parametric models, thus increasing the reliability of fitted models and associated inference. This new, second edition of Robust Statistics: Theory and Methods (with R) presents a broad coverage of the theory of robust statistics that is integrated with computing methods and applications. Updated to include important new research results of the last decade and focus on the use of the popular software package R, it features in-depth coverage of the key methodology, including regression, multivariate analysis, and time series modeling. The book is illustrated throughout by a range of examples and applications that are supported by a companion website featuring data sets and R code that allow the reader to reproduce the examples given in the book. Unlike other books on the market, Robust Statistics: Theory and Methods (with R) offers the most comprehensive, definitive, and up-to-date treatment of the subject. It features chapters on estimating location and scale; measuring robustness; linear regression with fixed and with random predictors; multivariate analysis; generalized linear models; time series; numerical algorithms; and asymptotic theory of M-estimates. Explains both the use and theoretical justification of robust methods Guides readers in selecting and using the most appropriate robust methods for their problems Features computational algorithms for the core methods Robust statistics research results of the last decade included in this 2nd edition include: fast deterministic robust regression, finite-sample robustness, robust regularized regression, robust location and scatter estimation with missing data, robust estimation with independent outliers in variables, and robust mixed linear models. Robust Statistics aims to stimulate the use of robust methods as a powerful tool to increase the reliability and accuracy of statistical modelling and data analysis. It is an ideal resource for researchers, practitioners, and graduate students in statistics, engineering, computer science, and physical and social sciences.

# Multivariate Time Series Analysis With R and Financial Applications

Author: Ruey S. Tsay

Publisher: John Wiley & Sons

ISBN: 1118617754

Category: Mathematics

Page: 520

View: 3967

An accessible guide to the multivariate time series toolsused in numerous real-world applications Multivariate Time Series Analysis: With R and FinancialApplications is the much anticipated sequel coming from one ofthe most influential and prominent experts on the topic of timeseries. Through a fundamental balance of theory and methodology,the book supplies readers with a comprehensible approach tofinancial econometric models and their applications to real-worldempirical research. Differing from the traditional approach to multivariate timeseries, the book focuses on reader comprehension by emphasizingstructural specification, which results in simplified parsimoniousVAR MA modeling. Multivariate Time Series Analysis: With R andFinancial Applications utilizes the freely available Rsoftware package to explore complex data and illustrate relatedcomputation and analyses. Featuring the techniques and methodologyof multivariate linear time series, stationary VAR models, VAR MAtime series and models, unitroot process, factor models, andfactor-augmented VAR models, the book includes: • Over 300 examples and exercises to reinforce thepresented content • User-friendly R subroutines and research presentedthroughout to demonstrate modern applications • Numerous datasets and subroutines to provide readerswith a deeper understanding of the material Multivariate Time Series Analysis is an ideal textbookfor graduate-level courses on time series and quantitative financeand upper-undergraduate level statistics courses in time series.The book is also an indispensable reference for researchers andpractitioners in business, finance, and econometrics.

# Introduction to Probability Simulation and Gibbs Sampling with R Author: Eric A. Suess,Bruce E. Trumbo

Publisher: Springer Science & Business Media

ISBN: 0387687653

Category: Mathematics

Page: 307

View: 6170

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.