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By Kenneth Baclawski ISBN Code: : 9781420065220
 Publisher : CRC Press
 Pages : 384
 Category : Mathematics
 Reads : 838
 Book Compatibility : Pdf, ePub, Mobi, Kindle
 Pdf : introductiontoprobabilitywithr.pdf
Book Excerpt :
Based on a popular course taught by the late GianCarlo 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 calculusbased 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.
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 Pdf File: anintroductiontoclusteringwithr.pdf
Book Short Summary:
The purpose of this book is to thoroughly prepare the reader for applied research in clustering. Cluster analysis comprises a class of statistical techniques for classifying multivariate data into groups or clusters based on their similar features. Clustering is nowadays widely used in several domains of research, such as social sciences, psychology, and marketing, highlighting its multidisciplinary nature. This book provides an accessible and comprehensive introduction to clustering and offers practical guidelines for applying clustering tools by carefully chosen reallife datasets and extensive data analyses. The procedures addressed in this book include traditional hard clustering methods and uptodate developments in soft clustering. Attention is paid to practical examples and applications through the open source statistical software R. Commented R code and output for conducting, step by step, complete cluster analyses are available. The book is intended for researchers interested in applying clustering methods. Basic notions on theoretical issues and on R are provided so that professionals as well as novices with little or no background in the subject will benefit from the book.
R for Data Science
By Hadley Wickham,Garrett Grolemund File : Pdf, ePub, Mobi, Kindle
 Publisher : "O'Reilly Media, Inc."
 Book Code : 1491910348
 Total of Pages : 520
 Category : Computers
 Members : 995
 Pdf File: rfordatascience.pdf
Book Short Summary:
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You’ll get a complete, bigpicture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you’ve learned along the way. You’ll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a lowdimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results
Linear Models with R
By Julian J. Faraway File : Pdf, ePub, Mobi, Kindle
 Publisher : CRC Press
 Book Code : 1439887349
 Total of Pages : 286
 Category : Mathematics
 Members : 223
 Pdf File: linearmodelswithr.pdf
Book Short Summary:
A HandsOn Way to Learning Data AnalysisPart of the core of statistics, linear models are used to make predictions and explain the relationship between the response and the predictors. Understanding linear models is crucial to a broader competence in the practice of statistics. Linear Models with R, Second Edition explains how to use linear models
An Introduction to Statistics with Python
By Thomas Haslwanter File : Pdf, ePub, Mobi, Kindle
 Publisher : Springer
 Book Code : 3319283162
 Total of Pages : 278
 Category : Computers
 Members : 453
 Pdf File: anintroductiontostatisticswithpython.pdf
Book Short Summary:
This textbook provides an introduction to the free software Python and its use for statistical data analysis. It covers common statistical tests for continuous, discrete and categorical data, as well as linear regression analysis and topics from survival analysis and Bayesian statistics. Working code and data for Python solutions for each test, together with easytofollow Python examples, can be reproduced by the reader and reinforce their immediate understanding of the topic. With recent advances in the Python ecosystem, Python has become a popular language for scientific computing, offering a powerful environment for statistical data analysis and an interesting alternative to R. The book is intended for master and PhD students, mainly from the life and medical sciences, with a basic knowledge of statistics. As it also provides some statistics background, the book can be used by anyone who wants to perform a statistical data analysis.
An Introduction to Bootstrap Methods with Applications to R
By Michael R. Chernick,Robert A. LaBudde File : Pdf, ePub, Mobi, Kindle
 Publisher : John Wiley & Sons
 Book Code : 1118625412
 Total of Pages : 240
 Category : Mathematics
 Members : 882
 Pdf File: anintroductiontobootstrapmethodswithapplicationstor.pdf
Book Short Summary:
A comprehensive introduction to bootstrap methods in the Rprogramming environment Bootstrap methods provide a powerful approach to statisticaldata analysis, as they have more general applications than standardparametric methods. An Introduction to Bootstrap Methods withApplications to R explores the practicality of this approach andsuccessfully utilizes R to illustrate applications for thebootstrap and other resampling methods. This book provides a modernintroduction to bootstrap methods for readers who do not have anextensive background in advanced mathematics. Emphasis throughoutis on the use of bootstrap methods as an exploratory tool,including its value in variable selection and other modelingenvironments. The authors begin with a description of bootstrap methods andits relationship to other resampling methods, along with anoverview of the wide variety of applications of the approach.Subsequent chapters offer coverage of improved confidence setestimation, estimation of error rates in discriminant analysis, andapplications to a wide variety of hypothesis testing and estimationproblems, including pharmaceutical, genomics, and economics. Toinform readers on the limitations of the method, the book alsoexhibits counterexamples to the consistency of bootstrapmethods. An introduction to R programming provides the needed preparationto work with the numerous exercises and applications presentedthroughout the book. A related website houses the book's Rsubroutines, and an extensive listing of references providesresources for further study. Discussing the topic at a remarkably practical and accessiblelevel, An Introduction to Bootstrap Methods with Applications to Ris an excellent book for introductory courses on bootstrap andresampling methods at the upperundergraduate and graduate levels.It also serves as an insightful reference for practitioners workingwith data in engineering, medicine, and the social sciences whowould like to acquire a basic understanding of bootstrapmethods.
Basic Statistics with R
By Stephen C. Loftus File : Pdf, ePub, Mobi, Kindle
 Publisher : Academic Press
 Book Code : 0128209267
 Total of Pages : 304
 Category : Mathematics
 Members : 140
 Pdf File: basicstatisticswithr.pdf
Book Short Summary:
Basic Statistics with R: Reaching Decisions with Data provides an understanding of the processes at work in using data for results. Sections cover data collection and discuss exploratory analyses, including visual graphs, numerical summaries, and relationships between variables  basic probability, and statistical inference  including hypothesis testing and confidence intervals. All topics are taught using realdata drawn from various fields, including economics, biology, political science and sports. Using this wide variety of motivating examples allows students to directly connect and make statistics essential to their field of interest, rather than seeing it as a separate and ancillary knowledge area. In addition to introducing students to statistical topics using real data, the book provides a gentle introduction to coding, having the students use the statistical language and software R. Students learn to load data, calculate summary statistics, create graphs and do statistical inference using R with either Windows or Macintosh machines. Features realdata to give students an engaging practice to connect with their areas of interest Evolves from basic problems that can be worked by hand to the elementary use of opensource R software Offers a direct, clear approach highlighted by useful visuals and examples
Simulation
By Sheldon M. Ross File : Pdf, ePub, Mobi, Kindle
 Publisher : Academic Press
 Book Code : 0124159710
 Total of Pages : 328
 Category : Mathematics
 Members : 374
 Pdf File: simulation.pdf
Book Short Summary:
The 5th edition of Ross’s Simulation continues to introduce aspiring and practicing actuaries, engineers, computer scientists and others to the practical aspects of constructing computerized simulation studies to analyze and interpret real phenomena. Readers learn to apply results of these analyses to problems in a wide variety of fields to obtain effective, accurate solutions and make predictions about future outcomes. This latest edition features allnew material on variance reduction, including control variables and their use in estimating the expected return at blackjack and their relation to regression analysis. Additionally, the 5th edition expands on Markov chain monte carlo methods, and offers unique information on the alias method for generating discrete random variables. By explaining how a computer can be used to generate random numbers and how to use these random numbers to generate the behavior of a stochastic model over time, Ross’s Simulation, 5th edition presents the statistics needed to analyze simulated data as well as that needed for validating the simulation model. Additional material on variance reduction, including control variables and their use in estimating the expected return at blackjack and their relation to regression analysis Additional material and examples on Markov chain Monte Carlo methods Unique material on the alias method for generating discrete random variables Additional material on generating multivariate normal vectors