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Graphical Data Analysis with R shows you what information you can gain from graphical displays. The book focuses on why you draw graphics to display data and which graphics to draw (and uses R to do so). All the datasets are available in R or one of its packages and the R code is available at rosuda.org/GDA.
Graphical data analysis is useful for data cleaning, exploring data structure, detecting outliers and unusual groups, identifying trends and clusters, spotting local patterns, evaluating modelling output, and presenting results. This book guides you in choosing graphics and understanding what...
Data science has recently gained much attention for a number of reasons, and among them is Big Data. Scientists (from almost all disciplines including physics, chemistry, biology, sociology, among others) and engineers (from all fields including civil, environmental, chemical, mechanical, among others) are faced with challenges posed by data volume, variety, and velocity, or Big Data. This book is designed to highlight the unique characteristics of geospatial data, demonstrate the need to different approaches and techniques for obtaining new knowledge from raw geospatial data, and present select state-of-the-art geospatial data...
Praise for the First Edition
"I cannot think of a better book for teachers of introductory statistics who want a readable and pedagogically sound text to introduce Bayesian statistics."
—Statistics in Medical Research
"[This book] is written in a lucid conversational style, which is so rare in mathematical writings. It does an excellent job of presenting Bayesian statistics as a perfectly reasonable approach to elementary problems in statistics."
—STATS: The Magazine for Students of Statistics, American Statistical Association
"Bolstad offers clear explanations of every concept and method making the book accessible and valuable to...
Discovered by an 18th century mathematician and preacher, Bayes' rule is a cornerstone of modern probability theory. In this richly illustrated book, a range of accessible examples is used to show how Bayes' rule is actually a natural consequence of common sense reasoning. Bayes' rule is then derived using intuitive graphical representations of probability, and Bayesian analysis is applied to parameter estimation using the MatLab and Python programs provided online. The tutorial style of writing, combined with a comprehensive glossary, makes this an ideal primer for novices who wish to become familiar with the basic principles of...
This quirky, fast-paced excursion through the world of statistics brings basic statistical concepts down to earth for general readers by showing how statistics are applied in our everyday lives. Drawing on such diverse examples as how pills are manufactured, elections are forecast, and chess tournaments are structured, psychologist Joseph Tal familiarizes readers with variables, means, medians, scales of measurement, sampling, estimating, and other stock-in-trade tools of the statistician.
An unusually lively, informal review of statistical concepts Reading Between the Numbers:
• Features dozens of fascinating, often...
The R version of Andy Field′s hugely popular Discovering Statistics Using SPSS takes students on a journey of statistical discovery using the freeware R. Like its sister textbook, Discovering Statistics Using R is written in an irreverent style and follows the same ground breaking structure and pedagogical approach. The core material is enhanced by a cast of characters to help the reader on their way, hundreds of examples, self assessment tests to consolidate knowledge, and additional website material for those wanting to learn more.
Designed for an intermediate undergraduate course, Probability and Statistics with R shows students how to solve various statistical problems using both parametric and nonparametric techniques via the open source software R. It provides numerous real-world examples, carefully explained proofs, end-of-chapter problems, and illuminating graphs to facilitate hands-on learning.
Integrating theory with practice, the text briefly introduces the syntax, structures, and functions of the S language, before covering important graphically and numerically descriptive methods. The next several chapters elucidate probability and random variables...
R is a programming language and software environment for statistical analysis, graphics representation, and reporting. If you are trying to understand the R programming language as a beginner, this short book will give you enough understanding of almost all the concepts of the R language. The author will guide you through examples, how to program in R and how to use R for effective data analysis.Buy your copy Now!
Book Objectives
This book is about R programming. The following are the objectives of the author:
To familiarize you with the basics of R programming language.
Applied Spatial Data Analysis with R, second edition, is divided into two basic parts, the first presenting R packages, functions, classes and methods for handling spatial data. This part is of interest to users who need to access and visualise spatial data. Data import and export for many file formats for spatial data are covered in detail, as is the interface between R and the open source GRASS GIS and the handling of spatio-temporal data. The second part showcases more specialised kinds of spatial data analysis, including spatial point pattern analysis, interpolation and geostatistics, areal data analysis and disease mapping. The...
The second edition of a bestselling textbook, Using R for Introductory Statistics guides students through the basics of R, helping them overcome the sometimes steep learning curve. The author does this by breaking the material down into small, task-oriented steps. The second edition maintains the features that made the first edition so popular, while updating data, examples, and changes to R in line with the current version.
See What’s New in the Second Edition:
Increased emphasis on more idiomatic R provides a grounding in the functionality of base R.
Discussions of the use of RStudio helps new R users avoid as many...
The cost of statistical computing software has precluded many universities from installing these valuable computational and analytical tools. R, a powerful open-source software package, was created in response to this issue. It has enjoyed explosive growth since its introduction, owing to its coherence, flexibility, and free availability. While it is a valuable tool for students who are first learning statistics, proper introductory materials are needed for its adoption.
Using R for Introductory Statistics fills this gap in the literature, making the software accessible to the introductory student. The author presents a self-contained...
Doing for R what Everitt's other Handbooks have done for S-PLUS, STATA, SPSS, and SAS, A Handbook of Statistical Analyses Using R presents straightforward, self-contained descriptions of how to perform a variety of statistical analyses in the R environment.
From simple inference to recursive partitioning and cluster analysis, eminent experts Everitt and Hothorn lead you methodically through the steps, commands, and interpretation of the results, addressing theory and statistical background only when useful or necessary.
They begin with an introduction to R, discussing the syntax, general operators, and basic data manipulation while...
This tutorial manual provides a comprehensive introduction to R, a software package for statistical computing and graphics.
R supports a wide range of statistical techniques and is easily extensible via user-defined functions. One of R's strengths is the ease with which publication-quality plots can be produced in a wide variety of formats.
This is a printed edition of the tutorial documentation from the R distribution, with additional examples, notes and corrections. It is based on R version 2.9.0, released April 2009. R is free software, distributed under the terms of the GNU General Public License (GPL). It can be used with...
R in Action, Second Edition presents both the R language and the examples that make it so useful for business developers. Focusing on practical solutions, the book offers a crash course in statistics and covers elegant methods for dealing with messy and incomplete data that are difficult to analyze using traditional methods. You'll also master R's extensive graphical capabilities for exploring and presenting data visually. And this expanded second edition includes new chapters on time series analysis, cluster analysis, and classification methodologies, including decision trees, random forests, and support vector machines...
Begin developing your mastery of the powerful R programming language. Become comfortable with the R environment and learn how to find ways for R to fulfill your data needs. With author Barton Poulson as your guide, you will soon be making use of this flexible, unparalleled tool. Unprecedented control of your data is within your grasp!
Analyzing your data is vital for success. Begin mastering statistical analysis with R Succinctly.
This is a textbook for an undergraduate course in probability and statistics. The approximate prerequisites are two or three semesters of calculus and some linear algebra. Students attending the class include mathematics, engineering, and computer science majors.
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...
We are in the era of big data. There are essentially two reasons why people gather increasing volumes of data: first, they think some valuable assets are implicitly coded within them, and second computer technology enables effective data storage at reduced costs.
The procedure for finding useful patterns in data is known by different names in different communities but more and more, the set of computational techniques and tools to support the modelling of large amount of data is grouped under the label of machine learning.
This book aims to present the statistical foundations of machine learning intended as the discipline which deals...
R is the world's most popular language for developing statistical software: Archaeologists use it to track the spread of ancient civilizations, drug companies use it to discover which medications are safe and effective, and actuaries use it to assess financial risks and keep economies running smoothly.
This book takes you on a guided tour of software development with R, from basic types and data structures to advanced topics like closures, recursion, and anonymous functions. No statistical knowledge is required, and your programming skills can range from hobbyist to pro.
At its core, this is an introductory statistics textbook pitched...
This is a textbook on data analysis methods, intended for advance undergraduate students who have already taken classes in probability, mathematical statistics, and linear regression. It began as the lecture notes for 36-402 at Carnegie Mellon University.
Every subject covered here can be profitably studied using vastly more sophisticated techniques; that's why this is advanced data analysis from an elementary point of view.
The book also presumes that you can read and write simple functions in R. If you are lacking in any of these areas, this book is not really for you, at least not now
Data science has taken the world by storm. Every field of study and area of business has been affected as people increasingly realize the value of the incredible quantities of data being generated. But to extract value from those data, one needs to be trained in the proper data science skills. The R programming language has become the de facto programming language for data science. Its flexibility, power, sophistication, and expressiveness have made it an invaluable tool for data scientists around the world. This book is about the fundamentals of R programming. You will get started with the basics of the language, learn how to...
Part 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 in physical science, engineering, social science, and business applications. The book incorporates several improvements that reflect how the world of R has greatly expanded since the publication of the first edition.
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