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Author: Tilman M. Davies Publisher: No Starch Press ISBN: 1593276516 Category : Computers Languages : en Pages : 833
Book Description
The Book of R is a comprehensive, beginner-friendly guide to R, the world’s most popular programming language for statistical analysis. Even if you have no programming experience and little more than a grounding in the basics of mathematics, you’ll find everything you need to begin using R effectively for statistical analysis. You’ll start with the basics, like how to handle data and write simple programs, before moving on to more advanced topics, like producing statistical summaries of your data and performing statistical tests and modeling. You’ll even learn how to create impressive data visualizations with R’s basic graphics tools and contributed packages, like ggplot2 and ggvis, as well as interactive 3D visualizations using the rgl package. Dozens of hands-on exercises (with downloadable solutions) take you from theory to practice, as you learn: –The fundamentals of programming in R, including how to write data frames, create functions, and use variables, statements, and loops –Statistical concepts like exploratory data analysis, probabilities, hypothesis tests, and regression modeling, and how to execute them in R –How to access R’s thousands of functions, libraries, and data sets –How to draw valid and useful conclusions from your data –How to create publication-quality graphics of your results Combining detailed explanations with real-world examples and exercises, this book will provide you with a solid understanding of both statistics and the depth of R’s functionality. Make The Book of R your doorway into the growing world of data analysis.
Author: Tilman M. Davies Publisher: No Starch Press ISBN: 1593276516 Category : Computers Languages : en Pages : 833
Book Description
The Book of R is a comprehensive, beginner-friendly guide to R, the world’s most popular programming language for statistical analysis. Even if you have no programming experience and little more than a grounding in the basics of mathematics, you’ll find everything you need to begin using R effectively for statistical analysis. You’ll start with the basics, like how to handle data and write simple programs, before moving on to more advanced topics, like producing statistical summaries of your data and performing statistical tests and modeling. You’ll even learn how to create impressive data visualizations with R’s basic graphics tools and contributed packages, like ggplot2 and ggvis, as well as interactive 3D visualizations using the rgl package. Dozens of hands-on exercises (with downloadable solutions) take you from theory to practice, as you learn: –The fundamentals of programming in R, including how to write data frames, create functions, and use variables, statements, and loops –Statistical concepts like exploratory data analysis, probabilities, hypothesis tests, and regression modeling, and how to execute them in R –How to access R’s thousands of functions, libraries, and data sets –How to draw valid and useful conclusions from your data –How to create publication-quality graphics of your results Combining detailed explanations with real-world examples and exercises, this book will provide you with a solid understanding of both statistics and the depth of R’s functionality. Make The Book of R your doorway into the growing world of data analysis.
Author: Hadley Wickham Publisher: "O'Reilly Media, Inc." ISBN: 1491910364 Category : Computers Languages : en Pages : 521
Book Description
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, big-picture 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 low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results
Author: R.G. Davies Publisher: Elsevier ISBN: 0323147879 Category : Reference Languages : en Pages : 505
Book Description
Computer Programming in Quantitative Biology covers the general background of Fortran coding and the more sophisticated computer programs likely to be encountered in quantitative biology. It discusses the application of over 40 appropriate and easily adaptable programming techniques to problems of major biological interest. Organized into 15 chapters, the book starts by providing an introductory outline of computer structure and function needed to appreciate many basic programming procedures. A chapter discusses some general principles underlying Fortran coding and the use of digital computers, with emphasis on major features of Fortran IV. Other chapters present short introduction to the statistical or mathematical techniques in each of the main sections under which program are described. These chapters also provide some aspects of matrix algebra that are essential for serious statistical programming and offer a general guide to efficiency in programming. All complete programs are accompanied by a flowchart and a detailed discussion. This book is a valuable source of information for biologists, computational biologists, research biologists, undergraduate students, and advanced or specialized students of biology.
Author: Neil Smalheiser Publisher: Academic Press ISBN: 0128113073 Category : Science Languages : en Pages : 284
Book Description
Data Literacy: How to Make Your Experiments Robust and Reproducible provides an overview of basic concepts and skills in handling data, which are common to diverse areas of science. Readers will get a good grasp of the steps involved in carrying out a scientific study and will understand some of the factors that make a study robust and reproducible.The book covers several major modules such as experimental design, data cleansing and preparation, statistical analysis, data management, and reporting. No specialized knowledge of statistics or computer programming is needed to fully understand the concepts presented. This book is a valuable source for biomedical and health sciences graduate students andresearchers, in general, who are interested in handling data to make their research reproducibleand more efficient. Presents the content in an informal tone and with many examples taken from the daily routine at laboratories Can be used for self-studying or as an optional book for more technical courses Brings an interdisciplinary approach which may be applied across different areas of sciences
Author: Ronald Deep Publisher: Academic Press ISBN: 0123694639 Category : Mathematics Languages : en Pages : 708
Book Description
Probability & Statistics with Integrated Software Routines is a calculus-based treatment of probability concurrent with and integrated with statistics through interactive, tailored software applications designed to enhance the phenomena of probability and statistics. The software programs make the book unique.The book comes with a CD containing the interactive software leading to the Statistical Genie. The student can issue commands repeatedly while making parameter changes to observe the effects. Computer programming is an excellent skill for problem solvers, involving design,
Author: Manfred Denker Publisher: Springer Science & Business Media ISBN: 9780817640316 Category : Mathematics Languages : en Pages : 550
Book Description
Integrates traditional statistical data analysis with new computational experimentation capabilities and concepts of algorithmic complexity and chaotic behavior in nonlinear dynamic systems, offering tools for the study of random phenomena occurring in engineering and the natural, life, and social sciences. Each chapter presents experiments, exercises, and projects using the Mathematica Uncertain Virtual Worlds software packages. Large and original real-life data sets are introduced and analyzed as a model for independent study. Includes brief tutorials on using Mathematica programs. Intended as a text for an introductory level statistics course. Prerequisites include calculus and basic computer programming. Annotation copyrighted by Book News, Inc., Portland, OR
Author: M. D. Edge Publisher: ISBN: 0198827628 Category : Mathematics Languages : en Pages : 318
Book Description
Focuses on detailed instruction in a single statistical technique, simple linear regression (SLR), with the goal of gaining tools, understanding, and intuition that can be applied to other contexts.
Author: Yoni Nazarathy Publisher: Springer Nature ISBN: 3030709019 Category : Computers Languages : en Pages : 527
Book Description
This monograph uses the Julia language to guide the reader through an exploration of the fundamental concepts of probability and statistics, all with a view of mastering machine learning, data science, and artificial intelligence. The text does not require any prior statistical knowledge and only assumes a basic understanding of programming and mathematical notation. It is accessible to practitioners and researchers in data science, machine learning, bio-statistics, finance, or engineering who may wish to solidify their knowledge of probability and statistics. The book progresses through ten independent chapters starting with an introduction of Julia, and moving through basic probability, distributions, statistical inference, regression analysis, machine learning methods, and the use of Monte Carlo simulation for dynamic stochastic models. Ultimately this text introduces the Julia programming language as a computational tool, uniquely addressing end-users rather than developers. It makes heavy use of over 200 code examples to illustrate dozens of key statistical concepts. The Julia code, written in a simple format with parameters that can be easily modified, is also available for download from the book’s associated GitHub repository online. See what co-creators of the Julia language are saying about the book: Professor Alan Edelman, MIT: With “Statistics with Julia”, Yoni and Hayden have written an easy to read, well organized, modern introduction to statistics. The code may be looked at, and understood on the static pages of a book, or even better, when running live on a computer. Everything you need is here in one nicely written self-contained reference. Dr. Viral Shah, CEO of Julia Computing: Yoni and Hayden provide a modern way to learn statistics with the Julia programming language. This book has been perfected through iteration over several semesters in the classroom. It prepares the reader with two complementary skills - statistical reasoning with hands on experience and working with large datasets through training in Julia.
Author: Tilman M. Davies
Author: Tilman M. Davies
Author: Oliver Eugene Dial
Author: Hadley Wickham
Author: R.G. Davies
Author: Neil Smalheiser
Author: Ronald Deep
Author: Manfred Denker
Author: M. D. Edge
Author: Yoni Nazarathy
Author: S. P. Agrawal