R Companion to Elementary Applied Statistics PDF Download

Author: Christopher Hay-Jahans
Publisher: CRC Press
ISBN: 042982727X
Category : Mathematics
Languages : en
Pages : 358

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Book Description
The R Companion to Elementary Applied Statistics includes traditional applications covered in elementary statistics courses as well as some additional methods that address questions that might arise during or after the application of commonly used methods. Beginning with basic tasks and computations with R, readers are then guided through ways to bring data into R, manipulate the data as needed, perform common statistical computations and elementary exploratory data analysis tasks, prepare customized graphics, and take advantage of R for a wide range of methods that find use in many elementary applications of statistics. Features: Requires no familiarity with R or programming to begin using this book. Can be used as a resource for a project-based elementary applied statistics course, or for researchers and professionals who wish to delve more deeply into R. Contains an extensive array of examples that illustrate ideas on various ways to use pre-packaged routines, as well as on developing individualized code. Presents quite a few methods that may be considered non-traditional, or advanced. Includes accompanying carefully documented script files that contain code for all examples presented, and more. R is a powerful and free product that is gaining popularity across the scientific community in both the professional and academic arenas. Statistical methods discussed in this book are used to introduce the fundamentals of using R functions and provide ideas for developing further skills in writing R code. These ideas are illustrated through an extensive collection of examples. About the Author: Christopher Hay-Jahans received his Doctor of Arts in mathematics from Idaho State University in 1999. After spending three years at University of South Dakota, he moved to Juneau, Alaska, in 2002 where he has taught a wide range of undergraduate courses at University of Alaska Southeast.

Author: Danney Rasco
Publisher: SAGE Publications
ISBN: 1071806300
Category : Social Science
Languages : en
Pages : 360

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Book Description
An R Companion for Applied Statistics I: Basic Bivariate Techniques breaks the language of the R software down into manageable chunks in order to help students learn how to use it. R is a powerful, flexible, and free tool. However, the flexibility—which eventually becomes a great asset—can make the initial learning curve appear steep. This book introduces a few key aspects of the R tool. As readers become comfortable with these aspects, they develop a foundation from which to more thoroughly explore R and the packages available for it. This introduction does not explain every possible way to analyze data or perform a specific type of analysis. Rather, it focuses on the analyses that are traditionally included in an undergraduate statistics course and provides one or two ways to run these analyses in R. Datasets and scripts to run the examples are provided on an accompanying website. The book has been designed to be an R companion to Warner′s Applied Statistics I, Third Edition, and includes end-of-chapter instructions for replicating the examples from that book in R. However, this text can also be used as a stand-alone R guide, without reference to the Warner text.

Author: Danney Rasco
Publisher: SAGE Publications
ISBN: 1071815628
Category : Psychology
Languages : en
Pages : 289

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Book Description
An R Companion for Applied Statistics II: Multivariable and Multivariate Techniques breaks the language of the R software down into manageable chunks in order to help students learn how to use R to analyze multivariate data. The book has been designed to be an R companion to Rebecca M. Warner′s Applied Statistics II: Third Edition, and includes end-of-chapter instructions for replicating the examples from that book in R.

Author: Christopher Hay-Jahans
Publisher: CRC Press
ISBN: 0429827261
Category : Mathematics
Languages : en
Pages : 419

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Book Description
The R Companion to Elementary Applied Statistics includes traditional applications covered in elementary statistics courses as well as some additional methods that address questions that might arise during or after the application of commonly used methods. Beginning with basic tasks and computations with R, readers are then guided through ways to bring data into R, manipulate the data as needed, perform common statistical computations and elementary exploratory data analysis tasks, prepare customized graphics, and take advantage of R for a wide range of methods that find use in many elementary applications of statistics. Features: Requires no familiarity with R or programming to begin using this book. Can be used as a resource for a project-based elementary applied statistics course, or for researchers and professionals who wish to delve more deeply into R. Contains an extensive array of examples that illustrate ideas on various ways to use pre-packaged routines, as well as on developing individualized code. Presents quite a few methods that may be considered non-traditional, or advanced. Includes accompanying carefully documented script files that contain code for all examples presented, and more. R is a powerful and free product that is gaining popularity across the scientific community in both the professional and academic arenas. Statistical methods discussed in this book are used to introduce the fundamentals of using R functions and provide ideas for developing further skills in writing R code. These ideas are illustrated through an extensive collection of examples. About the Author: Christopher Hay-Jahans received his Doctor of Arts in mathematics from Idaho State University in 1999. After spending three years at University of South Dakota, he moved to Juneau, Alaska, in 2002 where he has taught a wide range of undergraduate courses at University of Alaska Southeast.

Author: Justin C. Touchon
Publisher: Oxford University Press
ISBN: 0192640127
Category : Science
Languages : en
Pages : 334

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Book Description
The statistical analyses that students of the life-sciences are being expected to perform are becoming increasingly advanced. Whether at the undergraduate, graduate, or post-graduate level, this book provides the tools needed to properly analyze your data in an efficient, accessible, plainspoken, frank, and occasionally humorous manner, ensuring that readers come away with the knowledge of which analyses they should use and when they should use them. The book uses the statistical language R, which is the choice of ecologists worldwide and is rapidly becoming the 'go-to' stats program throughout the life-sciences. Furthermore, by using a single, real-world dataset throughout the book, readers are encouraged to become deeply familiar with an imperfect but realistic set of data. Indeed, early chapters are specifically designed to teach basic data manipulation skills and build good habits in preparation for learning more advanced analyses. This approach also demonstrates the importance of viewing data through different lenses, facilitating an easy and natural progression from linear and generalized linear models through to mixed effects versions of those same analyses. Readers will also learn advanced plotting and data-wrangling techniques, and gain an introduction to writing their own functions. Applied Statistics with R is suitable for senior undergraduate and graduate students, professional researchers, and practitioners throughout the life-sciences, whether in the fields of ecology, evolution, environmental studies, or computational biology.

Author: Christopher Hay-Jahans
Publisher: CRC Press
ISBN: 1439873658
Category : Mathematics
Languages : en
Pages : 374

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Book Description
Focusing on user-developed programming, An R Companion to Linear Statistical Models serves two audiences: those who are familiar with the theory and applications of linear statistical models and wish to learn or enhance their skills in R; and those who are enrolled in an R-based course on regression and analysis of variance. For those who have never used R, the book begins with a self-contained introduction to R that lays the foundation for later chapters. This book includes extensive and carefully explained examples of how to write programs using the R programming language. These examples cover methods used for linear regression and designed experiments with up to two fixed-effects factors, including blocking variables and covariates. It also demonstrates applications of several pre-packaged functions for complex computational procedures.

Author: Chelsea Myers
Publisher: CRC Press
ISBN: 1000329895
Category : Education
Languages : en
Pages : 185

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Book Description
Project-Based R Companion to Introductory Statistics is envisioned as a companion to a traditional statistics or biostatistics textbook, with each chapter covering traditional topics such as descriptive statistics, regression, and hypothesis testing. However, unlike a traditional textbook, each chapter will present its material using a complete step-by-step analysis of a real publicly available dataset, with an emphasis on the practical skills of testing assumptions, data exploration, and forming conclusions. The chapters in the main body of the book include a worked example showing the R code used at each step followed by a multi-part project for students to complete. These projects, which could serve as alternatives to traditional discrete homework problems, will illustrate how to "put the pieces together" and conduct a complete start-to-finish data analysis using the R statistical software package. At the end of the book, there are several projects that require the use of multiple statistical techniques that could be used as a take-home final exam or final project for a class. Key features of the text: Organized in chapters focusing on the same topics found in typical introductory statistics textbooks (descriptive statistics, regression, two-way tables, hypothesis testing for means and proportions, etc.) so instructors can easily pair this supplementary material with course plans Includes student projects for each chapter which can be assigned as laboratory exercises or homework assignments to supplement traditional homework Features real-world datasets from scientific publications in the fields of history, pop culture, business, medicine, and forensics for students to analyze Allows students to gain experience working through a variety of statistical analyses from start to finish The book is written at the undergraduate level to be used in an introductory statistical methods course or subject-specific research methods course such as biostatistics or research methods for psychology or business analytics. Author After a 10-year career as a research biostatistician in the Department of Ophthalmology and Visual Sciences at the University of Wisconsin-Madison, Chelsea Myers teaches statistics and biostatistics at Rollins College and Valencia College in Central Florida. She has authored or co-authored more than 30 scientific papers and presentations and is the creator of the MCAT preparation website MCATMath.com.

Author: Nancy Glenn Griesinger
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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Book Description
Elementary Statistics: A Guide to Data Analysis Using R provides students with an introduction to both the field of statistics and R, one of the most widely used languages for statistical computing, analysis, and graphing in a variety of fields, including the sciences, finance, banking, health care, e-commerce, and marketing. Part I provides an overview of both statistics and R. Part II focuses on descriptive statistics and probability. In Part III, students learn about discrete and continuous probability distributions with chapters addressing probability distributions, binominal probability distributions, and normal probability distributions. Part IV speaks to statistical inference with content covering confidence intervals, hypothesis testing, chi-square tests and F-distributions. The final part explores additional statistical inference and assumptions, including correlation, regression, and nonparametric statistics. Helpful appendices provide students with an index of terminology, an index of applications, a glossary of symbols, and a guide to the most common R commands. Elementary Statistics is an ideal resource for introductory courses in undergraduate statistics, graduate statistics, and data analysis across the disciplines. Nancy L. Glenn Griesinger is a tenured professor of mathematics and the founder and CEO of Mobile Math, LLC. She has taught statistics and mathematics for 20 years at universities in Texas, South Carolina, and California. She holds a Ph.D. in statistics from Rice University. Daniel Vrinceanu is a tenured professor of physics at Texas Southern University. He holds a Ph.D. in theoretical atomic physics from the Georgia Institute of Technology. Monica C. Jackson is the deputy provost and dean of faculty at American University. She is also a tenured professor of mathematics and statistics. She holds a Ph.D. in applied mathematics and scientific computation from the University of Maryland, College Park. William C. Howell is a professorial lecturer in the Department of Mathematics and Statistics at American University. He holds a Ph.D. in applied mathematics and scientific computing from the University of Maryland, College Park.

Author: Gareth James
Publisher: Springer Nature
ISBN: 3031387473
Category : Mathematics
Languages : en
Pages : 617

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Book Description
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.

Author: Dieter Rasch
Publisher: John Wiley & Sons
ISBN: 1119551528
Category : Mathematics
Languages : en
Pages : 509

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Book Description
Instructs readers on how to use methods of statistics and experimental design with R software Applied statistics covers both the theory and the application of modern statistical and mathematical modelling techniques to applied problems in industry, public services, commerce, and research. It proceeds from a strong theoretical background, but it is practically oriented to develop one's ability to tackle new and non-standard problems confidently. Taking a practical approach to applied statistics, this user-friendly guide teaches readers how to use methods of statistics and experimental design without going deep into the theory. Applied Statistics: Theory and Problem Solutions with R includes chapters that cover R package sampling procedures, analysis of variance, point estimation, and more. It follows on the heels of Rasch and Schott's Mathematical Statistics via that book's theoretical background—taking the lessons learned from there to another level with this book’s addition of instructions on how to employ the methods using R. But there are two important chapters not mentioned in the theoretical back ground as Generalised Linear Models and Spatial Statistics. Offers a practical over theoretical approach to the subject of applied statistics Provides a pre-experimental as well as post-experimental approach to applied statistics Features classroom tested material Applicable to a wide range of people working in experimental design and all empirical sciences Includes 300 different procedures with R and examples with R-programs for the analysis and for determining minimal experimental sizes Applied Statistics: Theory and Problem Solutions with R will appeal to experimenters, statisticians, mathematicians, and all scientists using statistical procedures in the natural sciences, medicine, and psychology amongst others.