This student-oriented manual describes how to use R in college science and mathematics courses. The manual features fully developed exercises based around the main precalculus analysis skills needed in the standard college general education courses in science and math. The exercises illustrate a wide variety of applications and subjects. The author presents applications drawn from all sciences and social sciences and includes the most often used features of R on a reference card in the back of the book. In addition, each chapter provides a set of computational challenges: exercises in R calculations that are designed to be performed alone or in groups.

This compact, accessibly written text prepares students for their experience of community-based learning. It is designed for students to read and reflect on independently or to foster discussion in class on their motivations and dispositions toward community engagement and service learning. It prepares students to work with diverse individuals, groups, and organizations that may be outside their prior experience. Faculty can use the book as a tool to deepen the educational experience of the course and enrich community engagement. This text is a guide to what’s involved in community-engaged learning, from understanding the pervasiveness of social, economic and environmental problems, to learning about how individuals and organizations in communities work to overcome them. Students will discover through a process of reflection how service connects to personal development and the content of their courses, builds their ability to engage with people different from themselves, and develops new life skills, all in the context of working with communities to overcome systemic injustice.Critical questions woven into each chapter prompt students to reflect on ideas and perspectives about social justice, community development, and their role in fostering them.The book concludes with case studies of students who have experienced the transformative power of community-engaged learning. The stories illustrate common themes inherent in the student experience, including listening to understand, challenging stereotypes, learning the nature of their role, and seeing the world through a new lens.A special feature of this book is the embedded QR codes that provide access, as students read the text, to online resources, and original and public videos that explore particular themes or perspectives more deeply. The authors also include text directed to faculty to provide ideas about framing their community-engaged course and integrating the book.

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.

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.

In the mid- late 1800s and early 1900s, Thomas Hardy produced a plethora of eclectic works that were considered too candid and even sacrilegious for their time. Hardy's publishing of fiction, drama, poetry, and the short story ranks him with Shakespeare, one of few other authors in the English language to write major works in more than one literary genre. Growing up, Hardy apprenticed as an architect but soon realized his true calling was writing. He based much of his work on his homeland and local culture in England, creating the fictional county of Wessex, the setting for most of his works. This companion explores the life of Hardy, examining his career and most important works. Ideal for high school and undergraduate students, as well as readers with a general interest in Hardy's life and works, this book takes a close look at Hardy's unconventional works and why he ultimately decided to abandon novel-writing in favor of his first love-poetry.

This book expertly guides the reader through all stages involved in undertaking quantitative psychological research, from accessing the relevant literature, through designing and conducting a study, analysing and interpreting data, and finally reporting the research. This third edition includes two new chapters - on preliminary checking of data and allowing for additional variables when comparing the means of different conditions - and expands on original topics such as choosing sample sizes and how to test for mediation effects. It also contains increased coverage of tests and further detail of techniques and terms which psychologists will meet when working with those in the medical professions. As the chapters focus on choosing appropriate statistical tests and how to interpret and report them (rather than the detailed calculations, which appear in appendices), the reader is able to gain an understanding of a test without being interrupted by the need to understand the complex mathematics behind it. In addition, for the first time, the book is accompanied by an online bank of multiple choice questions. The book helps readers to: Locate reports of relevant existing research Design research while adhering to ethical principles Identify various methods which can be used to ask questions or observe behaviour Choose appropriate samples Display and analyse findings numerically and graphically to test hypotheses Report psychological research in a variety of ways. As such, the book is suitable for psychology students and professionals at all levels, and is particularly useful to those working in Health and Clinical Psychology.

"Learning Statistics with R" covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software and adopting a light, conversational style throughout. The book discusses how to get started in R, and gives an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book. For more information (and the opportunity to check the book out before you buy!) visit http://ua.edu.au/ccs/teaching/lsr or http://learningstatisticswithr.com