A Primer for Using Open Source R Software for Accessibility and Visualization
Author: Alon Friedman
Publisher: Rowman & Littlefield
Category: Language Arts & Disciplines
Statistics for Library and Information Services, written for non-statisticians, provides logical, user-friendly, and step-by-step instructions to make statistics more accessible for students and professionals in the field of Information Science. It emphasizes concepts of statistical theory and data collection methodologies, but also extends to the topics of visualization creation and display, so that the reader will be able to better conduct statistical analysis and communicate his/her findings. The book is tailored for information science students and professionals. It has specific examples of dataset sets, scripts, design modules, data repositories, homework assignments, and a glossary lexicon that matches the field of Information Science. The textbook provides a visual road map that is customized specifically for Information Science instructors, students, and professionals regarding statistics and visualization. Each chapter in the book includes full-color illustrations on how to use R for the statistical model that particular chapter will cover. This book is arranged in 17 chapters, which are organized into five main sections: the first section introduces research design and data collection; the second section discusses basic statistical concepts, including descriptive, bivariate, time series, and regression analyses; section 3 covers the subject of visualization creation using Open Source R; section 4 covers decision making from the analysis; and the last section provides examples and references. Every chapter illustrates how to use Open Source R and features two subsections for the major ideas of the chapter: its statistical model and its visual representation. The statistical model captures the main statistical formulas/theories covered in each chapter, while the visual representation addresses the subject of the types of visualization that are produced from the statistical analysis model covered in that particular chapter. Don’t miss the book’s companion Web site at www.statisticsforlis.org
Research has identified the importance of helping students develop the ability to monitor their own comprehension and to make their thinking processes explicit, and indeed demonstrates that metacognitive teaching strategies greatly improve student engagement with course material. This book -- by presenting principles that teachers in higher education can put into practice in their own classrooms -- explains how to lay the ground for this engagement, and help students become self-regulated learners actively employing metacognitive and reflective strategies in their education. Key elements include embedding metacognitive instruction in the content matter; being explicit about the usefulness of metacognitive activities to provide the incentive for students to commit to the extra effort; as well as following through consistently. Recognizing that few teachers have a deep understanding of metacognition and how it functions, and still fewer have developed methods for integrating it into their curriculum, this book offers a hands-on, user-friendly guide for implementing metacognitive and reflective pedagogy in a range of disciplines. Offering seven practitioner examples from the sciences, technology, engineering and mathematics (STEM) fields, the social sciences and the humanities, along with sample syllabi, course materials, and student examples, this volume offers a range of strategies for incorporating these pedagogical approaches in college classrooms, as well as theoretical rationales for the strategies presented. By providing successful models from courses in a broad spectrum of disciplines, the editors and contributors reassure readers that they need not reinvent the wheel or fear the unknown, but can instead adapt tested interventions that aid learning and have been shown to improve both instructor and student satisfaction and engagement.
Fulfilling the need for a practical user’s guide, Statistics in MATLAB: A Primer provides an accessible introduction to the latest version of MATLAB® and its extensive functionality for statistics. Assuming a basic knowledge of statistics and probability as well as a fundamental understanding of linear algebra concepts, this book: Covers capabilities in the main MATLAB package, the Statistics Toolbox, and the student version of MATLAB Presents examples of how MATLAB can be used to analyze data Offers access to a companion website with data sets and additional examples Contains figures and visual aids to assist in application of the software Explains how to determine what method should be used for analysis Statistics in MATLAB: A Primer is an ideal reference for undergraduate and graduate students in engineering, mathematics, statistics, economics, biostatistics, and computer science. It is also appropriate for a diverse professional market, making it a valuable addition to the libraries of researchers in statistics, computer science, data mining, machine learning, image analysis, signal processing, and engineering.