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
Rezensionen / Stimmen
Dr. Friedman's book arrives at the right time as library and information professionals begin to grapple with the complexities of big data. This well-written and clearly organized primer will be a valuable addition to the LIS curriculum - it is clearly the moment for us to have a textbook that introduces statistics and an open source statistical computing language for our students and for information professionals from an "insider" who knows our field well. -- Howard Rosenbaum, Professor of Information Science and Associate Dean for Graduate Studies, Department of Information and Library Science, Indiana University
Sprache
Verlagsort
Verlagsgruppe
Bloomsbury Publishing Plc
Zielgruppe
Für höhere Schule und Studium
Für Beruf und Forschung
US School Grade: From College Freshman to College Graduate Student
Maße
Höhe: 237 mm
Breite: 158 mm
Dicke: 27 mm
Gewicht
ISBN-13
978-1-4422-4992-9 (9781442249929)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Alon Friedman is an Assistant Professor at the School of Information at the University of South Florida. He teaches Introduction to Visualization and Big Data to undergraduate and graduate students. Previously he has taught introductory and advanced statistics undergraduate, graduate, and PhD students for 10 years across the New York City region. His research interests and expertise focus on classification and visualization using Open Source R. Alon also has worked as a web programmer in NYC and Tel Aviv.
Part I INTRODUCTION TO STATISTICS
CHAPTER 1 Introduction
CHAPTER 2 Research Design
CHAPTER 3 Data (Types and Collection Methods)
CHAPTER 4 How to Run R
Part II MAKING SENSE OF STATISTICS
CHAPTER 5 Descriptive statistics
CHAPTER 6 Bivariate Statistics
CHAPTER 7 Probability Theory
CHAPTER 8 Random Variables and Probability Distributions
CHAPTER 9 Sampling Distributions
CHAPTER 10 Confidence Interval Estimation
CHAPTER 11 Fundamentals of Hypothesis Testing
CHAPTER 12 Correlation and Regression
CHAPTER 13 Analysis of Variances and Chi-square Tests
CHAPTER 14 Time Series and Predictive Analytics
Part III VISUALIZATION IN R
CHAPTER 15 Visualization Display
CHAPTER 16 Advanced Visualization Display
CHAPTER 17 Applying visualization to statistics analysis
APPENDIX A Frequency used formulas used in this book
APPENDIX B Frequency R commands
APPENDIX C References
