Exploratory and Robust Data Analysis: A Modern Applied Statistics Guide Using SPSS and R

Exploratory and Robust Data Analysis: A Modern Applied Statistics Guide Using SPSS and R is an essential resource for students, researchers, and professionals seeking a comprehensive yet practical approach to modern statistical analysis. This book bridges traditional statistical methods with contemporary techniques, emphasizing exploratory and robust data analysis while integrating powerful computational tools such as R and SPSS. Designed for intermediate-level courses and research applications, the book begins with fundamental concepts in exploratory data analysis, graphical methods, and confirmatory statistical procedures. It then introduces robust statistical methods, including M-estimators, high breakdown estimators, bootstrap techniques, and Monte Carlo simulations, equipping readers with tools to handle complex and real-world data scenarios. Key topics include regression analysis, multiple linear models, nonparametric regression, and generalized linear models, ensuring broad applicability across disciplines. What sets this book apart is its emphasis on theoretical foundations and hands-on applications. Annotated computer sessions guide readers through statistical analysis, enabling them to apply techniques effectively while understanding their theoretical underpinnings. This book fosters an analytical mindset that encourages critical thinking and data-driven decision-making by combining classical statistical procedures with modern computational methods. With real-world datasets, practical exercises, and detailed software integration, this book is an indispensable guide for those looking to master data analysis in an era where statistical rigor and computational efficiency are paramount.

Table of Contents:
INTRODUCION. CHAPTER 1 EXPLORATORY DATA ANALYSIS FOR THE LOCATION MODEL. CHAPTER 2 COMBINING EXPLORATORY AND CONFIRMATORY DATA ANALYSIS FOR THE LOCATION MODEL. CHAPTER 3 ROBUST ESTIMATION IN THE LOCATION MODEL. CHAPTER 4 ROBUST ESTIMATION IN THE LOCATION MODEL: COMPUTER-INTENSIVE METHODS. CHAPTER 5 COMPARING TWO OR MORE GROUPS OF DATA: EXPLORATORY, CLASSICAL INFERENCE, AND OTHER FORMS OF ANALYSIS. CHAPTER 6 SIMPLE LINEAR REGRESSION: CLASSICAL, ROBUST, AND NONPARAMETRIC METHODS. CHAPTER 7 MULTIPLE LINEAR REGRESSION AND THE GENERAL LINEAR MODEL. CHAPTER 8 ANALYSIS OF NON-NORMAL DATA

About the Author :
Abdel-Salam G. Abdel-Salam is an associate professor of statistics at Qatar University and a chartered (CStat) and certified (PStat) statistician. He holds degrees from Cairo University (BS, MS) and Virginia Tech (MS, PhD) and also holds an MBA from Qatar University. He has taught at Cairo University, Virginia Tech, and Oklahoma State University and was Assistant Vice President (AVP) in risk management at JPMorgan Chase & Co. His expertise includes statistical modeling, quality control, and AI applications, with numerous publications and awards, including Qatar University’s Outstanding Faculty Service Award. Jeffrey B. Birch, a distinguished statistician, earned his PhD in biostatistics from the University of Washington (1978) and has been a professor at Virginia Tech since 1999. He directed graduate programs in statistics (2001–2016) and specializes in robust regression, nonparametric statistics, and applied data analysis. A recipient of multiple teaching awards, he is an active ASA and Sigma Xi member.