Exploratory and Robust Data Analysis (eBook, PDF)
A Modern Applied Statistics Guide Using SPSS and R
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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 confi...
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.
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.
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