Applied Statistical Analysis with SPSS (eBook, ePUB)
Definitive Reference for Developers and Engineers
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"Applied Statistical Analysis with SPSS" "Applied Statistical Analysis with SPSS" is an indispensable resource for advanced practitioners, data analysts, and researchers seeking a comprehensive and practical guide to harnessing the full power of SPSS for statistical analysis. Meticulously structured, the book opens with an advanced technical overview of SPSS's architecture, computational engine, and data handling strategies, including integration with Python and R for maximal flexibility. Readers will learn how to automate data import and ETL workflows, optimize performance for large-scale ana...
"Applied Statistical Analysis with SPSS"
"Applied Statistical Analysis with SPSS" is an indispensable resource for advanced practitioners, data analysts, and researchers seeking a comprehensive and practical guide to harnessing the full power of SPSS for statistical analysis. Meticulously structured, the book opens with an advanced technical overview of SPSS's architecture, computational engine, and data handling strategies, including integration with Python and R for maximal flexibility. Readers will learn how to automate data import and ETL workflows, optimize performance for large-scale analytics, and fully customize their SPSS workspace through scripting and modular design.
The book delves deeply into every step of the analytical process, from data preparation and quality assurance to sophisticated exploratory data analysis and visualization techniques. It covers the full spectrum of inferential statistical methods, advanced regression, and predictive modeling-including linear, hierarchical, logistic, and regularized models. Techniques for dimension reduction, cluster analysis, multivariate methods, and specialized modeling such as survival analysis, time series forecasting, and generalized linear models are presented with clear guidance on both theory and SPSS implementation. Throughout, the text emphasizes best practices, reproducibility, data integrity, and auditability, aligning statistical rigor with real-world data challenges.
With a dedicated focus on workflow automation, extension building, and seamless integration with external systems, this book ensures readers can streamline operations and scale up analyses efficiently. It concludes with insightful case studies from healthcare, finance, and the social sciences, along with in-depth discussions of ethics, compliance, and future trends in statistical analysis. Whether advancing research in academia or driving insights in industry, "Applied Statistical Analysis with SPSS" equips professionals with robust methods, reproducible workflows, and the expertise to extract actionable value from complex data.
"Applied Statistical Analysis with SPSS" is an indispensable resource for advanced practitioners, data analysts, and researchers seeking a comprehensive and practical guide to harnessing the full power of SPSS for statistical analysis. Meticulously structured, the book opens with an advanced technical overview of SPSS's architecture, computational engine, and data handling strategies, including integration with Python and R for maximal flexibility. Readers will learn how to automate data import and ETL workflows, optimize performance for large-scale analytics, and fully customize their SPSS workspace through scripting and modular design.
The book delves deeply into every step of the analytical process, from data preparation and quality assurance to sophisticated exploratory data analysis and visualization techniques. It covers the full spectrum of inferential statistical methods, advanced regression, and predictive modeling-including linear, hierarchical, logistic, and regularized models. Techniques for dimension reduction, cluster analysis, multivariate methods, and specialized modeling such as survival analysis, time series forecasting, and generalized linear models are presented with clear guidance on both theory and SPSS implementation. Throughout, the text emphasizes best practices, reproducibility, data integrity, and auditability, aligning statistical rigor with real-world data challenges.
With a dedicated focus on workflow automation, extension building, and seamless integration with external systems, this book ensures readers can streamline operations and scale up analyses efficiently. It concludes with insightful case studies from healthcare, finance, and the social sciences, along with in-depth discussions of ethics, compliance, and future trends in statistical analysis. Whether advancing research in academia or driving insights in industry, "Applied Statistical Analysis with SPSS" equips professionals with robust methods, reproducible workflows, and the expertise to extract actionable value from complex data.
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