Regression Models for Categorical and Limited Dependent Variables
por
J. Scott Long
Ainda sem avaliações
Formato
Capa dura
Páginas
328
Idioma
Inglês
Publicado
Jan 9, 1997
Editora
SAGE Publications, Inc
ISBN-10
0803973748
ISBN-13
9780803973749
Descrição
J. Scott Long presents a comprehensive exploration of the complex world of categorical and limited dependent variables. This work stands out for its clarity and systematic approach, making it accessible to both seasoned statisticians and those new to the field. Long delves into various regression models, explaining their relevance and application in real-world scenarios, while ensuring that the underlying concepts are thoroughly understood.
The author emphasizes the importance of context when analyzing data, offering insights into the nuances of different methodologies. He effectively combines theoretical frameworks with practical examples, empowering readers to apply their newfound knowledge to empirical research. Long's articulate writing style and attention to detail foster a deep appreciation for the intricacies involved in statistical modeling.
Overall, this book serves as a valuable resource for researchers and practitioners alike, bridging gaps in understanding and equipping them with essential tools to tackle complex data challenges. It encourages readers to think critically about their analytical choices, ultimately enhancing their investigative capabilities.
The author emphasizes the importance of context when analyzing data, offering insights into the nuances of different methodologies. He effectively combines theoretical frameworks with practical examples, empowering readers to apply their newfound knowledge to empirical research. Long's articulate writing style and attention to detail foster a deep appreciation for the intricacies involved in statistical modeling.
Overall, this book serves as a valuable resource for researchers and practitioners alike, bridging gaps in understanding and equipping them with essential tools to tackle complex data challenges. It encourages readers to think critically about their analytical choices, ultimately enhancing their investigative capabilities.
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