Description:
Addressing issues that have plagued researchers throughout the last decade, this book provides new insights into the many existing problems in statistical modeling and offers several alternative strategies to approach these problems. Emphasizing the importance of statistical thinking behind all analyses, the authors use specific examples in epidemiology to illustrate different model specifications that can imply different sets of causal relationships between variables. Each model is interpreted with regard to the context of implicit or explicit causal relationships. The authors also use vector geometry where applicable to provide an intuitive understanding of important statistical concepts.
Review Quotes:
"... this book is enjoyable ... it encourages readers to conceptualize statistical thinking in a graphically entertaining way. ... one of the impressive works of the book lies in visualization of statistically important concepts.... I would recommend this book to diverse audiences. ... the book provides novel insight on how one can develop the core concepts from scratch via graphical concepts, which will definitely be beneficial. Bearing in mind the geometrical concepts from this book, statistical thinking of more complicated models is readily welcomed."
--Journal of Agricultural, Biological, and Environmental Statistics, Volume 20, Number 2, 2015
"There are extensive references to the literature, both in statistics and in medicine. This is a demanding text, not mathematically but for the subtlety of the issues canvassed, some of which remain controversial. Should any reader come to this text thinking that the interpretation of regression results is a simple matter, they will be quickly disabused."
--International Statistical Review, 2013
"The graphical explanations proposed are quite convincing and these tools should be more exploited in statistical classes."
--Sophie Donnet, Université Paris-Dauphine, CHANCE, 25.4