Statistical Thinking in Epidemiology

Author: Yu-Kang Tu
Edition: 1
Publisher: Chapman and Hall/CRC
Binding: Hardcover
ISBN: 1420099914

Statistical Thinking in Epidemiology

While biomedical researchers may be able to follow instructions in the manuals accompanying the statistical software packages, they do not always have sufficient knowledge to choose the appropriate statistical methods and correctly interpret their results. Medical books Statistical Thinking in Epidemiology. Statistical Thinking in Epidemiology examines common methodological and statistical problems in the use of correlation and regression in medical and epidemiological research: mathematical coupling, regression to the mean, collinearity, the reversal paradox, and statistical interaction.

Statistical Thinking in Epidemiology is about thinking statistically when looking at problems in epidemiology. The authors focus on several methods and look at them in detail: specific examples in epidemiology illustrate how different model specifications can imply different causal relationships amongst variables, and model interpretation is undertaken with appropriate consideration of the context of implicit or explicit causal relationships. This book is intended for applied statisticians and epidemiologists, but can also be very useful for clinical and applied health researchers who want to have a better understanding of statistical thinking Medical books Statistical Thinking In Epidemiology By Tu Yu-kang Hardcover Book. Store Search search Title, ISBN and Author Statistical Thinking in Epidemiology by Tu Yu-Kang Estimated delivery 3-12 business days Format Hardcover Condition Brand New 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

Download link for Statistical Thinking in Epidemiology

Store Search search Title, ISBN and Author Statistical Thinking in Epidemiology by Tu Yu-Kang Estimated delivery 3-12 business days Format Hardcover Condition Brand New 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

author mark s gilthorpe author yu kang tu format hardback language english publication year 25 07 2011 subject mathematics sciences subject 2 mathematics title statistical thinking in epidemiology author tu yu kang author gilthorpe mark s author publisher chapman hall publication date nov 25 2010 pages 224 binding hardcover edition 1 st dimensions 6 46 wx 9 45 hx 0 83 d isbn 1420099914 subject mathematics probability statistics general description while biomedical researchers may be able to fo

Statistical Thinking in Epidemiology : Hardback : Taylor & Francis Ltd : 9781420099911 : 1420099914 : 25 Jul 2011 : Provides insights into the problems in statistical modeling and offers alternative strategies to approach the problems. Emphasizing the importance of statistical thinking behind all analyses, this book uses examples in epidemiology to illustrate different model specifications that can imply different sets of causal relationships between variables.

Taylor & Francis Ltd | 2011 | 231 pages | ISBN-13: 9781420099911 | ISBN-10: 1420099914 | You save 10%

Medical Book Statistical Thinking in Epidemiology

Statistical Thinking in Epidemiology examines common methodological and statistical problems in the use of correlation and regression in medical and epidemiological research: mathematical coupling, regression to the mean, collinearity, the reversal paradox, and statistical interaction.

Statistical Thinking in Epidemiology is about thinking statistically when looking at problems in epidemiology. The authors focus on several methods and look at them in detail: specific examples in epidemiology illustrate how different model specifications can imply different causal relationships amongst variables, and model interpretation is undertaken with appropriate consideration of the context of implicit or explicit causal relationships. This book is intended for applied statisticians and epidemiologists, but can also be very useful for clinical and applied health researchers who want to have a better understanding of statistical thinking.

Throughout the book, statistical software packages R and Stata are used for general statistical modeling, and Amos and Mplus are used for structural equation modeling.