Name: Statistical Thinking in Epidemiology (en Inglés)
Price: 2436.35 MXN
Availability: InStock
Author: Tu, Yu-Kang ; Gilthorpe, Mark
ISBN: 9781420099911

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portada Statistical Thinking in Epidemiology (en Inglés)

Formato

Libro Físico

Autor

Yu-Kang Tu

Mark Gilthorpe

Editorial

CRC Press

Año

2011

Idioma

Inglés

N° páginas

232

Encuadernación

Tapa Dura

Dimensiones

23.6 x 15.5 x 1.5 cm

Peso

0.39 kg.

ISBN13

9781420099911

Categorías

Epidemiología y estadística médica
Probabilidad y estadística

Statistical Thinking in Epidemiology (en Inglés)

Yu-Kang Tu (Autor) · Mark Gilthorpe (Autor) · CRC Press · Tapa Dura

Libro Físico

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Reseña del libro "Statistical Thinking in Epidemiology (en Inglés)"

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. 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.