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＜電子ブック＞
Quantitative Epidemiology / by Xinguang Chen
(Emerging Topics in Statistics and Biostatistics. ISSN:25247743)

版	1st ed. 2021.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2021
本文言語	英語
大きさ	XX, 336 p. 200 illus., 170 illus. in color : online resource
著者標目	*Chen, Xinguang author SpringerLink (Online service)
件　名	LCSH:Statistics  LCSH:Epidemiology LCSH:Biometry LCSH:Public health FREE:Applied Statistics FREE:Epidemiology FREE:Biostatistics FREE:Public Health
一般注記	1. Introduction to Quantitative Epidemiology -- 2. Characters, Variables, Data, and Information -- 3. Quantitative Descriptive Epidemiology -- 4. Causal Exploration with Bivariate Analysis -- 5. Confirmation with Multiple Linear Regression -- 6. Multivariate Analyses of Categorical and Counting Data -- 7. Multivariate Analysis of Time to Event Data -- 8. Simultaneous Analysis of Two Correlated Predictors -- 9. Special Issues with Quantitative Epidemiology -- 10. Power Analysis This book is designed to train graduate students across disciplines within the fields of public health and medicine, with the goal of guiding them in the transition to independent researchers. It focuses on theories, principles, techniques, and methods essential for data processing and quantitative analysis to address medical, health, and behavioral challenges. Students will learn to access to existing data and process their own data, quantify the distribution of a medical or health problem to inform decision making; to identify influential factors of a disease/behavioral problem; and to support health promotion and disease prevention. Concepts, principles, methods and skills are demonstrated with SAS programs, figures and tables generated from real, publicly available data. In addition to various methods for introductory analysis, the following are featured, including 4-dimensional measurement of distribution and geographic mapping, multiple linear and logistic regression, Poissonregression, Cox regression, missing data imputing, and statistical power analysis. HTTP:URL=https://doi.org/10.1007/978-3-030-83852-2

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