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[分享] Biostatistics in Public Health Using STATA 2016

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epiman 发表于 2016-5-23 15:49:47 | 显示全部楼层 |阅读模式

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) x$ ^/ ^6 L9 U+ t9 V' H+ zThis book demonstrates the use of Stata for statistical analyses common in public health research and in many other disciplines. For those new to Stata, the authors first provide an introduction to Stata's interface and to commands for descriptive statistics and graphics. In the discussions of statistical procedures that follow, readers will find extensive coverage of Stata's menus, dialog boxes, commands, and output. Topics include linear regression, analysis of variance, logistic regression, Poisson regression, survival analysis, multilevel mixed-effects models, and power and sample-size analysis.
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' V5 i) t9 {( `9 K. I0 `! I; j: gFeatures
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  • Covers various regression models and methods for selecting the best model
  • Includes examples for each model and exercise sets with solutions
  • Demonstrates how to use STATA v. 14 to solve problems
  • Provides essential knowledge for anyone in the public health field needing to perform statistical analysis
  • Presents high-level statistics in an accessible fashion across research fields in public health
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8 I* W% A  B7 u+ zSummary
' v4 v3 k. V  [' hStriking a balance between theory, application, and programming, Biostatistics in Public Health Using STATA is a user-friendly guide to applied statistical analysis in public health using STATA version 14. The book supplies public health practitioners and students with the opportunity to gain expertise in the application of statistics in epidemiologic studies.
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' s$ ~; y: \5 qThe book shares the authors’ insights gathered through decades of collective experience teaching in the academic programs of biostatistics and epidemiology. Maintaining a focus on the application of statistics in public health, it facilitates a clear understanding of the basic commands of STATA for reading and saving databases." d8 I& P; e/ X2 C5 Z1 }

+ A8 `0 Z* f) p% _The book includes coverage of data description, graph construction, significance tests, linear regression models, analysis of variance, categorical data analysis, logistic regression model, poisson regression model, survival analysis, analysis of correlated data, and advanced programming in STATA.
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5 u' p& i  }% O. J3 H5 kEach chapter is based on one or more research problems linked to public health. Additionally, every chapter includes exercise sets for practicing concepts and exercise solutions for self or group study. Several examples are presented that illustrate the applications of the statistical method in the health sciences using epidemiologic study designs.
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Presenting high-level statistics in an accessible manner across research fields in public health, this book is suitable for use as a textbook for biostatistics and epidemiology courses or for consulting the statistical applications in public health.8 X' @( u2 F" l, ^* P- b

1 q$ j' K; I$ ~- D# tFor readers new to STATA, the first three chapters should be read sequentially, as they form the basis of an introductory course to this software.* u/ T; I0 B, ]9 G! L0 x/ t- Q

' X- j$ r- Q: D4 ~: S: OPreface
, V4 F- X5 }' e* S7 O, nThis book is intended to serve as a guide to applied statistical analysis in public health using the Stata program. Our motivation for writing this book lies in our years of experience teaching biostatistics and epidemiology, particularly in the academic programs of biostatistics and epidemiology. The academic material is usually covered in biostatistics courses at the master’s and doctoral levels at schools of public health.
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The main focus of this book is the application of statistics in public health. Because of its user-friendliness, we used the Stata software package in the creation of the database and the statistical analysis that will be seen herein.! ~7 T3 H5 ~% ~, g
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This 12-chapter book can serve equally well as a textbook or as a source for consultation. Readers will be exposed to the following topics: Basic Commands, Data Description, Graph Construction, Significance Tests, Linear Regression Models, Analysis of Variance, Categorical Data Analysis, Logistic Regression Model, Poisson Regression Model, Survival Analysis, Analysis of Correlated Data, and Advanced Programming in Stata. Each chapter is based on one or more research problems linked to public health.
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' v6 d3 x: ^8 o, [2 SWe have started with the assumption that the readers of this book have taken at least a basic course in biostatistics and epidemiology. Further, for those readers who are new to Stata, the first three chapters should be read sequentially, as they form the basis of an introductory course to this software.

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Jasonbrilliant 发表于 2017-4-13 22:35:44 | 显示全部楼层
感谢楼主。顶上去。
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keith 发表于 2017-9-23 20:47:33 | 显示全部楼层
感觉不错的书,决定看一下,谢谢楼主提供资源
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qbc 发表于 2017-10-3 12:23:10 | 显示全部楼层
感谢楼主分享
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