Quantitative Methods in Population Health : Extensi*** of Ordinary Regression人口健康的定量方法 mobi 下载 网盘 caj lrf pdf txt 阿里云

Each topic starts with an explanation of the theoretical background necessary to allow full understanding of the technique and to facilitate future learning of more advanced or new methods and software。

Explanati*** are designed to assume as little background in mathematics and statistical theory as possible， except that some knowledge of calculus is necessary for certain parts。

SAS commands are provided for applying the methods。 （PROC REG，PROC MIXED， and PROC GENMOD）。

All secti*** contain real life examples， mostly from epidemiologic research First chapter includes a SAS refresher。

Preface

Acknowledgments

Acronyms

Introduction

　I.1 Newborn Lung Project

　I.2 Wisc***in Diabetes Registry

　I.3 Wisc***in Sleep Cohort Study

Suggested Reading

1 Review of Ordinary Linear Regression and Its Assumpti***

　1.1 The Ordinary Linear Regression Equation and Its Assumpti***

　　1.1.1 Straight-Line Relati***hip

　　1.1.2 Equal Variance Assumption

　　1.1.3 Normality Assumption

　　1.1.4 Independence Assumption

　1.2 A Note on How the Least-Squares Estimators are Obtained

　Output Packet Ⅰ：Examples of Ordinary Regression Analyses

2 The Maximum Likelihood Approach to Ordinary Regression

　2.1 Maximum Likelihood Estimation

　2.2 Example

　2.3 Properties of Maximum Likelihood Estimators

　2.4 How to Obtain a Residual Plot with PROC MIXED

　Output Packet Ⅱ：Using PROC MIXED and Comparis*** to PROC RE G

3 Reformulating Ordinary Regression Analysis in Matrix Notation

　3.1 Writing the Ordinary Regression Equation in Matrix Notation

　　3.1.1 Example

　3.2 Obtaining the Least-Squares Estimator β in Matrix Notation

　　3.2.1 Example：Matrices in Regression Analysis

　3.3 List of Matrix Operati*** to Know

4 Variance Matrices and Linear Transformati***

　4.1 Variance and Correlation Matrices

　　4.1.1 Example

　4.2 How to Obtain the Variance of a Linear Transformation

　　4.2.1 Two Variables

　　4.2.2 Many Variables

5 Variance Matrices of Estimators of Regression Coefficients

　5.1 Usual Standard Error of Least-Squares Estimator of Regression Slope in 　Nonmatrix Formulation

　5.2 Standard Errors of Least-Squares Regression Estimators in Matrix Notation

　　5.2.1 Example

　5.3 The Large Sample Variance Matrix of Maximum Likelihood Estimators

　5.4 Tests and Confidence Intervals

　　5.4.1 Example-Comparing PROC REG and PROC MIXED

6 Dealing with Unequal Variance Around the Regression Line

　6.1 Ordinary Least Squares with Unequal Variance

　　6.1.1 Examples

　6.2 Analysis Taking Unequal Variance into Account

　　6.2.1 The Functional Transformation Approach

　　6.2.2 The Linear Transformation Approach

　　6.2.3 Standard Errors of Weighted Regression Estimators

　Output Packet Ⅲ：Applying the Empirical Option to Adjust Standard Errors

　Output Packet Ⅳ：Analyses with Transformation of the Outcome Variable to 　Equalize Residual Variance

　Output Packet Ⅴ：Weighted Regression Analyses of GHb Data on Age

7 Application of Weighting with Probability Sampling and Nonresp***e

8 Principles in Dealing with Correlated Data

9 A Further Study of How the Transformation Works with Correlated Data

10 Random Effects

11 The Normal Distribution and Likelihood Revisited

12 The Generalization to Non-normal Distributi***.

13 Modeling Binomial and Binary Outcomes

14 Modeling Poisson Outcomes—The Analysis of Rates

15 Modeling Correlated Outcomes with Generalized Estimating Equati***

References

Appendix：Matrix Operati***

Index

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书籍介绍

Each topic starts with an explanation of the theoretical background necessary to allow full understanding of the technique and to facilitate future learning of more advanced or new methods and software Explanati*** are designed to assume as little background in mathematics and statistical theory as possible, except that some knowledge of calculus is necessary for certain parts. SAS commands are provided for applying the methods. (PROC REG, PROC MIXED, and PROC GENMOD) All secti*** contain real life examples, mostly from epidemiologic research First chapter includes a SAS refresher