Quantitative Methods in Population Health : Extensions of Ordinary Regression人口健康的定量方法 rtf 下载 2025 kindle mobi lrf 极速 pdf

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。

Explanations 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 sections 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 Wisconsin Diabetes Registry

　I.3 Wisconsin Sleep Cohort Study

Suggested Reading

1 Review of Ordinary Linear Regression and Its Assumptions

　1.1 The Ordinary Linear Regression Equation and Its Assumptions

　　1.1.1 Straight-Line Relationship

　　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 Comparisons 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 Operations to Know

4 Variance Matrices and Linear Transformations

　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 Nonresponse

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 Distributions.

13 Modeling Binomial and Binary Outcomes

14 Modeling Poisson Outcomes—The Analysis of Rates

15 Modeling Correlated Outcomes with Generalized Estimating Equations

References

Appendix：Matrix Operations

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 Explanations 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 sections contain real life examples, mostly from epidemiologic research First chapter includes a SAS refresher