Quantitative Methods in Population Health Extensions of Ordinary Regression [Hardcover]

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 refresherPreface.

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 ReformullCë