Applications of regression models in public health /
A one-stop guide for public health students and practitioners learning the applications of classical regression models in epidemiology This book is written for public health professionals and students interested in applying regression models in the field of epidemiology. The academic material is usu...
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| Main Authors: | , |
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| Other Authors: | |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Hoboken, New Jersey :
John Wiley & Sons, Inc.,
[2017]
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| Subjects: | |
| Online Access: | CONNECT |
Table of Contents:
- Applications of Regression Models in Public Health; Contents; Preface; Acknowledgments; About the Authors; 1: Basic Concepts for Statistical Modeling; 1.1 Introduction; 1.2 Parameter Versus Statistic; 1.3 Probability Definition; 1.4 Conditional Probability; 1.5 Concepts of Prevalence and Incidence; 1.6 Random Variables; 1.7 Probability Distributions; 1.8 Centrality and Dispersion Parameters of a Random Variable; 1.9 Independence and Dependence of Random Variables; 1.10 Special Probability Distributions; 1.10.1 Binomial Distribution; 1.10.2 Poisson Distribution; 1.10.3 Normal Distribution.
- 1.11 Hypothesis Testing1.12 Confidence Intervals; 1.13 Clinical Significance Versus Statistical Significance; 1.14 Data Management; 1.14.1 Study Design; 1.14.2 Data Collection; 1.14.3 Data Entry; 1.14.4 Data Screening; 1.14.5 What to Do When Detecting a Data Issue; 1.14.6 Impact of Data Issues and How to Proceed; 1.15 Concept of Causality; References; 2: Introduction to Simple Linear Regression Models; 2.1 Introduction; 2.2 Specific Objectives; 2.3 Model Definition; 2.4 Model Assumptions; 2.5 Graphic Representation; 2.6 Geometry of the Simple Regression Model; 2.7 Estimation of Parameters.
- 2.8 Variance of Estimators2.9 Hypothesis Testing About the Slope of the Regression Line; 2.9.1 Using the Student's t-Distribution; 2.9.2 Using ANOVA; 2.10 Coefficient of Determination R2; 2.11 Pearson Correlation Coefficient; 2.12 Estimation of Regression Line Values and Prediction; 2.12.1 Confidence Interval for the Regression Line; 2.12.2 Prediction Interval of Actual Values of the Response; 2.13 Example; 2.14 Predictions; 2.14.1 Predictions with the Database Used by the Model; 2.14.2 Predictions with Data Not Used to Create the Model; 2.14.3 Residual Analysis; 2.15 Conclusions.
- 3.16 Using Indicator Variables (Dummy Variables)3.17 Polynomial Regression Models; 3.18 Centering; 3.19 Multicollinearity; 3.20 Interaction Terms; 3.21 Conclusion; Practice Exercise; References; 4: Evaluation of Partial Tests of Hypotheses in a MLRM; 4.1 Introduction; 4.2 Specific Objectives; 4.3 Definition of Partial Hypothesis; 4.4 Evaluation Process of Partial Hypotheses; 4.5 Special Cases; 4.6 Examples; 4.7 Conclusion; Practice Exercise; References; 5: Selection of Variables in a Multiple Linear Regression Model; 5.1 Introduction; 5.2 Specific Objectives.
- Practice ExerciseReferences; 3: Matrix Representation of the Linear Regression Model; 3.1 Introduction; 3.2 Specific Objectives; 3.3 Definition; 3.3.1 Matrix; 3.4 Matrix Representation of a SLRM; 3.5 Matrix Arithmetic; 3.5.1 Addition and Subtraction of Matrices; 3.6 Matrix Multiplication; 3.7 Special Matrices; 3.8 Linear Dependence; 3.9 Rank of a Matrix; 3.10 Inverse Matrix [A-1]; 3.11 Application of an Inverse Matrix in a SLRM; 3.12 Estimation of [Beta] Parameters in a SLRM; 3.13 Multiple Linear Regression Model (MLRM); 3.14 Interpretation of the Coefficients in a MLRM; 3.15 ANOVA in a MLRM.
