| Description |
1 online resource (xix, 250 pages) |
| 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 |
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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 |
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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 |
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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 |
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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 |
| Summary |
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 usually covered in public health courses including (i) Applied Regression Analysis, (ii) Advanced Epidemiology, and (iii) Statistical Computing. The book is composed of 13 chapters, including an introduction chapter that covers basic concepts of statistics and probability. Among the topics covered are linear regression model, polynomial regression model, weighted least squares, methods for selecting the best regression equation, and generalized linear models and their applications to different epidemiological study designs. An example is provided in each chapter that applies the theoretical aspects presented in that chapter. In addition, exercises are included and the final chapter is devoted to the solutions of these academic exercises with answers in all of the major statistical software packages, including STATA, SAS, SPSS, and R. It is assumed that readers of this book have a basic course in biostatistics, epidemiology, and introductory calculus. The book will be of interest to anyone looking to understand the statistical fundamentals to support quantitative research in public health. In addition, this book: - Is based on the authors' course notes from 20 years teaching regression modeling in public health courses - Provides exercises at the end of each chapter - Contains a solutions chapter with answers in STATA, SAS, SPSS, and R - Provides real-world public health applications of the theoretical aspects contained in the chapters Applications of Regression Models in Epidemiology is a reference for graduate students in public health and public health practitioners. ERICK SUAREZ is a Professor of the Department of Biostatistics and Epidemiology at the University of Puerto Rico School of Public Health. He received a Ph. D. degree in Medical Statistics from the London School of Hygiene and Tropical Medicine. He has 29 years of experience teaching biostatistics. CYNTHIA M. PEREZ is a Professor of the Department of Biostatistics and Epidemiology at the University of Puerto Rico School of Public Health. She received an M.S. degree in Statistics and a Ph. D. degree in Epidemiology from Purdue University. She has 22 years of experience teaching epidemiology and biostatistics. ROBERTO RIVERA is an Associate Professor at the College of Business at the University of Puerto Rico at Mayaguez. He received a Ph. D. degree in Statistics from the University of California in Santa Barbara. He has more than five years of experience teaching statistics courses at the undergraduate and graduate levels. MELISSA N. MARTINEZ is an Account Supervisor at Havas Media International. She holds an MPH in Biostatistics from the University of Puerto Rico and an MSBA from the National University in San Diego, California. For the past seven years, she has been performing analyses for the biomedical research and media advertising fields |
| Notes |
Includes index |
| Bibliography |
Includes bibliographical references at the end of each chapters and index |
| Notes |
Print version record |
| Subject |
Medical statistics.
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Regression analysis.
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Public health.
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Regression Analysis
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Models, Statistical
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Epidemiologic Methods
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Public Health
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public health.
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HEALTH & FITNESS -- Holism.
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HEALTH & FITNESS -- Reference.
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MEDICAL -- Alternative Medicine.
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MEDICAL -- Atlases.
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MEDICAL -- Essays.
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MEDICAL -- Family & General Practice.
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MEDICAL -- Holistic Medicine.
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MEDICAL -- Osteopathy.
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Medical statistics
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Public health
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Regression analysis
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| Form |
Electronic book
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| Author |
Suárez Pérez, Erick L., 1953-
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Rivera, Roberto (Associate professor), author.
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Martínez, Melissa N., author
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| LC no. |
2016042829 |
| ISBN |
9781119212492 |
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1119212499 |
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1119212502 |
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9781119212508 |
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1119212510 |
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9781119212515 |
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