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The book brings together experts working in public health and multi-disciplinary areas to present recent issues in statistical methodological development and their applications. This timely book will impact model development and data analyses of public health research across a wide spectrum of analysis. Data and software used in the studies are available for the reader to replicate the models and outcomes. The fifteen chapters range in focus from techniques for dealing with missing data with Bayesian estimation, health surveillance and population definition and implications in applied latent class analysis, to multiple comparison and meta-analysis in public health data. Researchers in biomedical and public health research will find this book to be a useful reference, and it can be used in graduate level classes.

Statistics. --- Statistics for Life Sciences, Medicine, Health Sciences. --- Public Health. --- Laboratory Medicine. --- Medical laboratories. --- Public health. --- Statistique --- Santé publique --- Mathematical Statistics --- Mathematics --- Physical Sciences & Mathematics --- Public health --- Statistical methods. --- Community health --- Health services --- Hygiene, Public --- Hygiene, Social --- Public health services --- Public hygiene --- Sanitary affairs --- Social hygiene --- Laboratory medicine. --- Health --- Human services --- Biosecurity --- Health literacy --- Medicine, Preventive --- National health services --- Sanitation --- Diagnosis, Laboratory --- Health facilities --- Laboratories --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Econometrics --- Statistics . --- Clinical medicine --- Clinical pathology --- Diagnostic laboratory tests --- Laboratory diagnosis --- Laboratory medicine --- Medical laboratory diagnosis --- Diagnosis --- Pathology --- Biometry. --- Medicine --- Biology --- Biostatistics. --- Biomedical Research. --- Research. --- Biological research --- Biomedical research --- Health Workforce --- Biological statistics --- Biometrics (Biology) --- Biostatistics --- Biomathematics --- Statistics

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This book provides a concise point of reference for the most commonly used regression methods. It begins with linear and nonlinear regression for normally distributed data, logistic regression for binomially distributed data, and Poisson regression and negative-binomial regression for count data. It then progresses to these regression models that work with longitudinal and multi-level data structures. The volume is designed to guide the transition from classical to more advanced regression modeling, as well as to contribute to the rapid development of statistics and data science. With data and computing programs available to facilitate readers' learning experience, Statistical Regression Modeling promotes the applications of R in linear, nonlinear, longitudinal and multi-level regression. All included datasets, as well as the associated R program in packages nlme and lme4 for multi-level regression, are detailed in Appendix A. This book will be valuable in graduate courses on applied regression, as well as for practitioners and researchers in the fields of data science, statistical analytics, public health, and related fields.

Regression analysis. --- R (Computer program language) --- GNU-S (Computer program language) --- Domain-specific programming languages --- Analysis, Regression --- Linear regression --- Regression modeling --- Multivariate analysis --- Structural equation modeling --- Anàlisi de regressió --- R (Llenguatge de programació) --- GNU-S (Llenguatge de programació) --- Llenguatges de programació --- Model de regressió --- Regressió lineal --- Estadística --- Estadística matemàtica --- Correlació múltiple (Estadística) --- Correlació (Estadística) --- Models d'equacions estructurals --- Statistics. --- Programming languages (Electronic computers). --- Statistical Theory and Methods. --- Applied Statistics. --- Programming Language. --- Computer languages --- Computer program languages --- Computer programming languages --- Machine language --- Electronic data processing --- Languages, Artificial --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics

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The book brings together experts working in public health and multi-disciplinary areas to present recent issues in statistical methodological development and their applications. This timely book will impact model development and data analyses of public health research across a wide spectrum of analysis. Data and software used in the studies are available for the reader to replicate the models and outcomes. The fifteen chapters range in focus from techniques for dealing with missing data with Bayesian estimation, health surveillance and population definition and implications in applied latent class analysis, to multiple comparison and meta-analysis in public health data. Researchers in biomedical and public health research will find this book to be a useful reference, and it can be used in graduate level classes.

Statistical science --- Biomathematics. Biometry. Biostatistics --- Hygiene. Public health. Protection --- Semiology. Diagnosis. Symptomatology --- Human medicine --- volksgezondheid --- medische statistiek --- klinische laboratoria --- medische laboratoriumtechnologie --- biostatistiek --- statistiek

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This book provides a concise point of reference for the most commonly used regression methods. It begins with linear and nonlinear regression for normally distributed data, logistic regression for binomially distributed data, and Poisson regression and negative-binomial regression for count data. It then progresses to these regression models that work with longitudinal and multi-level data structures. The volume is designed to guide the transition from classical to more advanced regression modeling, as well as to contribute to the rapid development of statistics and data science. With data and computing programs available to facilitate readers' learning experience, Statistical Regression Modeling promotes the applications of R in linear, nonlinear, longitudinal and multi-level regression. All included datasets, as well as the associated R program in packages nlme and lme4 for multi-level regression, are detailed in Appendix A. This book will be valuable in graduate courses on applied regression, as well as for practitioners and researchers in the fields of data science, statistical analytics, public health, and related fields.

Statistical science --- Mathematical statistics --- Programming --- statistiek --- programmeertalen --- statistisch onderzoek

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Statistical science --- Mathematical statistics --- Programming --- statistiek --- programmeertalen --- statistisch onderzoek