Nahhas Ramzi (22 results)

hardcover. Condition: New. Brand new book, sourced directly from publisher. Dispatch time is 7-8 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

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Hardback. Condition: Very Good. Introduction to Regression Methods for Public Health Using R teaches regression methods for continuous, binary, ordinal, and time-to-event outcomes using R as a tool. Regression is a useful tool for understanding the associations between an outcome and a set of explanatory variables, and regression methods are commonly used in many fields, including epidemiology, public health, and clinical research. The focus of this book is on understanding and fitting regression models, diagnosing model fit, and interpreting and writing up results. Examples are drawn from public health and clinical studies. Designed for students, researchers, and practitioners with a basic understanding of introductory statistics, this book teaches the basics of regression and how to implement regression methods using R, allowing the reader to enhance their understanding and begin to grasp new concepts and models.The text includes an overview of regression (Chapter 2); how to examine and summarize the data (Chapter 3), simple (Chapter 4) and multiple (Chapter 5) linear regression; binary, ordinal, and conditional logistic regression, and log-binomial regression (Chapter 6); Cox proportional hazards regression (survival analysis) (Chapter 7); handling data arising from a complex survey design (Chapter 8); and multiple imputation of missing data (Chapter 9). Each chapter closes with a comprehensive set of exercises.Key Features:Comprehensive coverage of the most commonly used regression methods, as well as how to use regression with complex survey data or missing dataAccessible to those with only a first course in statisticsServes as a course textbook, as well as a reference for public health and clinical researchers seeking to learn regression and/or how to use R to do regression analysesIncludes examples of how to diagnose the fit of a regression modelIncludes examples of how to summarize, visualize, table, and write up the resultsIncludes R code to run the examples.…

HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.

HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.

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Hardback. Condition: Very Good. Introduction to Regression Methods for Public Health Using R teaches regression methods for continuous, binary, ordinal, and time-to-event outcomes using R as a tool. Regression is a useful tool for understanding the associations between an outcome and a set of explanatory variables, and regression methods are commonly used in many fields, including epidemiology, public health, and clinical research. The focus of this book is on understanding and fitting regression models, diagnosing model fit, and interpreting and writing up results. Examples are drawn from public health and clinical studies. Designed for students, researchers, and practitioners with a basic understanding of introductory statistics, this book teaches the basics of regression and how to implement regression methods using R, allowing the reader to enhance their understanding and begin to grasp new concepts and models.The text includes an overview of regression (Chapter 2); how to examine and summarize the data (Chapter 3), simple (Chapter 4) and multiple (Chapter 5) linear regression; binary, ordinal, and conditional logistic regression, and log-binomial regression (Chapter 6); Cox proportional hazards regression (survival analysis) (Chapter 7); handling data arising from a complex survey design (Chapter 8); and multiple imputation of missing data (Chapter 9). Each chapter closes with a comprehensive set of exercises.Key Features:Comprehensive coverage of the most commonly used regression methods, as well as how to use regression with complex survey data or missing dataAccessible to those with only a first course in statisticsServes as a course textbook, as well as a reference for public health and clinical researchers seeking to learn regression and/or how to use R to do regression analysesIncludes examples of how to diagnose the fit of a regression modelIncludes examples of how to summarize, visualize, table, and write up the resultsIncludes R code to run the examples.…

hardcover. Condition: New. Brand new book, sourced directly from publisher. Dispatch time is 24-48 hours from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

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Hardback. Condition: New. Introduction to Regression Methods for Public Health Using R teaches regression methods for continuous, binary, ordinal, and time-to-event outcomes using R as a tool. Regression is a useful tool for understanding the associations between an outcome and a set of explanatory variables, and regression methods are commonly used in many fields, including epidemiology, public health, and clinical research. The focus of this book is on understanding and fitting regression models, diagnosing model fit, and interpreting and writing up results. Examples are drawn from public health and clinical studies. Designed for students, researchers, and practitioners with a basic understanding of introductory statistics, this book teaches the basics of regression and how to implement regression methods using R, allowing the reader to enhance their understanding and begin to grasp new concepts and models.The text includes an overview of regression (Chapter 2); how to examine and summarize the data (Chapter 3), simple (Chapter 4) and multiple (Chapter 5) linear regression; binary, ordinal, and conditional logistic regression, and log-binomial regression (Chapter 6); Cox proportional hazards regression (survival analysis) (Chapter 7); handling data arising from a complex survey design (Chapter 8); and multiple imputation of missing data (Chapter 9). Each chapter closes with a comprehensive set of exercises.Key Features:Comprehensive coverage of the most commonly used regression methods, as well as how to use regression with complex survey data or missing dataAccessible to those with only a first course in statisticsServes as a course textbook, as well as a reference for public health and clinical researchers seeking to learn regression and/or how to use R to do regression analysesIncludes examples of how to diagnose the fit of a regression modelIncludes examples of how to summarize, visualize, table, and write up the resultsIncludes R code to run the examples.…

Condition: New. Ramzi W. Nahhas teaches biostatistics at Wright State University, Dayton, Ohio, USA, where he is Professor in the Department of Population and Public Health Sciences, Boonshoft School of Medicine. In addition to teaching, he is actively involved i.

Hardcover. Condition: Brand New. 472 pages. 10.00x7.00x10.00 inches. In Stock.

Hardback. Condition: New. Introduction to Regression Methods for Public Health Using R teaches regression methods for continuous, binary, ordinal, and time-to-event outcomes using R as a tool. Regression is a useful tool for understanding the associations between an outcome and a set of explanatory variables, and regression methods are commonly used in many fields, including epidemiology, public health, and clinical research. The focus of this book is on understanding and fitting regression models, diagnosing model fit, and interpreting and writing up results. Examples are drawn from public health and clinical studies. Designed for students, researchers, and practitioners with a basic understanding of introductory statistics, this book teaches the basics of regression and how to implement regression methods using R, allowing the reader to enhance their understanding and begin to grasp new concepts and models.The text includes an overview of regression (Chapter 2); how to examine and summarize the data (Chapter 3), simple (Chapter 4) and multiple (Chapter 5) linear regression; binary, ordinal, and conditional logistic regression, and log-binomial regression (Chapter 6); Cox proportional hazards regression (survival analysis) (Chapter 7); handling data arising from a complex survey design (Chapter 8); and multiple imputation of missing data (Chapter 9). Each chapter closes with a comprehensive set of exercises.Key Features:Comprehensive coverage of the most commonly used regression methods, as well as how to use regression with complex survey data or missing dataAccessible to those with only a first course in statisticsServes as a course textbook, as well as a reference for public health and clinical researchers seeking to learn regression and/or how to use R to do regression analysesIncludes examples of how to diagnose the fit of a regression modelIncludes examples of how to summarize, visualize, table, and write up the resultsIncludes R code to run the examples.…

Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Introduction to Regression Methods for Public Health Using R teaches regression methods for continuous, binary, ordinal, and time-to-event outcomes using R as a tool. Regression is a useful tool for understanding the associations between an outcome and a set of explanatory variables, and regression methods are commonly used in many fields, including epidemiology, public health, and clinical research. The focus of this book is on understanding and fitting regression models, diagnosing model fit, and interpreting and writing up results. Examples are drawn from public health and clinical studies. Designed for students, researchers, and practitioners with a basic understanding of introductory statistics, this book teaches the basics of regression and how to implement regression methods using R, allowing the reader to enhance their understanding and begin to grasp new concepts and models.The text includes an overview of regression (Chapter 2); how to examine and summarize the data (Chapter 3), simple (Chapter 4) and multiple (Chapter 5) linear regression; binary, ordinal, and conditional logistic regression, and log-binomial regression (Chapter 6); Cox proportional hazards regression (survival analysis) (Chapter 7); handling data arising from a complex survey design (Chapter 8); and multiple imputation of missing data (Chapter 9). Each chapter closes with a comprehensive set of exercises.Key Features:Comprehensive coverage of the most commonly used regression methods, as well as how to use regression with complex survey data or missing dataAccessible to those with only a first course in statisticsServes as a course textbook, as well as a reference for public health and clinical researchers seeking to learn regression and/or how to use R to do regression analysesIncludes examples of how to diagnose the fit of a regression modelIncludes examples of how to summarize, visualize, table, and write up the resultsIncludes R code to run the examples 458 pp. Englisch.…

Hardcover. Condition: Brand New. 472 pages. 10.00x7.00x10.00 inches. In Stock. This item is printed on demand.

Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Introduction to Regression Methods for Public Health Using R teaches regression methods for continuous, binary, ordinal, and time-to-event outcomes using R as a tool. Regression is a useful tool for understanding the associations between an outcome and a set of explanatory variables, and regression methods are commonly used in many fields, including epidemiology, public health, and clinical research. The focus of this book is on understanding and fitting regression models, diagnosing model fit, and interpreting and writing up results. Examples are drawn from public health and clinical studies. Designed for students, researchers, and practitioners with a basic understanding of introductory statistics, this book teaches the basics of regression and how to implement regression methods using R, allowing the reader to enhance their understanding and begin to grasp new concepts and models.The text includes an overview of regression (Chapter 2); how to examine and summarize the data (Chapter 3), simple (Chapter 4) and multiple (Chapter 5) linear regression; binary, ordinal, and conditional logistic regression, and log-binomial regression (Chapter 6); Cox proportional hazards regression (survival analysis) (Chapter 7); handling data arising from a complex survey design (Chapter 8); and multiple imputation of missing data (Chapter 9). Each chapter closes with a comprehensive set of exercises.Key Features:Comprehensive coverage of the most commonly used regression methods, as well as how to use regression with complex survey data or missing dataAccessible to those with only a first course in statisticsServes as a course textbook, as well as a reference for public health and clinical researchers seeking to learn regression and/or how to use R to do regression analysesIncludes examples of how to diagnose the fit of a regression modelIncludes examples of how to summarize, visualize, table, and write up the resultsIncludes R code to run the examples.…

Buch. Condition: Neu. Introduction to Regression Methods for Public Health Using R | Ramzi W. Nahhas | Buch | Einband - fest (Hardcover) | Englisch | 2024 | Chapman and Hall/CRC | EAN 9781032203072 | Verantwortliche Person für die EU: Taylor & Francis Verlag GmbH, Kaufingerstr. 24, 80331 München, gpsr[at]taylorandfrancis[dot]com | Anbieter: preigu Print on Demand. …