Melody S. Goodman

Biostatistics for Clinical and Public Health Research (eBook, ePUB)

• Format: ePub

Produktbeschreibung

The new edition of Biostatistics for Clinical and Public Health Research is an introductory workbook to provide not only a concise overview of key statistical concepts but also step-by-step guidance on how to apply these through a range of software packages, including R, SAS, and Stata.

Providing a comprehensive survey of essential topics - including probability, diagnostic testing, probability distributions, estimation, hypothesis testing, correlation, regression, and survival analysis - each chapter features a detailed summary of the topic at hand, followed by examples to show readers how to conduct analysis and interpret the results. Also including exercises and solutions, case studies, take-away points, and data sets (Excel, SAS, and Stata formats), the new edition now includes a chapter on data literacy and data ethics, as well as examples drawn from the COVID-19 pandemic.

Ideally suited to accompany either a course or as support for independent study, this book will be an invaluable tool for both students of biostatistics and clinical or public health practitioners.

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 622
• Erscheinungstermin: 19. August 2025
• Englisch
• ISBN-13: 9781040399699
• Artikelnr.: 75032635

Autorenporträt

Melody S. Goodman is a professor in the Department of Biostatistics at New York University School of Global Public Health. She is a biostatistician with experience in study design, developing survey instruments, data collection, data management, and data analysis for public health and clinical research projects. She has taught introductory biostatistics for masters of public health and medical students for over 15 years at multiple institutions (Stony Brook University School of Medicine, Washington University in St. Louis School of Medicine, New York University School of Global Public Health).

Inhaltsangabe

Descriptive Statistics. Introduction to SAS. Probability. Diagnostics.
Discrete Probability Distributions. Continuous Probability Distributions.
Probability Distributions. Estimation. One Sample Hypothesis Testing. Two
Sample Hypothesis Sample. Nonparametric Statistics. One Sample and Two
Sample Hypothesis Testing, Sample Size, and Nonparametric Methods.
Categorical Data Sets. ANOVA. Correlation. Linear Regression. Logistic
Regression. Survival Analysis. Data Analysis.

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Descriptive Statistics. Introduction to SAS. Probability. Diagnostics. Discrete Probability Distributions. Continuous Probability Distributions. Probability Distributions. Estimation. One Sample Hypothesis Testing. Two Sample Hypothesis Sample. Nonparametric Statistics. One Sample and Two Sample Hypothesis Testing, Sample Size, and Nonparametric Methods. Categorical Data Sets. ANOVA. Correlation. Linear Regression. Logistic Regression. Survival Analysis. Data Analysis.