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Introduction to Data Analysis and Graphical Presentation in Biostatistics with R : Statistics in the Large / by Thomas W. MacFarland.

Por: Colaborador(es): Tipo de material: TextoTextoSeries SpringerBriefs in StatisticsEditor: Cham : Springer International Publishing : Imprint: Springer, 2014Descripción: vii, 167 páginas 16 ilustraciones, 14 ilustraciones en color. recurso en líneaTipo de contenido:
  • texto
Tipo de medio:
  • computadora
Tipo de portador:
  • recurso en línea
ISBN:
  • 9783319025322
Formatos físicos adicionales: Edición impresa:: Sin títuloClasificación LoC:
  • QA276-280
Recursos en línea:
Contenidos:
Introduction to Biostatistics and R -- Data exploration, descriptive statistics and measures of central tendency -- Student's t-Test for independent samples -- Student's t-Test for matched pairs -- One way ANOVA -- Two way ANOVA -- Correlation and linear regression -- Future Actions and Next Steps.
Resumen: Through real-world datasets, this book shows the reader how to work with material in biostatistics using the open source software R. These include tools that are critical to dealing with missing data, which is a pressing scientific issue for those engaged in biostatistics. Readers will be equipped to run analyses and make graphical presentations based on the sample dataset and their own data. The hands-on approach will benefit students and ensure the accessibility of this book for readers with a basic understanding of R. Topics include: an introduction to Biostatistics and R, data exploration, descriptive statistics and measures of central tendency, t-Test for independent samples, t-Test for matched pairs, ANOVA, correlation and linear regression, and advice for future work.
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Springer eBooks

Introduction to Biostatistics and R -- Data exploration, descriptive statistics and measures of central tendency -- Student's t-Test for independent samples -- Student's t-Test for matched pairs -- One way ANOVA -- Two way ANOVA -- Correlation and linear regression -- Future Actions and Next Steps.

Through real-world datasets, this book shows the reader how to work with material in biostatistics using the open source software R. These include tools that are critical to dealing with missing data, which is a pressing scientific issue for those engaged in biostatistics. Readers will be equipped to run analyses and make graphical presentations based on the sample dataset and their own data. The hands-on approach will benefit students and ensure the accessibility of this book for readers with a basic understanding of R. Topics include: an introduction to Biostatistics and R, data exploration, descriptive statistics and measures of central tendency, t-Test for independent samples, t-Test for matched pairs, ANOVA, correlation and linear regression, and advice for future work.

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