Thomas W. MacFarland – författare

​In statistics, analysis of variance (ANOVA) is a collection of statistical models used to distinguish between an observed variance in a particular variable and its component parts.  In its simplest form, ANOVA provides a statistical test of whether or not the means of several groups are all equal, and therefore generalizes a test between these groups. One test often used by statisticians and researchers in their work is the Two-Way ANOVA, which determines the differences--and possible interactions--when variables are presented from the perspective of two or more categories.  When a Two-Way ANOVA is implemented, it enables one to compare and contrast variables resulting from independent or joint actions. This brief provides guidance on how R can be used to facilitate Two-Way ANOVA for data analysis and graphical presentation.  Along with instruction on the use of R and R syntax associated with Two-Way ANOVA, this brief will also reinforce the use of descriptive statistics and graphicalfigures to complement outcomes from parametric Two-Way ANOVA.

This book introduces the open source R software language that can be implemented in biostatistics for data organization, statistical analysis, and graphical presentation.

This book introduces the open source R software language that can be implemented in biostatistics for data organization, statistical analysis, and graphical presentation.

Introduction to Data Science in Biostatistics: Using R, the Tidyverse Ecosystem, and APIs defines and explores the term "data science" and discusses the many professional skills and competencies affiliated with the industry. With data science being a leading indicator of interest in STEM fields, the text also investigates this ongoing growth of demand in these spaces, with the goal of providing readers who are entering the professional world with foundational knowledge of required skills, job trends, and salary expectations. The text provides a historical overview of computing and the field's progression to R as it exists today, including the multitude of packages and functions associated with both Base R and the tidyverse ecosystem. Readers will learn how to use R to work with real data, as well as how to communicate results to external stakeholders. A distinguishing feature of this text is its emphasis on the emerging use of APIs to obtain data.

Introduction to Data Science in Biostatistics: Using R, the Tidyverse Ecosystem, and APIs defines and explores the term "data science" and discusses the many professional skills and competencies affiliated with the industry.

Through real-world datasets, this book shows the reader how to work with material in biostatistics using the open source software R.

This book contains a rich set of tools for nonparametric analyses, and the purpose of this text is to provide guidance to students and professional researchers on how R is used for nonparametric data analysis in the biological sciences:To introduce when nonparametric approaches to data analysis are appropriateTo introduce the leading nonparametric tests commonly used in biostatistics and how R is used to generate appropriate statistics for each testTo introduce common figures typically associated with nonparametric data analysis and how R is used to generate appropriate figures in support of each data set The book focuses on how R is used to distinguish between data that could be classified as nonparametric as opposed to data that could be classified as parametric, with both approaches to data classification covered extensively. Following an introductory lesson on nonparametric statistics for the biological sciences, the book is organized into eight self-contained lessons on various analyses and tests using R to broadly compare differences between data sets and statistical approach.