Introduction to Biostatistics Using R

Erscheinungsjahr 2021
ISBN: 978-1-77469-040-6
Verlag: Arcler Press

Buch, Englisch, 518 Seiten, Hardback, Format (B × H): 152 mm x 229 mm

This book covers some introductory steps in biostatistics using R programming language. Biostatistics is the branch of statistics that applies statistical methods to medical and biological problems. Biostatistics has become more important recently for studying the great amount of data that is produced from census data, genome sequencing, gene expression data, medical bioinformatics, and medical imaging data. With the help of R programming, statistical analysis, data cleaning, data visualization, and machine learning has become a relatively easy tasks for these huge datasets. R is now considered the centerpiece language for doing all these data science skills because it has many useful packages that not only can perform all these tasks, but also, has additional packages that were specifically designed for several statistical tasks related to biology and medical data. In addition, many scientific journals require the data analysis R scripts to ensure reproducibility of the submitted results. The first chapter of this book introduces many statistical concepts used in scientific research like study designs, sample, and population, and data types. Chapters 2, 4, and 5 cover the three main data types which are continuous data, categorical data, and time to event data. Chapter 3 discusses the popular continuous distribution that is the normal distribution along with its application to sample data. Chapter 6 is about the sampling distribution of different sample estimates along with a discussion of the famous central limit theorem (CLT). Chapters 7 and 8 are involved in confidence interval (CI) calculations, and Chapters 9–11 discuss several types of statistical tests like t-test, ANOVA, Chi-square, log-rank, etc. Finally, Chapters 12–14 deal with different regression types; linear regression for continuous outcomes, logistic regression for binary outcomes, and Cox regression for time to event outcomes. In all these chapters, many examples from many scientific journal articles or built in data sets along with different codes and outputs are given to help your understanding of these numerous statistical concepts. I hope this book will be a great addition to your future biostatistical projects.

Autoren/Hrsg.

Fachgebiete

• Mathematik | Informatik Mathematik Mathematik Allgemein

Inhaltsverzeichnis

- Chapter 1 Introduction to Statistics
- Chapter 2 Numerical Data
- Chapter 3 The Normal Distribution
- Chapter 4 Binary and Categorical Data
- Chapter 5 Time to Event Data = Survival Data = Failure Time Data
- Chapter 6 Sampling Distribution
- Chapter 7 Confidence Intervals
- Chapter 8 Confidence Intervals for Comparing Two or More Populations
- Chapter 9 Hypothesis Testing for Comparing Means
- Chapter 10 Hypothesis Testing for Proportions and Time to Event Data
- Chapter 11 Hypothesis Testing for More Than Two Populations
- Chapter 12 Simple and Multiple Linear Regression
- Chapter 13 Simple and Multiple Logistic Regression
- Chapter 14 Simple and Multiple Cox Regression

Über Autor(innen)

Mohsen Nady is a pharmacist with a M.D. in Microbiology and a diploma in Industrial Pharmacy. In addition, Mohsen has more than 4 years experience using R programming language. Mohsen has applied his skills in R programming to different projects related to Genomics, Microbiology, Biostatistics, Six Sigma, Data Analytics, Data Visualization, Building Apps, Geography, Market Analysis, Business Analysis,….etc. Mohsen also published his thesis in high impact journal that attracted many citations, where all the statistical analysis were performed by him in addition to the methodological part. Furthermore, Mohsen has earned additional certificates, from top universities (Harvard, Johns Hopkins, Denmark,.etc) in R programming, Python, Excel, and Minitab that highlight his outstanding programming skills.