Scientific Data Analysis with R Biostatistical Applications

In an era marked by exponential growth in data generation and an unprecedented convergence of technology and healthcare, the intersection of biostatistics and data science has become a pivotal domain. This book is the ideal companion in navigating the convergence of statistical methodologies and data science techniques with diverse applications implemented in the open-source environment of R. It is designed to be a comprehensive guide, marrying the principles of biostatistics with the practical implementation of statistics and data science in R, thereby empowering learners, researchers, and practitioners with the tools necessary to extract meaningful knowledge from biological, health, and medical datasets.

This book is intended for students, researchers, and professionals eager to harness the combined power of biostatistics, data science, and the R programming language while gathering vital statistical knowledge needed for cutting-edge scientists in all fields. It is useful for those seeking to understand the basics of data science and statistical analysis, or looking to enhance their skills in handling any simple or complex data including biological, health, medical, and industry data.

Key Features:

Presents contemporary concepts of data science and biostatistics with real-life data analysis examples Promotes the evolution of fundamental and advanced methods applying to real-life problem-solving cases Explores computational statistical data science techniques from initial conception to recent developments of biostatistics Provides all R codes and real-world datasets to practice and competently apply into reader’s own domains Written in an exclusive state-of-the-art deductive approach without any theoretical hitches to support all contemporary readers

Preface 

1. Introductory Data Sciences 

2. Contemporary Concepts of Biostatistics 

3. Summary Statistics and Presentation of Data 

4. Advanced Graphical Presentation of Data 

5. Measures of Centre and Dispersion 

6. Probability, Random Variables and Distributions 

7. Statistical Inferences 

8. Normality Testing 

9. Nonparametric Tests and Applications 

10. Statistical Association and Correlation 

11. Regression Analysis 

12. Survival Analysis and Factor Analysis