Principles and Applications of Biostatistics (Original PDF from Publisher)

Principles and Applications of Biostatistics: Unlocking the Power of Biological and Health Sciences through Statistical Analysis

Ray M. Merrill’s Principles and Applications of Biostatistics unveils the fundamental principles and methodologies essential for interpreting and using statistics effectively in the biological and health sciences. This comprehensive text provides a transformative understanding of how biostatistics empowers researchers to describe, assess, and grapple with pressing issues that endanger our collective well-being, including the ongoing COVID-19 pandemic.

With meticulous attention to detail, the text meticulously covers the entire spectrum of statistical processes: data collection, analysis, presentation, and interpretation. Ample real-world examples and thought-provoking exercises woven throughout each chapter bring these concepts to life, solidifying students’ grasp of the subject matter.

Key Features:

• Thought-Provoking Problem-Solving: Each problem culminates in practical questions, encouraging students to delve into the underlying reasons for the issue, formulate hypotheses, and statistically validate their assumptions.

• Comprehensive Real-World Cases: Every chapter abounds with practical examples and assignment problems, with a special focus on the implications of COVID-19. These diverse cases encompass a vast array of applications of biostatistics.

• Visual Clarity through Graphics: Meticulously designed and visually appealing graphics serve as powerful tools to unravel the complexities of statistical concepts, functions, and data, making them more accessible and comprehensible.

• Practical Applications with Computer Software: The text actively promotes the use of computer applications, such as SAS and MS Excel, to empower students in demonstrating data analysis techniques.

• Exploratory Data Analysis Tools: Advanced data analysis systems are harnessed to generate descriptive measures and create fundamental plots, such as stem-and-leaf and box plots, enabling students to explore and understand their data effectively.

• Rigorous Hypothesis Testing: The book meticulously presents the methodologies of hypothesis testing using p-values and constructing confidence intervals in both descriptive and analytical assessments. Computer printouts showcase these concepts in practice, providing students with tangible examples of statistical reasoning.