Introduction to Design and Analysis of Scientific Studies exposes undergraduate and graduate students to the foundations of classical experimental design and observational studies through a modern framework - The Rubin Causal Model. A causal inference framework is important in design, data collection and analysis since it provides a framework for investigators to readily evaluate study limitations and draw appropriate conclusions. R is used to implement designs and analyse the data collected.

Features:

• Classical experimental design with an emphasis on computation using tidyverse packages in R.
• Applications of experimental design to clinical trials, A/B testing, and other modern examples.
• Discussion of the link between classical experimental design and causal inference.
• The role of randomization in experimental design and sampling in the big data era.
• Exercises with solutions.

Instructor slides in RMarkdown, a new R package will be developed to be used with book, and a bookdown version of the book will be freely available. The proposed book will emphasize ethics, communication and decision making as part of design, data analysis, and statistical thinking.

Author: Nathan Taback
Binding Type: Hardcover
Publisher: CRC Press
Published: 04/27/2022
Series: Chapman & Hall/CRC Texts in Statistical Science
Pages: 292
Weight: 1.3lbs
Size: 9.21h x 6.14w x 0.69d
ISBN: 9780367456856

About the Author

Nathan Taback is Associate Professor of Statistics and Data Science at University of Toronto.