Design and Analysis of Experiments and Observational Studies using R

<p><b><i>Introduction to Design and Analysis of Scientific Studies</i></b> 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.</p><p>Features:</p><ul> <li>Classical experimental design with an emphasis on computation using tidyverse packages in R.</li> <li>Applications of experimental design to clinical trials A/B testing and other modern examples.</li> <li>Discussion of the link between classical experimental design and causal inference.</li> <li>The role of randomization in experimental design and sampling in the big data era.</li> <li>Exercises with solutions.</li> </ul><p>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.</p>