Design and Analysis of Experiments and Observational Studies using R av Nathan (University of Toronto Canada) Taback

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<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