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Design and Analysis of Experiments and Observational Studies using R is a free online textbook site based on lecture notes from a twelve-week course offered by the Department of Statistical Sciences at the University of Toronto. It focuses on classical experimental design and observational study design, using causal frameworks, real data, simulations, and R programming to help learners understand both methods and applications. A print edition is available from Routledge, but the website does not list a specific price.
Based on the available content, this is not a live course, recorded course, or 1-on-1 tutoring program, but rather a structured e-book/course notes resource. Each chapter typically introduces concepts or methods first, then includes a “Computational Lab” section showing how to implement them in R. The material emphasizes computation and simulation, using base R, tidyverse, and statistics-related packages such as scidesignR, FrF2, and emmeans. Datasets can also be installed via scidesignR. Overall, it is closer to a university statistics course companion textbook than a beginner-oriented bootcamp.
The author, Nathan Taback, is an Associate Professor, Teaching Stream in the Department of Statistical Sciences at the University of Toronto, giving the resource a clear academic background. The content assumes readers have already studied probability theory, mathematical statistics, regression analysis, and linear models; some R experience will also make the learning process smoother. The language of instruction is English. The main text does not mention a completion certificate, accreditation, assignment grading, or official learning support, so it is not suitable for users whose main goal is to obtain a certificate.
Its biggest advantage is that the online content is free and released under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License. The academic source is credible, and the explanations of R versions, dependencies, and coding practices are detailed, making it suitable for serious study and as a course reference. The limitations are that the entry barrier is relatively high, with expected knowledge of statistical mathematics and R syntax. It also lacks interactive teaching, Q&A support, learning path reminders, and Chinese-language support. Although the print book can be purchased, pricing and payment methods are not disclosed.
It is suitable for senior undergraduate students in statistics, applied master’s students, researchers, or anyone who wants to learn experimental design using R. If you are starting from zero in statistics or only want to quickly learn software operations, you may need to first build a foundation in probability and statistics, linear models, and R. Access from mainland China cannot be determined from the available text and is marked as unknown; payment information is also unclear. Alternatives include Coursera, edX, open statistics courses from Chinese universities, or Chinese-language textbooks on R and experimental design.
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