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Design and Analysis of Experiments and Observational Studies Using R
Hardcover
Series: Chapman & Hall/CRC Texts in Statistical Science
ApplicationsProbability & Statistics
ISBN13: 9780367456856
Publisher: CRC Press
Published: Apr 27 2022
Pages: 292
Weight: 1.30
Height: 0.69 Width: 6.14 Depth: 9.21
Language: English
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.
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