Design and Analysis of Experiments and Observational Studies using R

1. Auflage 2022
ISBN: 978-0-367-45685-6
Verlag: Chapman and Hall/CRC

Buch, Englisch, 292 Seiten, Format (B × H): 161 mm x 240 mm, Gewicht: 608 g

Reihe: Chapman & Hall/CRC Texts in Statistical Science

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.

Autoren/Hrsg.

Taback, Nathan

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik

Inhaltsverzeichnis

1 Introduction 2 Mathematical Statistics: Simulation and Computation 3 Comparing Two Treatments 4 Power and Sample Size 5 Comparing More Than Two Treatments 6 Factorial Designs at Two Levels - 2k Designs 7 Causal Inference

Über Autor(innen)

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