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.
General
Imprint: |
Crc Press
|
Country of origin: |
United Kingdom |
Series: |
Chapman & Hall/CRC Texts in Statistical Science |
Release date: |
April 2022 |
First published: |
2022 |
Authors: |
Nathan Taback
|
Dimensions: |
234 x 156 x 25mm (L x W x T) |
Format: |
Hardcover
|
Pages: |
292 |
ISBN-13: |
978-0-367-45685-6 |
Categories: |
Books >
Science & Mathematics >
Mathematics >
Probability & statistics
|
LSN: |
0-367-45685-0 |
Barcode: |
9780367456856 |
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