Design and analysis of experiments and observational and studies using R
著者
書誌事項
Design and analysis of experiments and observational and studies using R
（Texts in statistical science）
CRC Press, 2022
- :hbk
大学図書館所蔵 件 / 全5件
-
該当する所蔵館はありません
- すべての絞り込み条件を解除する
注記
Includes bibliographical references (p. 261-265) and index
内容説明・目次
内容説明
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.
目次
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
「Nielsen BookData」 より