@article{FAVERO2024111903,
title = {Ten questions concerning statistical data analysis in human-centric buildings research: a focus on thermal comfort investigations},
author = {Favero, Matteo and Carlucci, Salvatore and Chinazzo, Giorgia and Møller, Jan Kloppenborg and Schweiker, Marcel and Vellei, Marika and Sonta, Andrew},
journal = {Building and Environment},
pages = {111903},
year = {2024},
issn = {0360-1323},
doi = {https://doi.org/10.1016/j.buildenv.2024.111903}
}Synthetic data simulation
Ten questions concerning statistical data analysis in human-centric buildings research: a focus on thermal comfort investigations
Preface
This notebook is specifically designed to serve as a detailed guide for the examples with synthetic data that have been discussed in the paper titled ‘Ten questions concerning statistical data analysis in human-centric buildings research: a focus on thermal comfort investigations’. The primary objective of this notebook is to provide a comprehensive and practical explanation of the entire modelling process, along with detailed insights into the various methodologies and techniques employed in the process.
Our aim is to provide a valuable resource for researchers and practitioners at all levels of proficiency —both experienced and inexperienced. By providing a clear and practical explanation of the modelling methods and techniques used in the examples, we hope to enable researchers and practitioners to gain a deeper understanding of the subject.
However, it is important to note that this notebook is not intended and should not be used as a set of recipes to solve problems. Statistics is not like following a recipe in a cookbook. It is much more like real cooking, as there are often missing or suboptimal ingredients, multiple choices of which ingredients to include and how to prepare them, and the need to arrive at the best possible dish (usually, there is a part of taste or habit in that), which is often not perfect. Instead, this notebook should be viewed as a map to guide researchers through the modelling process.
A map that other researchers can use to reproduce the scientific journey will require:
- the data that was used;
- the code that was used to analyse the data;
- the software required to run that code;
- the reasoning used to make any decisions during the modelling process and interpret the results.
Contributors
This document was written by Matteo Favero with feedback from Andrew Sonta, Salvatore Carlucci, Giorgia Chinazzo, Jan Kloppenborg Møller, Marcel Schweiker and Marika Vellei.
License and citation
This work is openly licensed via a Creative Commons Attribution 4.0 International (CC BY 4.0).
For attribution, please cite this work as (BibTeX citation):
The corresponding primary article can be found at https://doi.org/10.1016/j.buildenv.2024.111903.