Understanding the Correlation of Parkinson’s dataset with Multivariate Analysis Packages
- DOI
- 10.2991/978-94-6463-294-1_2How to use a DOI?
- Keywords
- Parkinson’s disease; multivariate analysis; interpretation; graph; inferential statistics; comparative data analysis
- Abstract
Parkinson’s dataset available in open source was used in this work. Inferential statistical analysis is used here to inspect each unit from this dataset and to test a hypothesis depending on the sample data. From the analysis inferences could be extracted by applying probability and make generalizations about the whole data. This method can also be used in experimental and quasi-experimental research design or in program outcome evaluation. During the analysis, the whole population of the sample data was considered while making deductions. Specifically, multivariate analysis was used which involves more than two dependent variables, this method helps in reduction and simplification of data without losing its details. Packages like ggplot2, psych, corrgram, performance analysis, ggcorrplot, ggpubr and RColorBrewer Packages were used for interpreting the data.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - A. Rajha Priya AU - Preenon Bagchi PY - 2023 DA - 2023/11/17 TI - Understanding the Correlation of Parkinson’s dataset with Multivariate Analysis Packages BT - Proceedings of the International Conference on Advances in Nano-Neuro-Bio-Quantum (ICAN 2023) PB - Atlantis Press SP - 7 EP - 20 SN - 2468-5739 UR - https://doi.org/10.2991/978-94-6463-294-1_2 DO - 10.2991/978-94-6463-294-1_2 ID - Priya2023 ER -