Sathianandan, T V (2023) Principal Component Analysis and its Applications. In: Training Manual on Statistical Designs and Analytical Methods for Multifactor Experiments. ICAR- Central Marine Fisheries Research Institute, Kochi, pp. 214-225.
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Abstract
Principal Component Analysis (PCA) is one of the most popular techniques in multivariate statistical theory. A major application of PCA is data dimension reduction without losing much of the information contained in the multivariate data set. It has a wide range of applications in many areas especially where huge data sets are to be analysed and interpreted. The purpose of analysing multivariate data sets is either to visualise patterns hidden in the data or to examine the inter-relationships or to see the dynamic behaviour over time. The initial analysis in all such studies is to present the variability and interrelationships in terms of means and variances of the variables considered and covariances (or correlations) between possible pairs of variables.
| Item Type: | Book Section |
|---|---|
| Uncontrolled Keywords: | Principal Component Analysis (PCA) |
| Subjects: | Statistical Designs |
| Divisions: | CMFRI-Kochi > Marine Capture > Fishery Resources Assessment, Economics and Extension Division Subject Area > CMFRI > CMFRI-Kochi > Marine Capture > Fishery Resources Assessment, Economics and Extension Division CMFRI-Kochi > Marine Capture > Fishery Resources Assessment, Economics and Extension Division Subject Area > CMFRI-Kochi > Marine Capture > Fishery Resources Assessment, Economics and Extension Division |
| Depositing User: | Arun Surendran |
| Date Deposited: | 14 Sep 2023 05:14 |
| Last Modified: | 14 Sep 2023 05:14 |
| URI: | http://eprints.cmfri.org.in/id/eprint/17458 |
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