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A Cluster Analysis Method for Grouping Means in the Analysis of Variance
Abstract
It is sometimes useful in an analysis of variance to split the treatments into reasonably homogeneous groups. Multiple comparison procedures are often used for this purpose, but a more direct method is to use the techniques of cluster analysis. This approach is illustrated for several sets of data, and a likelihood ratio test is developed for judging the significance of differences among the resulting groups.
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Cited by
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An Empirical Comparison of Model Validation Techniques for Defect Prediction Models
Tantithamthavorn, C., McIntosh, S., Hassan, A. E., & Matsumoto, K. (2017) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2016.2584050 · Cited by 574
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Metadata
- Authors
- Scott, A. J., & Knott, M.
- Venue
- Biometrics
- Publisher
- JSTOR
- Published
- 1974-09
- Volume
- 30
- Issue
- 3
- Pages
- 507
- ISSN
- 0006-341X (ISSN-L)
- OpenAlex
- W2330210193
- Cited by
- 2,770
- References
- 0
- FWCI
- 2.56
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
