The DOI matches its Crossref record.
Algorithms for Computing the Sample Variance: Analysis and Recommendations
Abstract
The problem of computing the variance of a sample of N data points {xi } may be difficult for certain data sets, particularly when N is large and the variance is small. We present a survey of possible algorithms and their round-off error bounds, including some new analysis for computations with shifted data. Experimental results confirm these bounds and illustrate the dangers of some algorithms. Specific recommendations are made as to which algorithm should be used in various contexts.
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Cited by
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O’Flynn, C., & Chen, Z. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10175-0_17 · Cited by 258
Metadata
- Authors
- Chan, T. F., Golub, G. H., & Leveque, R. J.
- Venue
- The American Statistician
- Publisher
- Informa UK Limited
- Published
- 1983-08
- Volume
- 37
- Issue
- 3
- Pages
- 242-247
- ISSN
- 0003-1305 (ISSN-L)
- 1537-2731
- OpenAlex
- W2062506051
- Cited by
- 354
- References
- 0
- FWCI
- 2.80
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Algorithms for Computing the Sample Variance: Analysis and Recommendations. https://app.cochat.ai/papers/10.1080/00031305.1983.10483115
- The paper
- Chan, T. F., Golub, G. H., & Leveque, R. J. (1983). Algorithms for Computing the Sample Variance: Analysis and Recommendations. The American Statistician. https://doi.org/10.1080/00031305.1983.10483115
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
