The DOI matches its Crossref record.
On Grouping for Maximum Homogeneity
Abstract
Given a set of arbitrary numbers, what is a practical procedure for grouping them so that the variance within groups is minimized? An answer to this question, including a description of an automatic computer program, is given for problems up to the size where 200 numbers are to be placed in 10 groups. Two basic types of problem are discussed and illustrated. * This paper is the result of work supported by the Social Science Research Council and by the Bureau of General Research of Kansas State College; also by the courtesies extended to the author by the University of Illinois when he visited there during the summer of 1956. The author gratefully acknowledges the helpful suggestions he received from W. A. Neiswanger, Kern Dickman, and D. B. Gillies of the University of Illinois; from J. I. Northam, now with the Upjohn Company; and from Henry Tucker, now of the University of Arizona.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
(1967) · Cited by 23,719
Computation and analysis of multiple structural change models
Bai, J., & Perron, P. (2003) · Journal of Applied Econometrics · doi:10.1002/jae.659 · Cited by 5,771
An exploratory technique for investigating large quantities of categorical data
Kass, G. V. (1980) · Applied Statistics · doi:10.2307/2986296 · Cited by 2,880
Heritability of nociception I: Responses of 11 inbred mouse strains on 12 measures of nociception
Mogil, J. S., Wilson, S. G., Bon, K., Eun Lee, S., Chung, K., Raber, P., Pieper, J. O., Hain, H. S., Belknap, J. K., Hubert, L., et al. (1999) · Pain · doi:10.1016/s0304-3959(98)00197-3 · Cited by 656
Jin, D., Lu, Y., Qin, J., Cheng, Z., & Mao, Z. (2020) · Computers & Security · doi:10.1016/j.cose.2020.101984 · Cited by 169
Metadata
- Authors
- Fisher, W. D.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 1958-12
- Volume
- 53
- Issue
- 284
- Pages
- 789-798
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W1997648776
- Cited by
- 881
- References
- 0
- FWCI
- 0.99
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: On Grouping for Maximum Homogeneity. https://app.cochat.ai/papers/10.1080/01621459.1958.10501479
- The paper
- Fisher, W. D. (1958). On Grouping for Maximum Homogeneity. Journal of the American Statistical Association. https://doi.org/10.1080/01621459.1958.10501479
Data sources
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.
