The DOI matches its Crossref record.
Classical and Modern Regression With Applications.
Abstract
The author emphasizes applications with examples that illustrate nearly all the techniques discussed. Applications have been selected from physical sciences, engineering, biology, management science and economics. Emphasis is also placed on concepts with a blend between illustrations using real data sets and mathematical and conceptual development. Expanded coverage includes: simultaneous influence, maximum likelihood estimation of parameters, and the plotting of residuals, the use of the general linear hypothesis, indicator variables, the geometry of least squares, the relationship to ANOVA models, Box-Cox transformation with illustrations, categorical response, other nonnormal error situations, autocorrelated errors and logistic regression.
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
Adsorption isotherm models for basic dye adsorption by peat in single and binary component systems
Allen, S. J., Mckay, G., & Porter, J. F. (2004) · Journal of Colloid and Interface Science · doi:10.1016/j.jcis.2004.08.078 · Cited by 1,051
Angst, C. M., & Agarwal, R. (2009) · MIS Quarterly · doi:10.2307/20650295 · Cited by 1,043
Yu, Y. P., Landsittel, D., Jing, L., Nelson, J., Ren, B., Liu, L., McDonald, C., Thomas, R., Dhir, R., Finkelstein, S., et al. (2004) · Journal of Clinical Oncology · doi:10.1200/jco.2004.05.158 · Cited by 758
Sun, S. S., Chumlea, W. C., Heymsfield, S. B., Lukaski, H. C., Schoeller, D., Friedl, K., Kuczmarski, R. J., Flegal, K. M., Johnson, C. L., & Hubbard, V. S. (2003) · The American Journal of Clinical Nutrition · doi:10.1093/ajcn/77.2.331 · Cited by 692
Incremental Online Learning in High Dimensions
Vijayakumar, S., D'Souza, A., & Schaal, S. (2005) · Neural Computation · doi:10.1162/089976605774320557 · Cited by 527
Metadata
- Authors
- Steinhorst, R. K., & Myers, R. H.
- Venue
- Journal of the American Statistical Association
- Publisher
- JSTOR
- Published
- 1988-03
- Volume
- 83
- Issue
- 401
- Pages
- 271
- ISSN
- 0162-1459 (ISSN-L)
- OpenAlex
- W2898519191
- Cited by
- 3,690
- References
- 0
- FWCI
- 22.80
- 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: Classical and Modern Regression With Applications. https://app.cochat.ai/papers/10.2307/2288958
- The paper
- Steinhorst, R. K., & Myers, R. H. (1988). Classical and Modern Regression With Applications. Journal of the American Statistical Association. https://doi.org/10.2307/2288958
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.
