The DOI matches its Crossref record.
Applied Linear Statistical Models.
Abstract
This text uses an applied approach, with an emphasis on the understanding of concepts and exposition by means of examples. Sufficient theoretical information is provided to enable applications of regression analysis to be carried out. Case studies are used to illustrate many of the statistical methods. There is coverage of composite designs for response surface studies and an introduction to the use of computer-generated optimal designs. The Holm procedure is featured, as well as the analysis of means of identifying important effects. This edition includes an expanded use of graphics: scatter plot matrices, three-dimensional rotating plots, paired comparison plots, three-dimensional response surface and contour plots, and conditional effects plots. An accompanying Student Solutions Manual works out problems in the text.
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
Task-Technology Fit and Individual Performance
Goodhue, D. L., & Thompson, R. L. (1995) · MIS Quarterly · doi:10.2307/249689 · Cited by 6,006
Organizational Market Information Processes: Cultural Antecedents and New Product Outcomes
Moorman, C. (1995) · Journal of Marketing Research · doi:10.1177/002224379503200307 · Cited by 1,020
Self-reported weight and height
Rowland, M. (1990) · The American Journal of Clinical Nutrition · doi:10.1093/ajcn/52.6.1125 · Cited by 994
Effects of Age on Validity of Self-Reported Height, Weight, and Body Mass Index
KUCZMARSKI, M. F., KUCZMARSKI, R. J., & NAJJAR, M. (2001) · Journal of the American Dietetic Association · doi:10.1016/s0002-8223(01)00008-6 · Cited by 965
Volk, D. W., Austin, M. C., Pierri, J. N., Sampson, A. R., & Lewis, D. A. (2000) · Archives of General Psychiatry · doi:10.1001/archpsyc.57.3.237 · Cited by 697
Metadata
- Authors
- Barnett, V., Neter, J., & Wasserman, W.
- Venue
- Journal of the Royal Statistical Society. Series A (General)
- Publisher
- JSTOR
- Published
- 1975
- Volume
- 138
- Issue
- 2
- Pages
- 258
- ISSN
- 0035-9238 (ISSN-L)
- OpenAlex
- W3015302834
- Cited by
- 2,286
- References
- 0
- FWCI
- 28.19
- 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: Applied Linear Statistical Models. https://app.cochat.ai/papers/10.2307/2984653
- The paper
- Barnett, V., Neter, J., & Wasserman, W. (1975). Applied Linear Statistical Models. Journal of the Royal Statistical Society. Series A (General). https://doi.org/10.2307/2984653
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.
