Cited by 16Advanced Statistical Methods and ModelsStatistical and numerical algorithmsStatistical Methods and Applications

On sample size and precision in ordinary least squares

Montenegro, Alvaro·2001·Journal of Applied Statistics

Abstract

An expression relating estimation precision in the classical linear model to the number of parameters k and the sample size n is illustrated. A rule of thumb for the sample size is suggested.

Integrity signals

Registry

Crossref · 10.1080/02664760120047933

Cites

4 of 4 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Montenegro, A.
Corresponding author
Montenegro, Alvaro
Venue
Journal of Applied Statistics
Published
2001-07
Volume
28
Issue
5
Pages
603-605
ISSN
  • 0266-4763 (ISSN-L)
  • 1360-0532
OpenAlex
W1980731538
Cited by
16
References
4
Citations by year
2024: 2 · 2023: 1 · 2022: 2 · 2020: 1 · 2017: 1 · 2015: 1 · 2014: 2 · 2012: 1
FWCI
0.00
Type
journal article
Language
en
Open access
closed
Locations
  • Journal of Applied Statistics (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
  • Physical Sciences › Mathematics › Applied Mathematics › Statistical and numerical algorithms
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
Keywords
sample size, estimation precision, ordinary least squares, number of parameters, rules of thumb

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