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Peer-reviewedCC BY 4.0Axioms

OLS Testing with Predictors Scaled to Have Unit Sample Variance, and Similar Results for a Robust Regression Estimator

  • David J. Olive1
  • Sanjuka Johana Lemonge2
  1. 1School of Mathematical & Statistical Sciences, Southern Illinois University, Carbondale, IL 62901, USA
  2. 2School of Business, Black Hills State University, Spearfish, SD 57799, USA
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Abstract

We consider hypothesis tests for the multiple linear regression model with ordinary least squares if the predictor variables have been scaled to have unit sample variance. Some tests are unchanged, but confidence intervals, confidence regions, and some tests are no longer valid. Similar results are obtained for a robust regression estimator.

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Cite

BibTeXRIS
BibTeX
@article{olive2026testing,
  title = {{OLS Testing with Predictors Scaled to Have Unit Sample Variance, and Similar Results for a Robust Regression Estimator}},
  author = {David J. Olive and Sanjuka Johana Lemonge},
  journal = {Axioms},
  year = {2026},
  month = oct,
  doi = {10.3390/axioms15100738},
  url = {https://www.mdpi.com/2075-1680/15/10/738},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - OLS Testing with Predictors Scaled to Have Unit Sample Variance, and Similar Results for a Robust Regression Estimator
AU  - David J. Olive
AU  - Sanjuka Johana Lemonge
PY  - 2026
DA  - 2026/10/01
JO  - Axioms
DO  - 10.3390/axioms15100738
UR  - https://www.mdpi.com/2075-1680/15/10/738
AB  - We consider hypothesis tests for the multiple linear regression model with ordinary least squares if the predictor variables have been scaled to have unit sample variance. Some tests are unchanged, but confidence intervals, confidence regions, and some tests are no longer valid. Similar results are obtained for a robust regression estimator.
SN  - 2075-1680
ER  -