OptSig: Optimal Level of Significance for Regression and Other
Statistical Tests
The optimal level of significance is calculated based on a decision-theoretic approach. The optimal level is chosen so that the expected loss from hypothesis testing is minimized. A range of statistical tests are covered, including the test for the population mean, population proportion, and a linear restriction in a multiple regression model.
The details are covered in Kim and Choi (2020) <doi:10.1111/abac.12172>, and Kim (2021) <doi:10.1080/00031305.2020.1750484>.
| Version: |
2.2 |
| Imports: |
pwr |
| Published: |
2022-07-03 |
| Author: |
Jae H. Kim |
| Maintainer: |
Jae H. Kim <jaekim8080 at gmail.com> |
| License: |
GPL-2 |
| NeedsCompilation: |
no |
| CRAN checks: |
OptSig results |
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