Testing for signal-to-noise ratio in linear regression: a test under large or massive sample
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초록

This paper proposes a test for the signal-to-noise ratio applicable to a range of significance tests and model diagnostics in a linear regression model. It is particularly useful when sample size is large or massive, where, as a consequence, conventional tests frequently lead to inappropriate rejection of the null hypothesis. The test is conducted in the context of the traditional F-test, with its critical values increasing with sample size. It maintains desirable size properties under a large or massive sample size, when the null hypothesis is violated by a practically negligible margin. The test is widely applicable to many empirical studies in business and management. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

키워드

Effect sizeFalse positiveLarge sample size biasStatistical inference
제목
Testing for signal-to-noise ratio in linear regression: a test under large or massive sample
저자
Kim, Jae H.Ji, Philip I.
DOI
10.1007/s11846-023-00706-0
발행일
2024-10
유형
Article
저널명
Review of Managerial Science
18
10
페이지
3007 ~ 3024