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Re: st: Why F-test with regression output

From   John Antonakis <>
Subject   Re: st: Why F-test with regression output
Date   Thu, 05 May 2011 00:05:55 +0200

Here's an nice paper that discuss different issue associated with the F-test:

Geary, R. C., & Leser, C. E. V. (1968). Significance Tests in Multiple Regression. The American Statistician, 22(1), 20-21.



Prof. John Antonakis
Faculty of Business and Economics
Department of Organizational Behavior
University of Lausanne
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CH-1015 Lausanne-Dorigny
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Associate Editor
The Leadership Quarterly

On 04.05.2011 23:54, Joerg Luedicke wrote:
On Wed, May 4, 2011 at 5:19 PM, Steven Samuels<>  wrote:
Nick, I've seen examples where every regression coefficient was non-significant (p>0.05), but the F-test rejected the hypothesis that all were zero.
This can happen even when the predictors are uncorrelated. So I don't consider the test superfluous.
This is not surprising since "p>0.05" does not mean that the
contribution of a predictor is zero. I cannot see an argument here why
this F-test is not superfluous? I personally think that these kinds of
omnibus significance tests are useless since they carry little
information and thus little meaning. Say we have a model with 5
predictors, I want to see what each contributes in terms of effect
sizes. If I see that all effects are essentially zero I can interpret
that accordingly. What would it help if I looked at the F-test which
does not even carry information about things like effect sizes etc.?

I guess it is always printed out because it became standard at some
point in time, and I believe especially in Psychology and experimental
research. In Psychology, a common modeling strategy is to do an
omnibus test first and if the null-hypothesis is rejected a more
closer look is warranted. If not, the model gets discarded right away.

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