[R] Correct interpretation of a regression coefficient

[Brian Smith]

Hi Michael,

You made an interesting point that, scale of the underlying variable
may be vastly different as compared with other variables in the
equation.

Could I use logarithm of that variable instead of raw? Another
possibility is that we could standardise that variable. But IMO, for
out of sample prediction, the interpretation of standardisation is not
straightforward.

On Sun, 8 Mar 2026 at 23:05, Michael Dewey <lists using dewey.myzen.co.uk> wrote:
>
> Dear Brian
>
> You have not given us much to go on here but the problem is often
> related to the scale of the variables. So if the coefficient is per year
> tryin to re-express time in months or weeks or days.
>
> Michael
>
> On 08/03/2026 11:50, Brian Smith wrote:
> > Hi,
> >
> > My question is not directly related to R, but rather a basic question
> > about statistics. I am hoping to receive valuable insights from the
> > expert statisticians in this group.
> >
> > In some cases, when fitting a simple OLS regression, I obtain an
> > estimated beta coefficient that is very small—for example, 0.00034—yet
> > it still appears statistically significant based on the p-value.
> >
> > I am trying to understand how to interpret such a result in practical
> > terms. From a magnitude perspective, such a small coefficient would
> > not be expected to meaningfully affect the predicted response value,
> > but statistically it is still considered significant.
> >
> > I would greatly appreciate any insights or explanations regarding this
> > phenomenon.
> >
> > Thanks for your time.
> >
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>
> --
> Michael Dewey
>