The `predict` method for regressions typically offers estimates of the "confidence interval" and the "prediction interval" as separate calculations. The former characterizes how well your regression estimates the systematic behavior of your data, while the latter addresses how precisely the regression can predict specific values.
You should research the pitfalls of p-values... they tell you a lot about repeatability but little about significance. You should also not just think "differences in scale->log transformation"... logarithms "straighten out" data that is intrinsically exponential.... if that is how your data behaves then logarithms will help you linearize the analysis. There are lots of data arising from non-exponential processes for which a log will probably not help. On March 8, 2026 11:15:54 AM PDT, Brian Smith <[email protected]> wrote: >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 <[email protected]> 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. >> > >> > ______________________________________________ >> > [email protected] mailing list -- To UNSUBSCRIBE and more, see >> > https://stat.ethz.ch/mailman/listinfo/r-help >> > PLEASE do read the posting guide >> > https://www.R-project.org/posting-guide.html >> > and provide commented, minimal, self-contained, reproducible code. >> >> -- >> Michael Dewey >> > >______________________________________________ >[email protected] mailing list -- To UNSUBSCRIBE and more, see >https://stat.ethz.ch/mailman/listinfo/r-help >PLEASE do read the posting guide https://www.R-project.org/posting-guide.html >and provide commented, minimal, self-contained, reproducible code. -- Sent from my phone. Please excuse my brevity. [[alternative HTML version deleted]] ______________________________________________ [email protected] mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide https://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
