# What is the benefit of knowing the F statistic in multiple linear regression?

One of the basic figures you get when running multiple linear regression using almost any off-the-shelf software is the F statistics. However, I cannot recall one situation, where the F value was low enough that I could say, the ratio of model MSE and sample variance was not significant. I do understand that the figure makes sense if we compare two competing models, but the way it is usually reported by most software is by measuring the relative decrease in variance. Maybe I am overlooking something, but why report the F statistic at all?

• That they're often significant wouldn't be too surprising, since people try to avoid situations where they're rare (such as doing power studies before doing experiments). Nevertheless, I've seen many insignificant F's. If you always see them you're probably in a situation where hypothesis testing is less useful than things like point estimates and confidence intervals. Oct 10, 2014 at 8:59
• I do a lot of time series analysis, so I suppose this is a situation where they are less useful. What exactly is the domain where you see many insignificant F's?
– Nils
Oct 10, 2014 at 9:10
• One example: Student psych experiments where they simply can't get the sample size they need for the sort of effect size they expect (or, not infrequently, where the effect is just a lot smaller than they'd hoped). I've helped analyze a bunch of them. Oct 10, 2014 at 9:18
• Oct 17, 2020 at 2:41

The F-test may give you some useful information in some cases. For example, sometimes we find that according to the t-tests the regressors are individually not significant, while the F-test rejects the null that all the regressors (except the intercept) are jointly not significant. This may be a sign of multicollinearity among the regressors, which will lead to higher standard errors of parameter estimates and larger confidence intervals and should be somehow addressed (for example, creating a proxy variable that is a combination of some of the regressors).

• Thanks for the answer. I see your point. I did not think of multicollinearity. However, even in this extreme case, wouldn't it be enough to look at the R2 to become suspicious?
– Nils
Oct 10, 2014 at 8:57
• Yes, a high coefficient of determination in a model with individually not significant variables would raise the same suspicion. But the R2 would increase anyway as you add more and more variables even if there is no collinearity among them and are not significant, so it may be better to look at the F-test. Oct 10, 2014 at 9:05