# OLS: why is it possible to get insignificant F-test but resonably high adjusted R-squared?

I am estimating an OLS regression with 158 observations and about 140 regressors (some unstructured data features to personality measure). Below is the bottom output I get from OLS. Some of the regressors turn out to be significant at 0.05 level. But I am wondering why may there be a case when adjusted R-squared is reasonable but p-value for F-test is not. Does this mean that the model is not valid even though some of the regressors are significant and adjusted R-square is reasonable?

And what could be the reason causing this and how to remedy this?

Residual standard error: 0.5457 on 19 degrees of freedom
Multiple R-squared: 0.9083, Adjusted R-squared: 0.2424
F-statistic: 1.364 on 138 and 19 DF, p-value: 0.2205

• Your results make a lot of sense as you're dramatically overfitting to your data. You basically have the same number of observations as regressors. You need to dramatically reduce the number of regressors you're using (~10). This shows the pitfalls of the R-squared. Your model is bad, but the R-squared is still really high. Mar 26, 2019 at 6:20
• Possible duplicate of High R-squared although many insignificant coefficients Mar 26, 2019 at 9:34
• I don't think this is a duplicate. In that problem, there were lots of missing data and the overfitting (even with listwise deletion) was not nearly so severe. Mar 26, 2019 at 11:03

Don't just look at adjusted $$R^2$$, look also at the gap between that and the regular $$R^2$$. Here it is huge (dropping by 0.65). That is a sign of overfitting.