Add model sig for linear models

This is something often reported, but I couldn't find an easystats way to get this?

m <- lm(mpg ~ ., data = mtcars)
summary(m)
#> 
#> Call:
#> lm(formula = mpg ~ ., data = mtcars)
#> 
#> Residuals:
#>     Min      1Q  Median      3Q     Max 
#> -3.4506 -1.6044 -0.1196  1.2193  4.6271 
#> 
#> Coefficients:
#>             Estimate Std. Error t value Pr(>|t|)  
#> (Intercept) 12.30337   18.71788   0.657   0.5181  
#> cyl         -0.11144    1.04502  -0.107   0.9161  
#> disp         0.01334    0.01786   0.747   0.4635  
#> hp          -0.02148    0.02177  -0.987   0.3350  
#> drat         0.78711    1.63537   0.481   0.6353  
#> wt          -3.71530    1.89441  -1.961   0.0633 .
#> qsec         0.82104    0.73084   1.123   0.2739  
#> vs           0.31776    2.10451   0.151   0.8814  
#> am           2.52023    2.05665   1.225   0.2340  
#> gear         0.65541    1.49326   0.439   0.6652  
#> carb        -0.19942    0.82875  -0.241   0.8122  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Residual standard error: 2.65 on 21 degrees of freedom
#> Multiple R-squared:  0.869,  Adjusted R-squared:  0.8066 
#> F-statistic: 13.93 on 10 and 21 DF,  p-value: 3.793e-07

Talking about the last line here: F-statistic: 13.93 on 10 and 21 DF, p-value: 3.793e-07.

(On a related note - maybe compare_performance(m1,m2) can give the sig tests from anova(m1,m2)?)

Start from the requested linear-model output, focusing on the F-statistic, its numerator and denominator degrees of freedom, and p-value. Review the existing performance metrics for linear models and determine how the related compare_performance() and anova() request should be scoped; done means the requested significance result is exposed with coverage for the example.