Lets do a full fit on all the predictors
fitall <- lm(mpg ~ ., mtcars)
summary(fitall)$coef
Estimate Std. Error t value Pr(>|t|)
(Intercept) 12.30337416 18.71788443 0.6573058 0.51812440
cyl -0.11144048 1.04502336 -0.1066392 0.91608738
disp 0.01333524 0.01785750 0.7467585 0.46348865
hp -0.02148212 0.02176858 -0.9868407 0.33495531
drat 0.78711097 1.63537307 0.4813036 0.63527790
wt -3.71530393 1.89441430 -1.9611887 0.06325215
qsec 0.82104075 0.73084480 1.1234133 0.27394127
vs 0.31776281 2.10450861 0.1509915 0.88142347
am 2.52022689 2.05665055 1.2254035 0.23398971
gear 0.65541302 1.49325996 0.4389142 0.66520643
carb -0.19941925 0.82875250 -0.2406258 0.81217871
