# Linear Regression issue with model evaluation

Of these below 3 models output , though model 3 has a insignificant value in it , it has higher r square value and lesser F stat compare to model 2. In Model 2 though both variables are significant, the F statistic and RMSE value(1.638) is slightly higher than my model 3 (rms3 is 1.636). Which model should I consider here and why ? Can someone shed some light on this pleaes

 - model 1

Call:
lm(formula = tr$Sales ~ TV + Facebook, data = tr) Residuals: Min 1Q Median 3Q Max -8.6942 -1.4420 -0.1152 1.4358 7.8306 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 5.757673 0.571084 10.082 < 2e-16 *** TV 0.049902 0.002789 17.893 < 2e-16 *** Facebook 0.036963 0.010863 3.403 0.00086 *** Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 2.963 on 147 degrees of freedom Multiple R-squared: 0.6983, Adjusted R-squared: 0.6942 F-statistic: 170.1 on 2 and 147 DF, p-value: < 2.2e-16  model 2 summary(lfit2) Call: lm(formula = tr$Sales ~ TV + Adwords, data = tr)
Residuals:
Min      1Q  Median      3Q     Max
-8.5129 -0.5817  0.1659  1.1445  2.7496
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.926869   0.334627   8.747 4.68e-15 ***
TV          0.047292   0.001564  30.246  < 2e-16 ***
Adwords     0.180630   0.009500  19.015  < 2e-16 ***
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.655 on 147 degrees of freedom
Multiple R-squared:  0.9059,    Adjusted R-squared:  0.9046
F-statistic: 707.7 on 2 and 147 DF,  p-value: < 2.2e-16


model 3

summary(lfit3)

Call: