>>> model = lm(sales~TV, data=train)
>>> summary(model)
Call:
lm(formula = sales ~ TV, data = train)

Residuals:
    Min       1Q   Median      3Q     Max
-8.3138  -1.9024  -0.1591  2.0736  7.2839

Coefficients:
             Estimate Std.  Error t value  Pr(>|t|)
(Intercept)  6.962685    0.548144   12.70   <2e-16 ***
TV           0.047528    0.003222   14.75   <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 3.294 on 138 degrees of freedom
Multiple R-squared: 0.6119, Adjusted R-squared: 0.6091
F-statistic: 217.6 on 1 and 138 DF, p-value: < 2.2e-16

>>> Y_pred = predict(model, test)
>>> mean((Y_pred-test$sales) ** 2)   # MSE
10.10929