What does this linear regression summary tells us? I just started learning predictive modelling in R , however i do understand some terms below but i lack in making more interpretations ,  just want to know what a pro statistician or Data Scientist interpret from it . How does one look at it ?

&gt; summary(model)

Call:
lm(formula = comp$Minutes ~ comp$Units)

Residuals:
    Min      1Q  Median      3Q     Max 
-9.2318 -3.3415 -0.7143  4.7769  7.8033 

Coefficients:
            Estimate Std. Error t value Pr(&gt;|t|)    
(Intercept)    4.162      3.355    1.24    0.239    
comp$Units    15.509      0.505   30.71 8.92e-13 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 5.392 on 12 degrees of freedom
Multiple R-squared:  0.9874,    Adjusted R-squared:  0.9864 
F-statistic: 943.2 on 1 and 12 DF,  p-value: 8.916e-13
The global test shows that the model is significant as the p value is very low which is p-value: 8.916e-13 in alpha level of 0.05. 98.64% of the variability of Minutes  is described by Units. The effect of Units is significant in predicting Minutes. For each one unit value increase in Units, Minutes increases by 15.509.