MultiReg regression output

Call:
lm(formula = y ~ x1 + x2)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.9689 -1.2856 -0.5322  0.6461  3.8178 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 5.662e+01  2.277e+00   24.87 2.78e-07 ***
x1          4.782e-03  9.563e-04    5.00  0.00245 ** 
x2          7.186e-01  6.376e-02   11.27 2.92e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.343 on 6 degrees of freedom
Multiple R-squared:  0.962,     Adjusted R-squared:  0.9494 
F-statistic: 76.01 on 2 and 6 DF,  p-value: 5.474e-05

Estimated regression parameters
using beta = (X'X)^-1 X'Y.

Matrix X:
          x1 x2
 [1,] 1 1000  0
 [2,] 1 1000 15
 [3,] 1 1000 30
 [4,] 1 2000  0
 [5,] 1 2000 15
 [6,] 1 2000 30
 [7,] 1 3000  0
 [8,] 1 3000 15
 [9,] 1 3000 30
 
Matrix Y:
       [,1]
 [1,] 60.58
 [2,] 72.72
 [3,] 79.99
 [4,] 66.83
 [5,] 80.78
 [6,] 89.78
 [7,] 69.68
 [8,] 80.31
 [9,] 91.99

Estimated Parameter Vector beta:
           [,1]
   56.620555556
x1  0.004781667
x2  0.718555556