*** Linear Model ***

Call: lm(formula = V6 ~ V2 + V11 + V3 + V4 + V5 + V7 + V8 + V9 + V10 + V14 + V17,
 data = SDF22, na.action = na.exclude)
Residuals:
   Min     1Q Median    3Q  Max
 -1032 -177.9 -20.33 112.6 1103

Coefficients:
                Value Std. Error   t value  Pr(>|t|)
(Intercept)  465.6042  148.6525     3.1322    0.0020
         V2 -165.4611   97.0289    -1.7053    0.0897
        V11  -25.3774  143.4523    -0.1769    0.8598
         V3 -128.7073   85.3830    -1.5074    0.1332
         V4  -18.7181   98.7410    -0.1896    0.8498
         V5    0.1081    0.0202     5.3438    0.0000
         V7    3.0822    0.7325     4.2077    0.0000
         V8 -133.5404  123.1058    -1.0848    0.2793
         V9 -142.2426  125.4316    -1.1340    0.2581
        V10  375.9035  197.6838     1.9015    0.0586
        V14  -53.7757   23.5811    -2.2805    0.0236
        V17    2.4214    0.4415     5.4849    0.0000

Residual standard error: 311 on 206 degrees of freedom
Multiple R-Squared: 0.4942
F-statistic: 18.3 on 11 and 206 degrees of freedom, the p-value is 0

上面是本人一分调查数据所做的线性回归，V2,V3,V4和V8,V9,V10,V11是0-1变量，0.05的置信度，请问上面的回归拟合得怎样？