wloss data frame:
   center loss
1       C  8.5
2       C 11.6
3       C 10.2
4       C 10.9
5       C  9.0
6       C  9.6
7       C  9.9
8       C 11.3
9       C 10.5
10      C 11.2
11      D  8.7
12      D  9.3
13      D  8.2
14      D  8.3
15      D  9.0
16      D  9.4
17      D  9.2
18      D 12.2
19      D  8.5
20      D  9.9
Classical ANOVA summary:
            Df Sum Sq Mean Sq F value  Pr(>F)  
center       1  5.000  5.0000   4.174 0.05597 .
Residuals   18 21.562  1.1979                  
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Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 
Vector of dummy values:
 [1] 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0
with_summy data frame
   dummy loss
1      1  8.5
2      1 11.6
3      1 10.2
4      1 10.9
5      1  9.0
6      1  9.6
7      1  9.9
8      1 11.3
9      1 10.5
10     1 11.2
11     0  8.7
12     0  9.3
13     0  8.2
14     0  8.3
15     0  9.0
16     0  9.4
17     0  9.2
18     0 12.2
19     0  8.5
20     0  9.9
Regression model with dummy variable

Call:
lm(formula = loss ~ dummy, data = with_dummy)

Residuals:
   Min     1Q Median     3Q    Max 
-1.770 -0.695 -0.070  0.630  2.930 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)   9.2700     0.3461  26.784 5.92e-16 ***
dummy         1.0000     0.4895   2.043    0.056 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

Residual standard error: 1.094 on 18 degrees of freedom
Multiple R-squared: 0.1882,     Adjusted R-squared: 0.1431 
F-statistic: 4.174 on 1 and 18 DF,  p-value: 0.05597