[R] interpreting anova summary tables - newbie

[Andrew McDonagh]

Hello,

Apologies if this is the wrong list, I am a first-time poster here. I 
have an experiment in which an output is measured in response to 42 
different categories.
I am only interested which of the categories is significantly different 
from a reference category.

Here is the summary of the results:

summary(simple.fit)

Call:
lm(formula = as.numeric(as.vector(TNFa)) ~ Mutant.ID, data = 
imputed.data)

Residuals:
      Min       1Q   Median       3Q      Max
-238.459  -25.261   -0.868   25.660  309.496

Coefficients:
             Estimate Std. Error t value Pr(>|t|)
(Intercept)  49.0479    10.5971   4.628 5.08e-06 ***
Mutant.IDB  149.8070    23.1632   6.467 3.09e-10 ***
Mutant.IDC   98.7443    23.1632   4.263 2.55e-05 ***
Mutant.IDD   97.2203    23.1632   4.197 3.37e-05 ***
Mutant.IDE  118.9820    23.1632   5.137 4.49e-07 ***
Mutant.IDF  241.8537    23.1632  10.441  < 2e-16 ***
Mutant.IDG  107.4883    23.1632   4.640 4.80e-06 ***
Mutant.IDH  105.7664    23.1632   4.566 6.74e-06 ***
Mutant.IDI  517.4650    23.1632  22.340  < 2e-16 ***
Mutant.IDJ   19.7777    23.1632   0.854 0.393735
Mutant.IDK   47.4240    23.1632   2.047 0.041313 *
Mutant.IDL    3.2542    23.1632   0.140 0.888347
Mutant.IDM  180.9638    23.1632   7.813 5.63e-14 ***
Mutant.IDN   19.0582    23.1632   0.823 0.411155
Mutant.IDO   61.8684    23.1632   2.671 0.007891 **
Mutant.IDP   -0.5306    23.1632  -0.023 0.981738
Mutant.IDQ  -10.6972    23.1632  -0.462 0.644478
Mutant.IDR    1.5377    23.1632   0.066 0.947107
Mutant.IDS   14.6333    23.1632   0.632 0.527934
Mutant.IDT   48.8900    23.1632   2.111 0.035458 *
Mutant.IDU   58.9597    23.1632   2.545 0.011313 *
Mutant.IDV   81.7657    23.1632   3.530 0.000467 ***
Mutant.IDW   82.9576    23.1632   3.581 0.000386 ***
Mutant.IDY   49.1926    23.1632   2.124 0.034343 *
Mutant.IDZ   51.0381    23.1632   2.203 0.028170 *
Mutant.IDZA 116.0487    23.1632   5.010 8.38e-07 ***
Mutant.IDZB  56.4402    23.1632   2.437 0.015287 *
Mutant.IDZC -14.5305    23.1632  -0.627 0.530838
Mutant.IDZD  -5.0069    23.1632  -0.216 0.828983
Mutant.IDZE   9.1176    23.1632   0.394 0.694080
Mutant.IDZF 232.2879    23.1632  10.028  < 2e-16 ***
Mutant.IDZG -27.1671    23.1632  -1.173 0.241595
Mutant.IDZH   0.8757    23.1632   0.038 0.969862
Mutant.IDZI   4.7952    23.1632   0.207 0.836108
Mutant.IDZJ  -5.5859    23.1632  -0.241 0.809568
Mutant.IDZK -12.9263    23.1632  -0.558 0.577138
Mutant.IDZL  38.8621    23.1632   1.678 0.094224 .
Mutant.IDZM  39.2643    23.1632   1.695 0.090880 .
Mutant.IDZN  73.8419    23.1632   3.188 0.001553 **
Mutant.IDZO 147.7804    23.1632   6.380 5.20e-10 ***
Mutant.IDZP   0.5654    23.1632   0.024 0.980540
Mutant.IDZQ  50.5117    23.1632   2.181 0.029824 *
Mutant.IDZR 217.6824    23.1632   9.398  < 2e-16 ***
Mutant.IDZS 237.3227    23.1632  10.246  < 2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 61.79 on 377 degrees of freedom
Multiple R-Squared: 0.7351,     Adjusted R-squared: 0.7049
F-statistic: 24.33 on 43 and 377 DF,  p-value: < 2.2e-16

 >

My question relates to the meaning of the p-values. Do the p-values 
relate to
a) the confidence in the estimate
or
b)the confidence that the non-intercept categories are different to the 
intercept

Somebody mentioned to me that the p-value for the intercept is the 
confidence in the estimate of the intercept, whereas the remaining 
entries are the confidence in each strain being different from the 
reference / intercept

Note the contrasts setting is contr.treatment.

Any help would be appreciated

Andrew McDonagh,
PhD Candidate,
Department of Infectious Diseases,
Commonwealth Building,
Hammersmith Hospital,
Du Cane Road,
London W12 ONN

a.mcdonagh at imperial.ac.uk