Help needed on applying a function across different data sets and aggregating the results into a single data set

Hi folks,

Wish y'all a Happy New Year 2010!

I need some help with the following:

Say I have lots of data sets, on which I have to apply a certain function
on the same set of columns in each of the data set. Let's take, for ex,
the typical data set is:

df1 <- as.data.frame(cbind(rnorm(10),rnorm(10)))
names(df1)[1] <- "A"
names(df1)[2] <- "B"

There are many such data sets, df2,df3,... I have the names stored in a
list
DF <- cbind("df1","df2",...,"df100")

I now need to apply the following function:
a <- lsfit(df1$A,df1$B)

and stack up the following results:
a$coef
 Intercept          X 
-0.1479750  0.2485416 

So, I would end up with as many rows as there are data sets.

I think sapply would be the function I should be looking for (at least I
have used it in the case of applying a function across different columns
of the same data set), but for some reason I'm not able to nail down the
final stages in this case. 

Earlier, I used something like the following in the case of applying a
function across all columns(except the first) of the same data set:
my.func <- function(x){
mod <- lrm(my.data$y ~ x) 
data.frame(t(anova(mod)[1, ]), R2 = mod$stats[10]) 

sapply(my.data[,-1],my.func)

Where I need help is how to pass the reference of the names of the
different data sets to the sapply funtion.

Thanks!
-Girish

====================================
sessionInfo()
R version 2.10.0 (2009-10-26) 
i386-pc-mingw32 

locale:
[1] LC_COLLATE=English_United States.1252  LC_CTYPE=English_United
States.1252   
[3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C                          
[5] LC_TIME=English_United States.1252    

attached base packages:
[1] grid      stats     graphics  grDevices utils     datasets  methods  
base     

other attached packages:
[1] lattice_0.17-26 RWinEdt_1.8-2   ggplot2_0.8.5   digest_0.4.2   
reshape_0.8.3  
[6] plyr_0.1.9      proto_0.3-8    

loaded via a namespace (and not attached):
[1] Formula_0.2-0    kinship_1.1.0-23 MASS_7.3-4       nlme_3.1-96     
plm_1.2-1       
[6] sandwich_2.2-4   splines_2.10.0   survival_2.35-8  tools_2.10.0