Request for functions to calculate correlated factors influencing an outcome.

Hi
I have a dataset of the type attached.
Here's my code thus far.
dataset <-data.frame(read.delim("data", sep="\t", header=TRUE));
newData<-subset(dataset, select = c(Price, Reliability, Mileage, Weight,
Disp, HP));

cor(newData, method="pearson");
Results are
                  Price Reliability    Mileage     Weight       Disp
HP
Price        1.0000000          NA -0.6537541  0.7017999  0.4856769
  0.6536433
Reliability         NA           1         NA         NA         NA
NA
Mileage     -0.6537541          NA  1.0000000 -0.8478541 -0.6931928
-0.6667146
Weight       0.7017999          NA -0.8478541  1.0000000  0.8032804
  0.7629322
Disp         0.4856769          NA -0.6931928  0.8032804  1.0000000
  0.8181881
HP           0.6536433          NA -0.6667146  0.7629322  0.8181881
  1.0000000

It appears that Wt and Price, Wt and Disp, Wt and HP, Disp and HP, HP and
Price are strongly correlated.
To find the statistical significance,
I am trying  sample.correln<-cor.test(newData$Disp, newData$HP,
method="kendall", exact=NULL)
Kendall's rank correlation tau

data:  newx$Disp and newx$HP
z = 7.2192, p-value = 5.229e-13
alternative hypothesis: true tau is not equal to 0
sample estimates:
       tau
0.6563871

If I try the same with
sample.correln<-cor.test(newData$Disp, newData$HP, method="pearson",
exact=NULL)

I get Warning message:
In cor.test.default(newx$Disp, newx$HP, method = "spearman", exact = NULL) :
   Cannot compute exact p-value with ties

sample.correln

Spearman's rank correlation rho

data:  newx$Disp and newx$HP
S = 5716.8, p-value < 2.2e-16
alternative hypothesis: true rho is not equal to 0
sample estimates:
       rho
0.8411566

I am not sure how to interpret these values.
Basically, I am trying to figure out which combination of factors
influences efficiency.

Thanks
Lalitha