# Analysing data to find the principal parameter

I have a data set $N\times M$, which contains information about motorcycles: $N$ motorcycles have been sold during some time and for each bike there is $M$ parameters regarding the sale such as price, year it was built, where it was sold geographically etc.

I would like to investigate this data further and find out just which of these $M$ parameters best determine what price a bike is sold at.

I have previously heard about Principal Component Analysis and read some small notes about it. Can I use PCA for this, or are there other methods that suit this problem better?

First, I suggest you make univariate descriptions of your $M$ variables, then bivariate statistics, such as the correlation matrix of numerical variables. You can also make plots price vs other variables. After this you'll know a little more about your data and the relationships between the variables.
Then, I think a PCA is actually a good idea but it won't be enough. With a PCA you'll be able to reduce the dimension of your data from $M$ to a much smaller dimension (except if your variables are uncorrelated, which I doubt) and to visualize your data. Interpreting the principal factors will probably be very useful. You may also detect several groups between your observations.