I am a master student in Biology. I am now trying to analyse my thesis data, but I am facing a problem: I do not understand which test I should use for my large dataset containing one response variable and multiple explanatory variables (factor and numeric values!).

Please let me explain my problem in more detail:

- I have a dataset with one response variable and multiple explanatory variables. The response variable is nitric oxide (NO) production by my cells. The explanatory variables consist of factor variables (e.g. type of medium used, type of cell culture) and numeric variables (growth expressed in doubling time, general responsiveness of the cells, etc.)

- I now want to know which of the explanatory variables explain or predict the outcome of my response variable. For example, is the extent of NO production by my cells dependent on the type of medium used, or on the doubling time, or ...?

I have already been thinking of performing:

- Multiple regression analysis. The problem is here that I do not understand how to add my factor variables here. I also think that the combination of the explanatory variables are very important (so that I do not really find strong correlations between the NO production and only one other variable).

- Prinicipal component analysis. It did not work out very well, because here you can not select the response variable (or can I? I don't know how...)

- Model analysis. It would be ideal - I think - to make a model containing all the variables that have some influence on the response variable. However, I do not know how to do this with the factor variables. Somehow I might have to split my analysis in factor and numerical values.

Does anyone have any suggestion how to analyse these data? All replies are very welcome, I am getting a little desperate here..

Please let me know if I expressed myself unclear

Thanks!

Cass