I have a huge data set that looks roughly like this:
x = [x1, x2, x3, ..., x800] x is how much percent the real project cost is different from the planned project cost y1 = [y1.1, y1.2, y1.3, ..., y1.800] y1 is the estimated project cost in dollar (or another currency) y2 = [y2.1, y2.2, y2.3, ..., y2.800] y2 is the real projet duration y3 = [y3.1, y3.2, y3.3, ..., y3.800] y3 is the project manager (in coded form like "mk" and "op") .... y50 = [y50.1, y50.2, y50.3, ..., y50.800] y50 is the estimated project duration
Now the aim is to predict the
x (cost difference in percent) in dependency of other
y variables. It is a priori not clear which variables influence it. And some of the
y are not interdependent like for example the difference in percent
x, the real costs and the estimated costs since
x is calculated from the real and estimated costs.
I am now wondering how is the best practice way to analyse the data or how to build a model (linear or maybe logistic). The binary output for the logistic model could be difference bigger than 10 %.
Since I have a lot of data sets (over 800) almost everything could get significant on a e. g. 5 % level. And It's not easy to handle/control the power if you do not have a simple t-test.
What would you recommend? Is there a good book for "normal people" about that kind of statistics?