Hello everyone,

i am currently in a seminar regarding credit scoring and we are trying to predict the probability, that a company is going to bankrupt, by looking at ratios from their financial statement (recreating the Olson O-Score with classification)

We got a group of 20 Companies, that went bankrupt in the last few years and a peer group of companies that are still solvent as well as all the relevant numbers to calculate our financial ratios.

Our professor told us, that we can use the *lda* function to determine the weights in our equation, but so far i am struggling to get an output, that makes sense. (i also havent used r in a while, so my question might also be stupid)

We are basically trying to find out the best wheigtings, so that if you sum up all the variables multiplied by said weighting you get a high sum for the defaulted companies and a low sum for the still solvent ones.

**Now when i use the lda function in RStudio this is the output i get:**

I have a total of 13 rows, 12 of which are ratios for calculation and the first one being the status (default/solvent) for classification.

I am not exactily sure, if the lda function makes it clear, that the sums in the end should have the biggest possible difference, because when i take the coefficients back into excel, the resulting probablities make absolutely no sense, and i can get better results just by targeted guessing the coefficients.

Does someone know how i can optimize my outpout here to get the desired results?

Thanks alot for any help.