# Interpretation of OLS regression coefficient of -1.19 when depent variable ranges from 0-100

In an experiment, the participants were asked to allocate 100% of their budget to three categories in different scenarios. Now, I am analyzing the allocation to one particular category via OLS regression, where the dependent variable is the allocation to said good in a range from 0-100.

I have used the linear probability model (as requested by my instructor) (i.e., normal regress functions in Stata). Now, one coefficient that measures study hours amounts to -1.19, and I'm uncertain of the correct interpretation.

Analyzing the other two categories, I had coefficients of 0.67 and 0.91, which I interpreted as "the increase of one additional study hour is associated with a 67 / 91 percentage point increased allocation to the good.

As my dependent is restricted to a range of 0 -100 (as we did not allow borrowing additional money), I thought that the coefficient of the first category above 1 was a limitation of the normal regression, and I should rather use logit instead.

The logit coefficient amounts to -.2383773, and the odds ratio is .7879054.

I would be most grateful if anyone could tell me how to interpret the -1.19.

The idea is, we know the model is fundamentally flawed as it can produce values of the dependent variable that are outside the feasible range. However, perhaps it can be useful locally (for some "typical" values of $$X$$s).