Regression Analysis - Transformations and Non-Normality


I am fairly new to regression analysis and have a few questions..

I have a fairly large dataset containing information about 144 municipalities in a country. I am trying to create a model that uses a number of independent variables (urban ratio, literacy rate etc.) to model the migration in each municipality. My independent variables are a mix of binary (0,1) and continuous. I was wondering if anyone could point me in the right direction of where to start with analysing my variables? I have done a simple linear regression and that has produced no significant results. It has been suggested to me that I could try and transform some of the variables (as they are non normal) or to use a model that does not assume normality.

What sort of transformations are there and why might they be useful? What are the other models available that do not assume normality?

I have also been told to look at GLMs....

Sorry for the long message.. but I desperately need help!
Migration is defined (at the moment- until I can get better data) as a person who was not born in the municipality. The data will be as a percentage of the population.

I also want to create a second model with population change as the dependent variable (min -41% max 163%).

I am trying to answer the question of whether an increase in an industrial activity is having a more than proportional effect on migration than expected.