The most usual thing in this situation would be to think of "insurance" variable as adependentvariable and "age", "income" and "education" asexplanatoryvariables, that is as independent variables. Then you would have a model as something like this one:

insurance = a +b1*age + b2*income +b3* education + error

That would be called a multiple regression model. (Skip the thoughts about multivariate models. That is an other thing.)

But if the insurance variable is a "have" or "do not have" insurance, the you will need to use a logistic = logit model:

log(p/(1-p)) = a +b1*age + b2*income +b3* education

Where p is the proportion having an insurance at the given value of the explanatory variables. Don't worry if it looks complicated. The computer takes care of it and estimates the b1, b2 and b3 and gives you significance test.