If all of my coefficients in my logsitic model have really perfect t-statistics that all show sufficiently high significance but have two coefficients that have high VIF like 13-14 with sample size of 11000 for each independent variable, can I ignore the multicollinearity, given the way I propose to use the results?
I have a logistic regression model that has 6 independent variables where each independent variable and the dependent variable has the same sample size of 11000.
From this logistic regression I produce 6 predictions, based on changing the values of the independent variables. So let's say for prediction 1, I increase independent variable 1 by two units while all other independent variables are increased by only one unit. For prediction 2, I increase independent variable 2 by 2 units while all other independent variables are increased by only one unit and so on.
This makes a total of 6 different $y$ values which I need to use for my own purpose as below.
y = a + b1*2+b2*1+b3*1+b4*1+b5*1+b6*1 + error y = a + b1*1+b2*2+b3*1+b4*1+b5*1+b6*1 + error ... y = a + b1*1+b2*1+b3*1+b4*1+b5*1+b6*2 + error
I do this for each independent variable and produce 6 different predicted $y$ values accordingly. As you can see above, a two-unit increase for different independent variable each time so it makes a total of 6 different $y$ values.
So I have 6 independent variables and I have 6 different predicted $y$ values, based on changing each independent variable separately. Of course all intercepts and coefficients are highly significant at 0.01 level or less.
Then my main objective is to use the numerical values of these 6 different predicted $y$ values as an input to a separate function to produce a numerical output, a "utility value."
This "utility value" is the one I need. I want to show how this final utility value from a different model differs from that produced by this logistic regression model, as a function of how $y$ changes with different emphasis on independent-variable increases up to two units.