Hi everyone,

For a school project I'm writing a paper about the correlation between public transit usage and accident rates. I'm analyzing New Jersey. I have the number of total crashes, crashes that caused injuries, and crashes that resulted in fatalities for every county and the state as a whole from 2005 to 2017. This means I only have 22 observations per year--I know that's a problem, but not sure how much of it is a problem. My professor is okay with the lack of internal validity and values the experience of having created the project more than reaching statistically valid conclusions.

I've got a bunch of variables for controls, including economic conditions state-wide (not by county), county size, median age, median income, and minority populations:
njrgdp_ njur_ countysizesqmi_ asianAlone_ blackAlone_ hispanic_ medianAge_ medianIncome_ otherAlone_ population_ totalPopForRaceCalculations_ twoOrMoreRaces_ whiteAlone_

I used xtset to make panel data with my counties and years and ran the following regression:

xtreg l_tcrash l_pt l_cs l_pop l_white njur_ njrgdp_, fe
Just to be sure: this is taking my dependent variable (log of % of total crashes per worker in the county) and my independent variables (log of % of workers that take public transit, log of county size, log of population of county, log of % white of the county, the NJ unemployment rate and real GDP), and telling it to do that with fixed effects per county and year?

When I do that, I get the table that I've attached below.

Honestly I'm totally lost as to how to proceed.
1) I have no idea how to interpret a good amount of this, especially the coefficients on the logs.
2) Why would the log of county size be omitted because of collinearity?
3) Does anyone have any other tips on how to beef up the statistical validity of my project or is it hopeless?

Thank you so much for any help.