Interpreting this regression coefficient Quick background:
I am working on a political science project that involves analyzing the impact of different variables on the extent to which a candidate mentions other users when he or she tweets. 
One of these variables is whether the candidate answers the Political Courage Test (PCT). If he/she does, the value is 1. If they don't, it's 0.
Another variable is the amount of money that the candidate raises over the course of his/her campaign. 
Someone more experienced crunched the data for me via a regression, and sent me the following results:
PCT
Coefficient: 9.580
Standard error: 8.144

Amount of $$$ Raised
Coefficient: 0.000
Standard error: 0.000

PCT*Amount of $$$ Raised
Coefficient: 0.000
Standard error: 0.000

I have basically no background in statistics, so I am at a loss of how to interpret this outcome effectively. 
From what I can tell, neither the amount of money raised nor whether the candidate answered the CPT has much of an effect on the tweets, but I am confused about the third one (CPT*Money Raised), which I am told is an interaction. What exactly is that saying? 
Thank you in advance for your help.
 A: I am assuming that these coefficients come from a single regression (rather than 3 separate analyses). If that's not the case, the interpretation will be somewhat different. The outcome is number of tweets where the politician mentions another user.
The first coefficients means that answering the PCT is associated with 9.58 additional tweets, compared to a politician who did not answer. The standard error is rather large, so we cannot tell whether this association is truly there or found by chance. More data would be needed to tell the difference.
If the  $$$ is measured in dollars, the second coefficient means that an additional dollar raised is not associated with more or less tweets. In some ways this makes sense, since a single dollar is a tiny amount of money, so you would expect it to have hardly any influence. It might help if you divide this variable by 1000 to convert it it into thousands before running the analysis, so the coefficient would tell you the change from an extra $1,000, which might be more substantial. Alternatively, if you got more significant digits from your buddy, we could help with that calculation.
The third coefficient says that a politician who answered the PCT is expected to have the same number of tweets if he raises additional funds as a politician who did not answer and raised that dollar. Additional funds seem to have the same negligible effect for more forthcoming politicians. Again, the same warning about units applies here.
