I am a beginner who needs some help reading some results from linear regressions.
I am looking for factors that influence the location of civil conflict events. My dependent variable is the distance from the capital (km), a continuous variable.
I have 4 control variables which are all continuous too.
The variable (fightcap) I want to test is the fight capability of the rebels. This ordinal variable has three levels:
1 = low
2 = moderate
3 = high
However, I read in a couple of textbooks that to deal with ordinal predictors it is better to convert them to dummy variables. I thus created two dummies:
fightCap(low): 1 if fightcap = 1, 0 otherwise
fightCap(moderate): 1 if fightCap = 2, 0 otherwise
Using JMP (the software I know best), I ran the two models in parallel: one (left) with the fightcap ordinal variable, the second (right) with both fightCap(Low) and fightcap(moderate) dummy variables.
I notice that the output is identical for both models, excepts regarding the estimates of the ordinal/dummy variables.
My questions are thus the following:
How do I interpret the terms "fightcap[2-1]", "fightcap[3-2]" and their estimates? Do I read that a change of "fightcap" from 1 to 2 results in a 59.42 change in the dependent variable?
How do I interpret the fact that "fightcap[2-1]" has a significant estimate, but not "fightcap[3-2]"? Does it mean that only the variation from 1 to 2 explains the variation, but not the variation from 2 to 3?
How can I understand that "half" of my variable works if using the ordinal setup, but none of the dummies has a significant estimate?
Thanks for your help!