Surprising (or not so)

I was working my ass for this econometrics assignment of mine. For those of you who have no idea what the heck is that, its still a field/study/specialisation under economics.

In a laymen term: A study and/or solution of a particular problem/issue through the perspective of mathematical, statistical and economics.

I didn't google that up, so don't take that as an official definition. I could be wrong. I'm just quoting and translating back by memory from what my lecturer has said.

ANYWAY, we're suppose to come up with this 'mini thesis' assignment which is freely based on any topics of our choice. It's quite simple...decide on a problem/issue, get the data, process and interpret the results, transfer everything into a thesis form.

I was working my way from middle to front and then to the back. So my issue was decided based on the data I found. The issue I was working on was that road accidents are affected by the factor of the number of commercial vehicles (lorry, trailer) and the amount of summons issued by our cops during Ops Sikap.

To prove that I'm not lying, here's the output I got from the data.

Parameter Estimates

Parameter Standard
Variable DF Estimate Error t Value Pr > |t|

Intercept 1 5.01346 1.77470 2.82 0.2166
LX1 1 -0.15874 0.16041 -0.99 0.5033
LX2 1 0.72740 0.13309 5.47 0.1152

Do u see the -1.5874? That means its a negative relation among 2 variables.
So the 0.7274 is showing a positive relation. Simple enough rite?

Here goes:
In a laymen lingo, it means the more summons issued by the cops, the higher the amount of road accidents gets!! Lol. This is really interesting. Probably everyone speeds off right after the road block knowing there wont be another speed trap bout 200km ahead.

Anyway this research is lacking in alot of areas like lack of data (data from 2005-2008), accuracy of the given data, should be using the number of summons throughout the whole year (but I cant get it) etc etc.
Afterall the given varibles are not significant in explaining the model/equation. It means the data doesn't make much of an effect towards the number of accidents. But then again, it means that Ops Sikap doesn't make a significance change either! Ha!

So eventually I changed to a more boring topic which is about Malaysia economics. Very bland and textbook-like.

Moral of the story: Ops Sikap doesn't make a difference (besides financially)