# High p-values in Probit model

Can someone help me interpret those results? I get very high p-value in my probit model but I do not understand why...

confucius = dependent (binary 1=yes, 0=no)

totalagri = continuous (tons)

bribes = percentage

size = continuous (km2)

coast = binary

agrigdp = percentage

impchina, expchina, gdp = continuous (value in \$)

lifeexp = continuous (years)

urbpop = percentage

coalprod, nargasprod, oilreserv = continuous

diamond = binary

cropprod = index

Thanks!

• Well... you have n=39 split up across 16 variables, for one thing. May 2 '14 at 14:28
• Thank you Alexis for your reply! That indeed seems obvious (I'm quite in this whole regressions world). I've removed/replaced some of the variables in order to loose less observations and I end up with this: imageshack.com/a/img835/4459/a9cp.png Some p-values are still high but would this be enough for my hypothesis: H1: The higher the agricultural production (here squared) the more likely to have confucius H0: No relation between them May 2 '14 at 14:29

As Alexis was already hinting, you have too many variables. It's time to throw out some variables, not add new ones. Or aggregate: e.g. all the oil+gas+coal production can be aggregated into the "energy" score.

Also, it seems to me that many variables (like "totalagri") should scale with the population size. I propose logging them all (totalagri, popsize. coalprod etc.). This way, all scaling relationships will be moved into the intercept term.

• Thanks! I have made those changes and it seems to work much better! May 4 '14 at 19:14