Dears helpers,
I have a question not specially about R, but about statistical modelling. This
is the problem, I want to conduct a regression analysis to explain the causes
of students fail in an exam. I have two variables the score obtain at the exam
and the categorical variable coding 0 in case of fail and 1 in case of success,
on a panel data. I am interest to know what made a student who usually succeed
start to fail. So I have two possibilty : linear regression analysis with the
score or a probit model with the fail indicator.
In my view, the result will be similary. but is there any difference? what the
advantage off choosing one the two approaches ?
Sincerly
Justin BEM
Elève Ingénieur Statisticien Economiste
BP 294 Yaoundé.
Tél (00237)9597295.
___________________________________________________________________________
[[alternative HTML version deleted]]
______________________________________________
R-help@stat.math.ethz.ch mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
