I am a new B for R, so as this mailing list too. Thank you very much to those who will reply me and the contrubutors, THANK YOU. let me tell my problem that I am encountering now I have grabbed some data from census department to do an analysis. however, it is too bad that the data have been handle so I cannot treat them as the homework i did in school. I am going to test the determinants of the people want to move to developing city from developed city EXAMPLE suppose there is 3 individuals a,b,c a = age <30, salary 10k, and edu.level=highschool move b = age 30~40, salary 30k, and edu.level=college not move c = age 40~50, salary 20k, and edu.level=highschool move (IT IS JUST AN EXAMPLE TO TELL MY SITUATION, I HAVE THOUSAND OF DATA SETS INTEAD OF 3) I used <summary(glm(mydata)) and all the variable are significant, but once i do it as 3 models <summary(glm(move~edu.level)) <summary(glm(move~salary)) <summary(glm(move~age)) only salaries is significant, all the rest pvalue>0.2..too bad furthermore, i tried <summary(glm(mydata, family=binomial)) only salary significant too... why does it happen? the result affects by the "family=binomial"... i m not sure how it works.... further more actually, my data is not that good as shows as individual..instead... aged <30 move , 30~40 not move , 40~50 move salary.20k move, 30k not move, 40k move, edu.level highschool move, highschool move, college not move all the categories have been rearranged and i dont know which of each characteristic is with respect to which person. do u think I will miss some correlation for those factors? or i can test them group by group only..(move vs age, SAY AGE <30 ARE MORE MOBILITY) Thank you very much again. sbest regards Ben [[alternative HTML version deleted]]