I'm struggling a bit with interpreting the output of a linear regression in Weka. This is my model:
0.1063 * checking_status=0<=X<200,>=200,no checking + 0.1329 * checking_status=>=200,no checking + 0.0593 * checking_status=no checking + 0.2201 * credit_history=existing paid,delayed previously,critical/other existing credit + 0.0963 * credit_history=critical/other existing credit + 0.3752 * purpose=other,new car,repairs,business,domestic appliance,furniture/equipment,radio/tv,used car,retraining + -0.2895 * purpose=new car,repairs,business,domestic appliance,furniture/equipment,radio/tv,used car,retraining + 0.0988 * purpose=business,domestic appliance,furniture/equipment,radio/tv,used car,retraining + 0.0953 * purpose=used car,retraining + -0 * credit_amount + 0.103 * saving_status=no known savings,500<=X<1000,>=1000 + 0.0839 * employment=4<=X<7 + 0.0619 * personal_status=male mar/wid,male single + 0.0022 * age + 0.0344
Could someone explain how should I interpret different coefficients for different values of the same attribute? For example, checking_status has been divided into three separate "brackets", each with its own different coefficient. Does it mean that if "checking_status=>=200,no checking" has a coefficient of 0.1329, then it is somewhat "more important" than "checking_status=no checking" that has a coefficient of 0.0593?
Thanks