ridge regression multicolinearity?

是 否 +2 论坛币

k人 参与回答

经管之家送您一份

应届毕业生专属福利!

求职就业群

赵安豆老师微信：zhaoandou666

经管之家联合CDA

送您一个全额奖学金名额~ !

立即领取

感谢您参与论坛问题回答

经管之家送您两个论坛币！

+2 论坛币

Hello,

I have a problem with multicolinearity in a multiple regression analysis. Two of my predictors and the outcome are correlated at .8, VIF's are around 4-6, Tolerances at .2 - .3 and Condition index at 23. My dataset has 72 cases, 5 continuous predictors (excluding the controls, with them 13 variables, dummy coded categorical controls - age, tenure etc.)

I am running SPSS 17. I understand that in order to avoid the multicolinearity ridge regression can be used, with the CATREG  command. However, I do not know what options should I choose (I have read discretization option multiply, but besides this?), how to get the p values for the predictor's weights, and most importantly how to interpret the results.

I appreciate if anyone can suggest a way to handle this or to find more information about the procedure (I've tried googling and this much I came with).

扫码加我 拉你入群

请注明：姓名-公司-职位

以便审核进群资格，未注明则拒绝

分享0 收藏0 回帖

关键词：regression regressio regress Linear multi understand continuous controls running problem

In addition to the problems you mention, you are over-fitting your model (i.e., you have too many variables for the amount of data).  For a good overview of over-fitting, check out Mike Babyak's nice article.

   http://www.psychosomaticmedicine.org/content/66/3/411.short

Have to get to a meeting, so no time to address the other problems right now!