logistic regression missing values

Could I have a logistic regression with mi开发者_Go百科ssing values?

I have many continuos attributes and some categorical, could I set them as user-missing? Could it be useful?

For doing a regression analysis you need all variables measured for each event. Perhaps another technique works with missing attributes, but not regression.

BTW, you should try posting the question at https://stats.stackexchange.com/

HTH!

Most regression procedures require complete data, but there are a variety of methods for dealing with missing values. This is a subtle topic, so I won't pretend to give a complete answer here, and recommend doing some reading on the subject. Briefly, though:

1. Never delete observations to fix this problem.
2. Deletion of variables is always allowed, but obviously is quite severe in terms of one's data budget.
3. Filling in missing values with global constants, such as the mean or median of the non-missings, should be done sparingly (when the proportion of missings is very low) if at all.
4. Filling in missing values with values chosen based on other independent variables is preferred over number 3, above.

To learn more about this subject, seek information on the terms "imputation", especially "single imputation" and "multiple imputation", "missing at random" and "missing completely at random".