The rule of thumb I mentioned earlier says that in order to avoid over-fitting, you should have 15-20 events per model parameter. You have approximately 120 events (i.e., 15% of 797). Therefore, your model should have about 8 parameters at most. The model you posted has 25 parameters. You either need more data, or fewer parameters in your model.

HTH.

lcl23 wrote: > > around 85% fall under No (0), the rest are Yes (1) > > --- On Tue, 18/1/11, Bruce Weaver [via SPSSX Discussion] > <ml-node+3344966-315359726-147588@n5.nabble.com> wrote: > > From: Bruce Weaver [via SPSSX Discussion] > <ml-node+3344966-315359726-147588@n5.nabble.com> > Subject: Re: problem with logistic regression - linearity to the logit > To: "lcl23" <chuiling23@yahoo.com> > Date: Tuesday, 18 January, 2011, 2:52 AM > > > > How many fall into each category of the outcome (dependent) variable? > > > > lcl23 wrote: > I have 797 valid cases. > > Have you seen my attached output? How to interpret the 'something wrong' > there? > > > --- On Sun, 16/1/11, Bruce Weaver [via SPSSX Discussion] > <ml-node+3343380-135739802-147588@n5.nabble.com> wrote: > > > From: Bruce Weaver [via SPSSX Discussion] > <ml-node+3343380-135739802-147588@n5.nabble.com> > > Subject: Re: problem with logistic regression - linearity to the logit > > To: "lcl23" <chuiling23@yahoo.com> > > Date: Sunday, 16 January, 2011, 11:08 PM > > > > >     > > lcl23 wrote: > > attached herewith the output file of the test of nonlinearity > > > > How many cases you have, and how many fall into each category of the > outcome variable?  One rule of thumb is that in order to avoid > over-fitting the model, you should have 15-20 'events' per model > parameter, where 'event' is defined as the outcome category with the lower > frequency.  You have 12 explanatory variables, plus interaction terms, and > 25 parameters in total (including the constant).  So you would need at > least 750 cases, assuming 50% Yes and 50% No on the outcome variable (25 > parameters * 15 * 2).  > > > > See Mike Babyak's nice readable article for more information on > over-fitting. > > > >   http://www.class.uidaho.edu/psy586/Course%20Readings/Babyak_04.pdf > > HTH. > > > >         > >         > >     -- > > > Bruce Weaver > > > bweaver@lakeheadu.ca > > > http://sites.google.com/a/lakeheadu.ca/bweaver/ > > "When all else fails, RTFM." > > > > NOTE: My Hotmail account is not monitored regularly. > > > To send me an e-mail, please use the address shown above. > > > >         > >         > >     > > >     > > >     > >     > >         View message @ > http://spssx-discussion.1045642.n5.nabble.com/problem-with-logistic-regression-linearity-to-the-logit-tp3336036p3343380.html > >         > >         To unsubscribe from problem with logistic regression - > linearity to the logit, click here. > >     > > > > > > > -- > > Bruce Weaver > > bweaver@lakeheadu.ca > > http://sites.google.com/a/lakeheadu.ca/bweaver/ > > "When all else fails, RTFM." > > > NOTE: My Hotmail account is not monitored regularly. > > To send me an e-mail, please use the address shown above. > > > > > > > > > > > View message @ > http://spssx-discussion.1045642.n5.nabble.com/problem-with-logistic-regression-linearity-to-the-logit-tp3336036p3344966.html > > > To unsubscribe from problem with logistic regression - linearity to the > logit, click here. > > > >

----- -- Bruce Weaver bweaver@lakeheadu.ca http://sites.google.com/a/lakeheadu.ca/bweaver/

"When all else fails, RTFM."

NOTE: My Hotmail account is not monitored regularly. To send me an e-mail, please use the address shown above.

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