It is very strange.
If 95% Wald Confidence Limits all contain 1 ,then it only illustrate that your model is not very good, Maybe you miss some very important independent variable to influence the response variable.
Thanks for the response. It is odd. There are no warnings or messages in the log. I'm trying to create a predictive model for customer behavior (0 = good, 1 = bad) based on a bunch of demographic data. Var1 in the model is Current Home Value and has a very right-skewed distribution. The dataset has 5429 observations, 2208 observations were deleted due to missing values for the response or explanatory variables.
Below is the log output and the Calculated Odds Ratios:
NOTE: PROC LOGISTIC is modeling the probability that Cust_Type=1.
NOTE: Convergence criterion (GCONV=1E-8) satisfied in Step 0.
NOTE: Convergence criterion (GCONV=1E-8) satisfied in Step 1.
NOTE: There were 5429 observations read from the data set TESTDATA5.
NOTE: The data set WORK.BETAS has 25 observations and 29 variables.
NOTE: The data set WORK.PRED has 5429 observations and 41 variables.
NOTE: PROCEDURE LOGISTIC used (Total process time):
real time 5.34 seconds
cpu time 0.54 seconds
Odds Ratio Estimates
Point 95% Wald
Effect Estimate Confidence Limits
var1 1.000 1.000 1.000
var6 M vs U 0.772 0.639 0.932
var6 S vs U 0.724 0.533 0.983
var8 H vs U 0.669 0.410 1.093
var8 R vs U 1.454 0.844 2.506
var10 0.989 0.979 0.999
var13 01 vs 06 1.228 0.883 1.707
var13 02 vs 06 1.579 1.131 2.205
var13 04 vs 06 1.593 1.119 2.270
var13 05 vs 06 1.949 1.258 3.019
var14 0 vs 4 1.778 1.269 2.489
var14 1 vs 4 1.273 0.960 1.689
var14 2 vs 4 1.344 1.027 1.758
var14 3 vs 4 0.902 0.692 1.174
var17 0.249 0.092 0.670
var19 4.860 1.312 18.013
var23 0.984 0.976 0.991
Thanks again for the response. No doubt there could be multicollinearity and a host of other problems with my model. But it turns out that they are not all equal to 1, it just seems to be a matter of rounding. When I use the method here (http://support.sas.com/kb/37/106.html) to get more decimal places displayed in the output, I get the following results:
You talked about multicollinearity.But in logistic model ,there will not be consider in general,because we assume the residual have like binary distribution,so it is hard to measure multicollinearity.
But Another alternation way is that Maybe you can try to use multi-variable linear regression(encode the category variable with some proper method). Be honest, your result is really looks curious.
PS: using logic link function to map 0 and 1 into infinity