However, when I tried drawing a box-plots (reverse analysis) of the two variables vs. Two continuous variables I added were very significant, making the entire model to be (and destroying the significant P value of Type=1). My second question regarding this analysis is for when I added variable. But why isn't the model significant then ? A chi square test and Fisher exact both were NOT significant. I understand why it can be significant, you can see that Type=1 has most red area, while Type=5, which is reference, doesn't have red at all. The P value of Type=1 is significant, however the model isn't. I attach a Mosaic plot JMP gave me, showing the two-way table (blue=no outcome, gray = normal outcome, red=double outcome - X axis is the categories of the IV), and I am attaching most of SAS's output of the logistic regression. I attach the frequency histogram of both Y and Type(X). And I don't understand why.I am attaching you some of my output.
#Logistic regression sas jmp full#
My main problem is, that the full model was not significant, however one of the dummy variables created by SAS is. I ran a logistic regression with both SAS and JMP. I am trying to fit a model, I have several independent variables, some discrete and some continuous, however, my main variable of interest is discrete with not less than 5 levels! This variable called "Type" represents 5 treatments, from which the last 2 are combinations of two of the first three treatments.īefore controlling for other variables, I wish to fit a simple model with this independent variable only. I have an ordinal dependent variable with 3 levels: 0 - no outcome 1 - normal outcome 2 - double outcome I need your help to understand something weird in the SAS output of the logistic regression.