Yes, each row (individual) will get a result from the formula for the odds.
Yes, you can use the mean odds. Use Table > Summary or Analyze > Tabulate to get the results for each group or subgroup.
The hand calculation is as you say Pr(group A) divided by Pr(not group A) or whatever. The odds ratio would be the ratio of the odds under different conditions (treated, untreated).
I am using JMP 13 Pro, not a student version.
Are there tutorials/videos for logistic regression with multiple outcomes?
I checked our learning assets but found no tutorials about nominal logistic regression. (See items from selecting Learning JMP on the JMP home page menu.) I then checked our YouTube account and found this tutorial about Multiple Logistic Regression. There is also Logistic Regression Introduction with Tutorial in JMP on YouTube. It covers logistic regression more thoroughly but only for the outcome. It does not cover multinomial logistic regression.
We offer this training course, which covers this topic: Analyzing Discrete Responses. We cover the origin, use, and interpretation of such models as well as how to preform this regression in JMP.
You can't do logistic regression with multiple dependent variables in one run of logistic. But perhaps you have ONE dependent variable  behavior of gibbon  with multiple levels? How is "behavior" operationalized? Is the data something like this:
Case ID Num People Behavior
1 3 A
2 2 B
3 5 A
4 2 C
etc.? Or does each gibbon engage in multiple behaviors? Or is each gibbon engaged in multiple cases? (in that case, you'd need some form of multilevel model, probably with GLIMMIX?
It is like you have laid out. The gibbon only does one behavior at a time, but it has several behaviors it can do, such as feed, travel, vocalize, groom, etc. What I am trying to do is see how the number of people present affects the likelihood of the gibbon doing a certain behavior e.g. does the gibbon reduce time spent feeding when more people are present. The logistic regression tells gives me a p value for the entire model, so I can see that number of people does affect gibbon behavior, but what I would like to do is see which individual behaviors are driving the model  I'd like some sort of stats with p values that show me which behaviors are actually changing. All I am doing now is looking at the output figure and describing how the behaviors change. Here is what the output looks like. I tried to insert the figure, but it wasn't working. I have figured out though, that I cannot do the odds ratio test because my response (behavior) has more than 2 variables.
Logistic Fit of Behavior By total humans
Whole Model Test
Model  LogLikelihood  DF  ChiSquare  Prob>ChiSq 
Difference  37.2401  11  74.48025  <.0001* 
Full  1162.7796  
Reduced  1200.0197 


RSquare (U)  0.0310 
AICc  2371.15 
BIC  2468.39 
Observations (or Sum Wgts)  660 
Measure  Training  Definition 
Entropy RSquare  0.0310  1Loglike(model)/Loglike(0) 
Generalized RSquare  0.1096  (1(L(0)/L(model))^(2/n))/(1L(0)^(2/n)) 
Mean Log p  1.7618  ∑ Log(ρ 
RMSE  0.7987  √ ∑(y 
Mean Abs Dev  0.7918  ∑ y 
Misclassification Rate  0.6727  ∑ (ρ 
N  660  n 
Parameter Estimates
Term 
 Estimate  Std Error  ChiSquare  Prob>ChiSq 
Intercept[Drink]  Unstable  9.3119005  1537.0201  0.00  0.9952 
total humans[Drink]  Unstable  13.895268  1537.0192  0.00  0.9928 
Intercept[Feed]  1.5746952  0.2067421  58.01  <.0001*  
total humans[Feed]  0.45943506  0.0703651  42.63  <.0001*  
Intercept[Groom]  3.8964691  0.7363762  28.00  <.0001*  
total humans[Groom]  0.17142825  0.2657919  0.42  0.5189  
Intercept[Groom Recipient]  5.9062151  1.021258  33.45  <.0001*  
total humans[Groom Recipient]  0.60419887  0.1604264  14.18  0.0002*  
Intercept[Hang]  3.4626526  0.5849713  35.04  <.0001*  
total humans[Hang]  0.187694  0.2073532  0.82  0.3654  
Intercept[Not Visible]  0.9158515  0.1862225  24.19  <.0001*  
total humans[Not Visible]  0.35203714  0.0696868  25.52  <.0001*  
Intercept[Other]  7.0115111  1.4481919  23.44  <.0001*  
total humans[Other]  0.71085044  0.1806613  15.48  <.0001*  
Intercept[Rest  Sleep]  2.0491431  0.2866187  51.11  <.0001*  
total humans[Rest  Sleep]  0.25952368  0.0987496  6.91  0.0086*  
Intercept[Rest  Still]  1.703556  0.2215666  59.12  <.0001*  
total humans[Rest  Still]  0.40532436  0.0739462  30.05  <.0001*  
Intercept[Self groom]  5.34457  1.6699724  10.24  0.0014*  
total humans[Self groom]  0.09443149  0.6681153  0.02  0.8876  
Intercept[Travel]  1.709588  0.2321228  54.24  <.0001*  
total humans[Travel]  0.34947804  0.0784157  19.86  <.0001* 
For log odds of Drink/Vocalize, Feed/Vocalize, Groom/Vocalize, Groom Recipient/Vocalize, Hang/Vocalize, Not Visible/Vocalize, Other/Vocalize, Rest  Sleep/Vocalize, Rest  Still/Vocalize, Self groom/Vocalize, Travel/Vocalize
The Nominal Logistic Regression personality in Fit Model should help. You can get the odds ratios by clicking on the red triangle at the top of the platform. Also, you can use the prediction profiler to estimate the probability of all the levels for any condition, and then compute the odds. Now change the condition (predictor level) and repeat the calculation. Now compute the odds ratio.
Can you give me more information on the predictor profiler? I am able to get that figure when I run the test with fit model instead of fit y by x, but I am not sure how to estimate the odds for each prediction. What I got was a figure like the one I made, but with red horizontal lines on it as well. There are several options in the red drop down arrow, but I am not sure which one tests each prediction. And indeed, drink was a rare behavior, I assumed that is why it was unstable.
I am having a problem running the odds ratio in that jmp won't give me that option because there are more than 2 behaviors. I think that is what I need to do, but I can't seem to make jmp do it. The option does not appear on the drop down triangle unless I make up a new data sheet that only has 2 possible outcomes in the response variable. I can use the predictor profiler, but I am not really familiar with how it works.
Thanks again
Use the LRTs to judge signficance and choose your model. Once that part is done, then use the model to compute the odds ratios.
Use the prediction profiler to compute the probabilities and then the odds of an event versus the nonevent for a given predictor level. Change the predictor level, get the updated probabilities, and compute the new odds. You can then compute the ratio. This way you can compute the odds ratio for anything.
The fact that two parameter estimates are unstable indicates that DRINK was probably an uncommon behavior.
The easiest way to interpret logistic regression is via odds ratios  which are exp(parameter estimate) and predicted probabilities. I don't know JMP, but if you have SAS/STAT I wrote a paper on multinomial logistic. It's here: