I need to extract the significance of the interaction effect (the 0.1397 under Effect Tests) for each of a set of variables for example, interchanging the "Distance" variable with "Elapsed Time". Below is the JMP output for a logistic regression using our toylogistic data set with PassClass regressed on MidtermScore. any advice would be appreciated.įor example, using the Airline Delays JMP sample data set, this is the result from one of the steps. In the absence of a test, one can fit both an ordinal logistic regression and a multinomial logistic regression to compare the AIC values. JMP does not offer a test of proportional odds. These data were collected on 200 high schools students and are scores on various tests, including science, math, reading and social studies (socst).The variable female is a dichotomous variable coded 1 if the student was female and 0 if male. output, as does SPSS’s ordinal regression menu. outcome (response) variable is binary (0/1) win or lose. that influence whether a political candidate wins an election. Previous attempts to scripting have resulted in ~350 results windows, or ~350 model dialogs. This page shows an example of logistic regression with footnotes explaining the output. Example 1: Suppose that we are interested in the factors.
Now, I need to be able to switch out one of these variables that is, for a list of ~350 variables, say varA, varB, etc., I need to run the following regressions, result = group + varA + group*varAĪnd get the significance of that interaction effect. Binary logistic regression is for a case where the response variable has only two possible values. SAS provides many more, including the complete BKW. The focus of the analysis is to predict the probability of the levels of the categorical response. JMP provides some very useful regression diagnostics such a leverage plots.
I've been advised that the way to do this in JMP is to make a series of linear regressions like the following, result = group + varA + group*varAĪnd then examine the significance of the interaction effect, e.g., the "Prob > F" column in this "Country*Displacement" example: (I don't have the reputation to post an image.) Logistic Regression is appropriate when the response variable is categorical. I'm trying to take a set of independent variables and test if they are (statistically significantly) differently-correlated to two groups of data.