my understanding is that Multinomial logistic regression is where your dependent variable could take values of 1,2 or 3 where 1-3 are classes. But what isit called if you have two dependent variables your model is supposed to predict continuous values with logistic regression?
for example,I am trying to predict the number of males and number females at an event given a list of conditions [weather,day of week, time of day]
An analysis with multiple dependent variables is called a multivariate analysis. If you had two binary dependent variables, you could perform multivariate logistic regression.
However, your research question doesn't seem to indicate you have binary variables. If my understanding is correct, you have a data set where each entry is an event, and the columns correspond to the predictors (weather, day, time) and the outcomes (number of males, number of females). In this case, you can use linear regression models or count models (e.g., negative binomial), and you can use the multivariate version of these. Count models might be more realistic because your outcome variables are positive integers (i.e., number of males and number of females).
If your goal is prediction and you're not so interested in the individual effects of variables but rather on making good predictions, you can avoid parametric models and use a machine learning method like a random forest or gradient boosting machine. If there are nonlinearities in the relationships between the predictors and the outcomes beyond those included in the regression models, machine learning methods may be better suited to make the predictions for future events.
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