I am building a model that has 10 dummy variables for a category called operator. The operator values are string, so I created binary variables to make sure each operator is within the model. I am predicting the time it takes for an operator to finish a job (dependent: DaysToCompletion, which is based on approval time approved = 1, notapproved = 0) based on one independent variables (# of codes for the job and the operator dummies.)
I am going off the rule of thumb that there are about 15 times as many nonevents/events as there are parameters in my model. this rule of thumb is met as I have 80k+ values in the dataset, and plenty for each event. However I don't know if this is the best approach since I only have one other independent variable.
Is there a better approach for this type of situation? I feel like this comes up a lot with datasets I am working with, another example would be having 12 countries and trying to predict an event that might happen in said country. But having 11 dummy variables seems like a ridiculous approach given the possible breach of degrees of freedom etc.
Any help is greatly appreciated!