My question is about the validity of using a logistic regression model to predict the probability of X occurring when knowing only one or two input variables.
Consider the binary response variable that a person will either become obese (1) or they will not become obese (0). Say we have a sample of people and data for the independent variables currently at a healthy weight, these variables are: initial weight, age, daily exercise (time), calories consumed per day. And we have some numerical model which uses the above variables to calculate precisely what the outcome will eventually be for each person: whether or not they will grow to obesity.
We want to use this data to make a prediction on anyone in the total population given their age and calories consumed. Logistic regression seems to be the way to go, but please tell me if there is a more appropriate approach.
What if, when going to the total population we are only able to know a given person's age and calories consumed. For some reason the initial weight and daily exercise is not available. So then for our logistic regression, we would only be able to use age and calories consumed as predicting factors to whether someone will become obese. Is logistic regression still appropriate for this kind of problem?