Statistics: How to identify dependent and independent variables in my dataset?
In any given data set, labeling variables as dependent or independent is arbitrary -- there is no fundamental reason that one column should be independent and another should be dependent.
That said, typically it's conventional to say that "causes" are independent variables and "effects" are dependent variables. But this business about causes and effects is arbitrary too -- often enough there are several interacting variables, with each of the them "causing" the others, and each of them "affected" by the others.
The bottom line is that you should assign dependent and independent according to what you're trying to achieve. What is the most interesting or most useful variable in your data? Typically if that one is missing or has an unknown value, you'll have to estimate it from the other variables. In that case the interesting variable is the dependent variable, and all others are independent.
You'll probably get more interest in this question on stats.stackexchange.com.
I am a little bit confused in the classification of dependent and independent variables in my dataset, on which I need to make a model for prediction. Any insights or how-to's would be very helpful here.
Suppose my dataset have 40 variables. In this case, it would be very difficult to classify the variables as independent or dependent. Are there any tests in python which can help us in identifying these?