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Can we use regression for discrete data?
If you have a discrete variable, you can decide whether to treat it as a continuous or categorical predictor. A discrete variable can be measured and ordered but it has a countable number of values. If you have one continuous predictor, you can use Simple Regression.
Can you use discrete data in linear regression?
Both continuous (number so fine that you can’t name the exact point) and discrete (consists of whole numbers) variables are considered as interval/ratio. They are treated the same way when used as an independent variable in linear regression analysis.
Is a variable continuous or discrete?
A variable is a quantity whose value changes. A discrete variable is a variable whose value is obtained by counting. A continuous variable is a variable whose value is obtained by measuring.
How do you tell if a situation is discrete or continuous?
When figuring out if a graph is continuous or discrete we see if all the points are connected. If the line is connected between the start and the end, we say the graph is continuous. If the points are not connected it is discrete.
How are discrete variables treated in a regression?
Both continuous (number so fine that you can’t name the exact point) and discrete (consists of whole numbers) variables are considered as interval/ratio. They are treated the same way when used as an independent variable in linear regression analysis.
Is there a module for discrete dependent variable regression?
Regression with Discrete Dependent Variable Regression models for limited and qualitative dependent variables. The module currently allows the estimation of models with binary (Logit, Probit), nominal (MNLogit), or count (Poisson, NegativeBinomial) data.
Can You regress with a discrete independent variable?
My issue isn’t on regressing with discrete variables per se, but with the combination of discrete independent variables and continuous dependent variables. Typically in regression one has a set of data points (dependent variables) with one-to-one correspondence to independent variable, {x i, y i }.
How to train a model for discrete values?
To train a model, it should except as input all the features (preferably normalized), and output a single number which you can later round to the closest discrete value (if you like). I highly recommend starting simple. It seems like you’re trying to jump into some sort of recommendation problem without even knowing your data or what’s possible.