Can discrete variables be used 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.
Can Count data be used in linear regression?
1 Answer. Your count data does not follow a normal distribution, because it simply can not. Because it can not, simple linear regression is not the way to go.
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.
Are there any problems with linear regression for count data?
The distribution of counts is discrete, not continuous, and is limited to non-negative values. There are two problems with applying an ordinary linear regression model to these data. First, many distributions of count data are positively skewed with many observations in the data set having a value of 0.
How are all discrete regression models the same?
Currently all models are estimated by Maximum Likelihood and assume independently and identically distributed errors. All discrete regression models define the same methods and follow the same structure, which is similar to the regression results but with some methods specific to discrete models.
How are regression problems and classification problems related?
There are regression problems and classification problems. Basically, given some features (discrete (car model) or continuous (Miles per Gallon)) you want to estimate the price (a continuous variable). Your model will use the independent variables (your features) to estimate the dependent variable.