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Can you do regression with discrete variables?
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.
Do you use continuous variables in regression?
Regression analysis is used when you want to predict a continuous dependent variable from a number of independent variables. If the dependent variable is dichotomous, then logistic regression should be used. The independent variables used in regression can be either continuous or dichotomous.
Can logistic regression take continuous variables?
Logistic regression is usually used with binary response variables ( 0 or 1 ), the predictors can be continuous or discrete.
What is a continuous variable in regression?
Regression analysis with a continuous dependent variable is probably the first type that comes to mind. While this is the primary case, you still need to decide which one to use. Continuous variables are a measurement on a continuous scale, such as weight, time, and length.
Can you use linear regression on discrete data?
As commonly used data analysis methods, such as regression analysis, naturally work with continuous data, complications can occur. However, the linear regression analysis can be carefully used to to analyze discrete data, but carefully.
Can you use continuous and discrete variables in multiple regression?
You can use both continuous and categorical/discrete variables as X’s in multiple regression. If the discrete variables are ordinal (i.e. Lickert Scale) then you can use them as you would any other X.
How to model continuous and discrete factors together?
For example, if your response variable is continuous and your predictors are some combination, you can run a linear regression model to get a decent predictive y. Or use logistical regression to model a respsonse variable that is ordinal, binary, or nominal.
Can a continuous variable be treated as a covariate?
Under those circumstances, it may be more reasonable to assume that the counts resemble random numbers drawn from a Gaussian distribution (following the Central Limit theorem), which may fit well with the underlying statistical assumptions of popular regression algorithms.
How to estimate mutual information between two random variables?
Estimate mutual information for a continuous target variable. Mutual information (MI) between two random variables is a non-negative value, which measures the dependency between the variables. It is equal to zero if and only if two random variables are independent, and higher values mean higher dependency.