Can linear regression be used for discrete variables?

You can certainly do a multiple regression with discrete independent variables and a continuous dependent variable. But the output of a regression is not the dependent variable, it’s the expected value of the dependent variable for the given set of independent variables.

Can linear regression be used for continuous variables?

For example, linear regression is used when the dependent variable is continuous, logistic regression when the dependent is categorical with 2 categories, and multinominal regression when the dependent is categorical with more than 2 categories.

What are the conditions for multiple linear regression?

Multiple linear regression requires at least two independent variables, which can be nominal, ordinal, or interval/ratio level variables. A rule of thumb for the sample size is that regression analysis requires at least 20 cases per independent variable in the analysis. Learn more about sample size here.

Can logistic regression be used for continuous variables?

Logistic regression is usually used with binary response variables ( 0 or 1 ), the predictors can be continuous or discrete.

Can you run a regression on discrete data?

You can still perform regression even if your input (or part of it) is discrete. If you think of it, even your “continues” values are actually discrete (starting from their initial measurement accuracy/resolution).

What is multiple linear regression used for?

Multiple linear regression is used to model the relationship between a continuous response variable and continuous or categorical explanatory variables. Recall that simple linear regression can be used to predict the value of a response based on the value of one continuous predictor variable.

When conducting a multiple regression analysis should your independent variable be categorical continuous or either?

The independent variables used in regression can be either continuous or dichotomous. Independent variables with more than two levels can also be used in regression analyses, but they first must be converted into variables that have only two levels.