Is age a continuous predictor?
Age can be considered as continuous, meaning that it can take on any value within a scale; technically, a person could be 23 years 15 days and 32 seconds old, although it is likely their age will be recorded in exact years only!
How do you use age as a continuous variable?
Age is measured in units that, if precise enough, could be any number. Therefore the set they come from is infinite. For example, someone could be 22.32698457 years old or 22.32698459 years old. We could be infinitly accurate and use an infinite number of decimal places, therefore making age continuous.
Is age group continuous variable?
Age can be considered as a continuous, ratio variable.
Should I treat age as a continuous variable or factor?
Should I treat age as a continuous variable or factor? Age is a predictor variable among a few others (gender, weight and height) and my response is the log-odds of a certain disease (binomial glm). My age data runs from 21 until 40.
When to categorize continuous predictor in a regression model?
Numerical predictors are usually coded with the actual numerical values while categorical variables are often coded with dummy variables—0 or 1. If you are not getting into the details of coding schemes, and all the values of the predictor are 0 and 1, you will not see any real information about the distance between them.
How to control for age in multiple regression?
If it’s not clearly linear, then group age by say, decade, and use it as categorical variable. For that matter, I think I’d group the outcome variable and do an ordinal regression.
When does it make sense to categorize a categorical variable?
Numerical predictors are usually coded with the actual numerical values. Categorical variables are often coded with dummy variables—0 or 1. Without getting into the details of coding schemes, when all the values of the predictor are 0 and 1, there is no real information about the distance between them.