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How do you compare a categorical variable with a continuous variable?
A simple approach could be to group the continuous variable using the categorical variable, measure the variance in each group and comparing it to the overall variance of the continuous variable.
What is a continuous and categorical variable?
Categorical variables contain a finite number of categories or distinct groups. Continuous variables are numeric variables that have an infinite number of values between any two values. A continuous variable can be numeric or date/time.
When to use continuous time vs categorical time?
Time is a special case that can be either type, depending on the way you want to look at the data. To focus on individual months, treat time as discrete and use bars. To look at trends and the rate of change (and thus, the space in between the data points), use continuous time.
Can a continuous data type be converted to categorical?
Time is a special case, and continuous can always be converted into categorical (e.g., you might classify age into age groups or weight into low/medium/high, etc.). But the underlying data still has a type that is either quantitive or categorical.
When to use categorical, discrete, and continuous variables?
If you have a discrete variable and you want to include it in a Regression or ANOVA model, you can decide whether to treat it as a continuous predictor (covariate) or categorical predictor (factor). If the discrete variable has many levels, then it may be best to treat it as a continuous variable.
What’s the difference between continuous and longitudinal data?
Static vs. longitudinal is a different way of looking at it, but it doesn’t change the underlying type of most of your data. Time is a special case, and continuous can always be converted into categorical (e.g., you might classify age into age groups or weight into low/medium/high, etc.).