Number of variables

Option	Description
Univariate	Models a single variable (exposure or response).
Bivariate	Handles exactly two variables in conjunction (e.g., one exposure and one outcome).
Multivariate	Simultaneously models multiple variables (e.g., several exposures or multivariate responses) when predictors are fewer than observations.
High-dimensional (p≫n)	Designed for contexts where the number of variables (p) greatly exceeds the number of observations (n), requiring techniques for variable selection, regularization, or dimension reduction.

Definition

The total number of distinct variables -either exposures or response variables- that the method should handle simultaneously.

Explanation

Some methods are designed to analyze a single variable at a time (univariate), while others can jointly handle two variables (bivariate), multiple variables (multivariate), or very large sets of variables including many exposures or response metrics (high‑dimensional). This criterion ensures you select a method compatible with the complexity of your variable set.

Tools/rationale for helping assessment

1. Count how many exposures, responses and covariates you wish to include simultaneously.
2. If p≫n, consider High‐dimensional; if exactly one, Univariate etc.

Example

I plan to model 4 spectral indices and 2 vegetation metrics simultaneously (total 6 variables) → I select Multivariate.