Multivariabel

Multivariabel is a term that is commonly encountered as a nonstandard or misspelled form of multivariate or multivariable. In standard statistical and mathematical usage, multivariate describes analyses that involve multiple dependent variables or outcomes, while multivariable describes analyses that include multiple predictor or explanatory variables. The distinction reflects whether the focus is on several outcomes being modeled together or on several inputs driving a single outcome.

In its core sense, multivariate analysis deals with data in more than one dimension. Data points are vectors in a high-dimensional space, and relationships among variables are summarized using concepts such as the covariance matrix and the correlation matrix. A key object is the covariance matrix, which encodes how pairs of variables vary together. Multivariate methods often assume or explore properties such as linear relationships, joint distributions, and dependence structures among variables.

Common techniques include multivariate analysis of variance (MANOVA), which extends ANOVA to multiple dependent variables; principal component analysis (PCA) and factor analysis, which reduce dimensionality; canonical correlation analysis, which studies relationships between two sets of variables; and multivariate regression, discriminant analysis, and structural equation modeling for modeling multiple outcomes simultaneously. Time series contexts use multivariate autoregressive models (VAR) to capture interdependencies over time.

Applications span many fields, including psychology, economics, biology, engineering, and social sciences. Visualization methods such as scatterplot matrices and heatmaps aid interpretation, while software tools in R, Python, and MATLAB provide implementations. The term multivariabel may appear in informal writing, but the standard terms are multivariate and multivariable, chosen to reflect the analysis’s focus on multiple outcomes or multiple predictors.