Margbreytilegar

Margbreytilegar is an Icelandic term used in statistics to describe data, analyses, or models that involve more than one variable. The word is formed from marg- meaning many and breytilegur meaning variable, and in Icelandic texts it functions as the equivalent of the English term “multivariate.” In statistical contexts, margbreytilegar analyses are often contrasted with univariate analyses that treat variables separately.

In a typical margbreytilegar analysis, several variables are observed for each case, and the relationships among them are analyzed simultaneously. This approach can capture dependencies between variables and reveal overarching patterns in the data. Common margbreytilegar methods include principal component analysis (PCA), factor analysis, multivariate analysis of variance (MANOVA), canonical correlation, and multivariate regression. These techniques are used to summarize complex data, reduce dimensionality, and model multiple outcomes at once.

Data suitable for margbreytilegar analyses are usually organized in a matrix where rows represent observations and columns represent variables. Because many variables can be correlated, margbreytilegar methods require sufficient sample sizes and careful interpretation to avoid overfitting. They are widely used across disciplines such as psychology, economics, biology, and environmental science to understand systems with several interrelated measurements.

Related terms in Icelandic literature include margbreytigreiningar (multivariate analyses) and margbreytigögn (multivariate data). The concept is a central part of the broader field of multivariate statistics.