Multivariate calibration is a statistical technique used to analyze multiple variables simultaneously to predict the outcome of a dependent variable. This method is commonly applied in fields like chemistry and engineering, where complex data sets are common. By using algorithms, it establishes relationships between various input variables and a target variable, allowing for more accurate predictions. In practice, multivariate calibration often employs methods such as Principal Component Analysis (PCA) or Partial Least Squares (PLS) regression. These techniques help simplify data interpretation by reducing dimensionality while retaining essential information, making it easier to understand the underlying patterns in the data.