Xvariabler

X-variabler, or x-variables, is a term used in mathematics, statistics and data science to refer to the independent or predictor variables in a model. These are the input quantities whose values are observed or controlled in order to explain or predict a dependent outcome, typically denoted by Y.

In standard modeling, such as simple or multiple regression, the relationship is expressed with equations like Y = β0 + β1X1 + β2X2 + ... + ε. Each Xi represents an x-variable. The set of x-variables can be a single continuous measure, several continuous measures, or a mix of continuous and categorical factors (the latter often encoded as dummy variables). In machine learning, these variables are commonly called features or attributes.

X-variables can be quantitative (interval or ratio scales) or qualitative when coded for analysis. They may be measured directly or derived from data transformations. The choice and quality of x-variables influence model accuracy and interpretability. Multicollinearity, where two or more x-variables convey overlapping information, is a common issue that can affect estimates.

In practice, x-variables appear across disciplines, from econometrics and engineering to social sciences. They are contrasted with the dependent variable (often called the response or outcome variable, Y). Understanding which factors to include as x-variables is central to model specification, data preprocessing, and the interpretation of results.