In statistics, what term is applied to techniques for modeling and analysing several variables to understand how the typical value of the dependent variable changes when one of the independent variables is varied, and the other variables are fixed?
Mathematical statistics for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. More specifically, regression analysis helps one understand how the typical value of the dependent variable (or 'criterion variable') changes when any one of the independent variables is varied, while the other independent variables are held fixed. Most commonly, regression analysis estimates the conditional expectation of the dependent variable given the independent variables – that is, the average value of the dependent variable when the independent variables are fixed. Less commonly, the focus is on a quantile, or
Dependent and independent variables term formula_7 is known as the "error" and contains the variability of the dependent variable not explained by the independent variable. With multiple independent variables, the model is formula_8, where "n" is the number of independent variables. In simulation, the dependent variable is changed in response to changes in the independent variables. Depending on the context, an independent variable is sometimes called a "predictor variable", regressor, covariate, "controlled variable", "manipulated variable", "explanatory variable", exposure variable (see reliability theory), "risk factor" (see medical statistics), "feature" (in machine learning and pattern recognition) or "input variable." In econometrics, the term "control variable" is