Extrapolaci

Extrapolaci is a term used in statistics and data analysis to describe the estimation of values beyond the range of observed data by extending a model fitted to that data. In practice, it overlaps with extrapolation, a standard concept in English; extrapolaci may appear in some languages as a transliteration or misspelling of extrapolasi. Extrapolation aims to project trends or patterns into the future or into unobserved regions.

Common methods include linear extrapolation, polynomial extrapolation, exponential growth or decay, spline-based approaches, and time-series forecasting models such as ARIMA. These methods assume that the underlying relationship observed in the data continues beyond the available observations, often requiring additional assumptions about stability, continuity, or seasonality.

Applications span economics, population studies, climate science, engineering planning, and any field seeking to anticipate future conditions or unseen ranges based on historical data. Extrapolaci can inform policy, budgeting, or resource management, but it also carries notable uncertainty.

Uncertainty and risk are central to extrapolaci. Predicted values should be reported with confidence intervals where possible, and analyses should consider multiple models or scenarios. Key limitations include model misspecification, nonstationarity, structural breaks, and changes in underlying processes that invalidate assuming past patterns will persist.

Best practices emphasize using domain knowledge, validating models with new data when available, performing sensitivity analyses, and avoiding long-range extrapolation without strong justification. See also interpolation, forecasting, regression analysis, and extrapolation methods.

Example: if y = 2x + 1 for x within a observed range, extrapolating to x = 12 yields y = 25.