Extrapolation

Definition

Extrapolation is the process of estimating values beyond the range of observed data by extending a trend or pattern. It relies on the assumption that existing trends continue.

5 Must Know Facts For Your Next Test

1. Extrapolation can lead to less accurate predictions compared to interpolation because it assumes that the established trend continues outside the observed range.
2. Linear regression models are often used for extrapolation, but caution must be taken as relationships may change outside the observed data range.
3. The accuracy of extrapolated values depends heavily on the validity of the model and assumptions made during analysis.
4. Outliers and anomalies in the dataset can significantly impact extrapolated predictions if not properly addressed.
5. Extrapolation is commonly used in various fields such as economics, engineering, and environmental science to predict future trends.

Review Questions

• Why is extrapolation generally considered less reliable than interpolation?
• How does an outlier in your data affect extrapolated predictions?
• What are some fields where extrapolation is commonly used?

Related terms

Interpolation: The process of estimating values within the range of observed data based on known values.

Linear Regression: A statistical method for modeling the relationship between a dependent variable and one or more independent variables using a linear equation.

$R^2$ (Coefficient of Determination): $R^2$ measures how well a regression model fits the data; it indicates the proportion of variance in the dependent variable explained by independent variables.