Extrapolation

Definition

Extrapolation involves predicting values outside the range of your data set by extending the trend line. It assumes that the existing pattern continues beyond known data points.

5 Must Know Facts For Your Next Test

1. Extrapolation is often less reliable than interpolation since it extends beyond observed data.
2. In linear functions, extrapolation uses the slope and y-intercept to estimate values outside the given range.
3. Errors in extrapolated values can increase significantly if the assumed linear relationship does not hold.
4. To perform extrapolation, you need a clear mathematical model derived from your existing data.
5. Extrapolation is useful for making predictions but should be done cautiously due to potential inaccuracies.

Review Questions

• What are the key differences between extrapolation and interpolation?
• Why can extrapolation be considered less reliable than interpolation?
• How do you use a linear function's equation to perform extrapolation?

Related terms

Interpolation:

Estimating values within the range of a set of known data points using a trend line or curve.

Linear Regression: A statistical method used to model the relationship between a dependent variable and one or more independent variables by fitting a linear equation to observed data.

$y = mx + b$:

$y = mx + b$ represents the equation of a straight line, where $m$ is the slope and $b$ is the y-intercept.