Interpolation

Definition

Interpolation is a method used to estimate unknown values that fall within the range of known data points. It is commonly used in statistics to predict outcomes based on linear regression models.

5 Must Know Facts For Your Next Test

1. Interpolation assumes that the estimated value lies within the range of the given data points, not outside it.
2. Linear interpolation uses a straight line to estimate values between two known points.
3. The accuracy of interpolation depends on how closely the model fits the actual data.
4. Interpolation can be less reliable if the data points are sparse or have high variability.
5. It contrasts with extrapolation, which estimates values outside the range of known data points.

Review Questions

• What is the primary difference between interpolation and extrapolation?
• How does linear interpolation estimate unknown values?
• Why might interpolation be less reliable with sparse or highly variable data?

Related terms

Extrapolation:

A method used to estimate values outside the range of known data points.

Linear Regression:

A statistical method for modeling the relationship between a dependent variable and one or more independent variables using a straight line.

Correlation:

A measure that describes the strength and direction of a relationship between two variables.