Extrapolation
Definition
Extrapolation refers to using a mathematical model, such as linear regression, to estimate or predict values outside of an observed range based on patterns within that range. It assumes that trends observed within known data will continue beyond those limits.
Analogy
Imagine you have been tracking your daily steps for two weeks and notice an increasing trend over time. Using extrapolation, you can estimate your step count for future days even if you haven't recorded them yet, assuming your pattern continues at a similar rate.
Related terms
Interpolation: Interpolation is estimating values within an observed range based on existing data points. Unlike extrapolation which predicts outside the range, interpolation focuses on estimating values between known data points.
Outliers: Outliers are extreme values that deviate significantly from the overall pattern of a dataset. When performing extrapolation, it's important to identify and consider outliers as they can have a strong influence on predictions.
Confidence Interval: A confidence interval is a range of values within which we expect the true value to fall with a certain level of confidence. When extrapolating, it's useful to calculate confidence intervals to understand the uncertainty associated with predicted values.
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