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Linear Interpolation Calculator

Enter two known coordinate pairs and a target x value to get the estimated y, slope, and interval position.

Interpolate Between Two Points

Enter two known coordinate pairs and a target x value to estimate y on the straight line between them.

The interpolated y value, slope, and interval position will appear here.

How to Use Linear Interpolation Calculator

1

Enter Two Known Points

Provide x₁, y₁, x₂, and y₂ using compatible scales.

2

Enter the Target X

Use a target between the two x values for interpolation.

3

Review Range Context

Check the estimated y, slope, and whether the target is inside or outside the known interval.

Linear Interpolation Calculator Features

  • Two-point linear interpolation
  • Live slope and interval position
  • Negative and decimal support
  • Clear extrapolation warning

Common Use Cases

Estimate a value in a table

Find an intermediate measurement between two known data rows when a straight-line assumption is appropriate.

Check a manual interpolation

Compare your fraction, slope, and estimated y value with a transparent calculation.

Limits and Important Notes

Result type: Exact calculation

Calculation Details

  • Subtract x₁ from the target x and divide by the full x interval.
  • Multiply that interval position by the change in y.
  • Add the result to y₁.

Formula

y = y₁ + (x − x₁)(y₂ − y₁) ÷ (x₂ − x₁)

Rounding

The calculation keeps full precision and displays up to eight decimal places.

Default Assumptions

  • The relationship between the known points is treated as a straight line.
  • The two x coordinates are different.
  • All values are expressed on compatible scales and units.

Verified Example

Inputs: Points (10, 100) and (20, 200), with target x = 15.

Calculation: Position = (15 − 10) ÷ (20 − 10) = 0.5; y = 100 + 0.5 × 100.

Result: Estimated y = 150.

Sources and Version

Version: Two-point linear interpolation

Last verified: 2026-08-13

Update responsibility: OnlineTool.me

  • A target outside the known x interval is extrapolation and may be less reliable.
  • The result is inappropriate when the underlying relationship is curved, discontinuous, categorical, or otherwise nonlinear.
  • Input-data uncertainty is not propagated into the result.

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Frequently Asked Questions