Interpolation Calculator

Enter two known data points (X0, Y0 and X1, Y1) and a target X value to find the estimated Y using linear interpolation. The Interpolation Calculator applies the standard linear formula to compute the unknown value between your two reference points — perfect for engineering tables, statistics, and scientific data. Also try the calculate Quadratic Regression Quadratic Equation (Y = ax² + bx + c).

The X coordinate of the first known point

The Y coordinate of the first known point

The X coordinate of the second known point

The Y coordinate of the second known point

The X value for which you want to estimate the corresponding Y

Results

Interpolated Y2 Value

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Slope (Rate of Change)

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ΔX (X2 − X0)

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ΔY Total (Y1 − Y0)

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What is linear interpolation?

Linear interpolation is a method of estimating an unknown value that falls between two known data points by assuming the relationship between them is a straight line. The formula calculates where the target X would land on that line to produce a corresponding Y value.

What formula does this calculator use?

The calculator uses the standard linear interpolation formula: Y2 = Y0 + (X2 − X0) × (Y1 − Y0) / (X1 − X0). This finds the proportional Y value at your target X based on the slope between the two known points.

Can I use this calculator for extrapolation?

Yes. If your target X2 falls outside the range of X0 and X1, the calculator will extrapolate — extending the line beyond the known points. Keep in mind that extrapolation assumes the linear trend continues, which may not be accurate for all real-world data.

What happens if X0 and X1 are the same value?

If X0 equals X1, the denominator in the interpolation formula becomes zero, making the calculation undefined. Make sure your two known X values are different to get a valid result.

What fields do I need to fill in?

You need four known values — X0 and Y0 for the first data point, and X1 and Y1 for the second data point — plus the target X2 for which you want to find the estimated Y2.

Is linear interpolation always accurate?

Linear interpolation assumes a straight-line relationship between two points, which works well when data changes gradually and uniformly. For curved or rapidly changing data, more advanced methods like polynomial or spline interpolation may give better results.

What are common uses of interpolation?

Interpolation is widely used in engineering (reading values from lookup tables), finance (estimating rates), meteorology (weather modeling), computer graphics (smooth animations), and scientific data analysis where measurements exist only at discrete points.

Can the interpolated Y2 be negative?

Yes. If the trend between your two known points decreases and your target X is beyond the lower-valued point, the interpolated Y2 can be negative. The formula handles negative values correctly as long as your inputs are valid.