Least Squares Sine Fit Guide
A sine pattern appears in many measured systems. It can describe sound, tides, vibration, seasons, electrical waves, and rotating motion. Real data is rarely perfect. Sensors add noise. Sampling may be uneven. A least squares sine fit finds the smooth curve that stays closest to all points.
What the Fit Finds
The calculator models data with amplitude, angular frequency, phase, and vertical offset. Amplitude shows half the wave height. Angular frequency controls how fast the cycle repeats. Phase shifts the curve left or right. Offset moves the center line up or down.
Why Least Squares Works
Least squares minimizes squared residuals. A residual is the measured value minus the fitted value. Squaring makes positive and negative errors add together. Large errors also receive more attention. This gives a stable fit for noisy mathematical data.
Frequency Handling
If the angular frequency is known, enter it directly. The page then solves a linear regression for sine, cosine, and offset terms. If the frequency is unknown, use the search option. The calculator tests many angular frequencies and selects the one with the smallest error. A refinement pass improves the final value.
Useful Output
The result includes amplitude, phase, offset, frequency, period, and fitted equation. It also reports error measures, such as SSE, RMSE, MAE, and R squared. These values help judge whether the wave explains the data well. A high R squared usually means a strong repeating pattern.
Practical Tips
Use several points across more than one cycle. Avoid fitting only one small section of a wave. Choose a realistic frequency search range. Too wide a range can find a false local answer. Weights can raise the influence of trusted points. Keep weak or uncertain points with lower weights.
When data contains gaps, keep the x values unchanged. Uneven spacing is allowed. The method uses each recorded coordinate directly. That makes it useful for lab records and daily field logs.
Interpreting the Model
The fitted equation is best viewed as an approximation. It should not replace subject knowledge. Check residuals before using the curve for decisions. Random residuals are a good sign. Clear residual patterns may mean the data needs another term, a trend, or a different model.