Engineering Data Regression Analyzer

Paste measurements or import CSV data, compare curve-fit models, inspect R²/RMSE and residuals, interpolate between observations, and predict new values — all locally in your browser.

Local analysis · no data upload

Fit summary

Selected model
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R²
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RMSE
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MAE
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N
0
Equation will appear after data is loaded.
Scatter plot & fitted curve
MeasuredSelected fit
Load data to visualize measurements and regression.
Residual diagnostics
Residual = measured − predicted
Residuals appear when a valid model is selected.

Model comparison

same measured dataset
ModelR²RMSEMAEStatus
Load data to compare models.

Analyzed points

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#XYPredictedResidual
No data loaded.
Regression is descriptive unless your experimental design supports causal interpretation. Verify units, sensor quality, outliers, extrapolation limits and physical constraints before engineering decisions.
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Engineering Data Regression Analyzer — Curve Fitting & Residuals

Analyze X-Y data with scatter plots, regression fits, R², RMSE, residuals, interpolation and predictions from pasted or uploaded CSV data.

What this tool does

This browser-based analyzer accepts pasted tabular data or CSV/TXT files, maps numeric X and Y columns, plots measured points, fits several regression models and compares their diagnostics. It also distinguishes local linear interpolation from model-based prediction.

Who this is for

How to use this tool

  1. Paste X-Y data or choose a CSV/TXT file, then parse it
  2. Select the independent X column and measured Y column and set axis labels
  3. Choose linear, polynomial, exponential, logarithmic or power regression, or use Best Fit
  4. Review the equation, fitted curve, R², RMSE, MAE, residual plot and analyzed points
  5. Enter an X value for interpolation and prediction, then export the analyzed CSV when needed

Inputs

Inputs are tabular X-Y data, selected columns, axis labels, polynomial degree from 2 to 5, a selected model and an optional X value for prediction. Non-numeric rows are ignored; duplicate X values remain in regression and are averaged for interpolation.

Outputs

Outputs include scatter and fitted-curve plots, model comparison, equation, R², RMSE, MAE, residuals, linear interpolation inside the measured range, model prediction, and an analyzed CSV export.

Limitations and responsible use

Analysis runs locally in the browser. Transformed models have domain requirements, higher-order fits can overfit sparse data, and predictions outside the measured range are extrapolations that need engineering validation.

Example use cases

Frequently asked questions

Does this upload my data?

No. The page parses and analyzes the selected or pasted data in the browser; it does not provide a server-side data workflow.

Is Best Fit a proof that the model is correct?

No. Best Fit ranks valid models by R² on the original Y scale. Use residuals, physical knowledge, measurement uncertainty and validation data before relying on a model.

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