Quadratic Regression Calculator

Author: Henrick Yau
- 4 October 2026
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Calculators

Find the best-fit quadratic equation (y = ax² + bx + c) for a set of data points. This calculator performs regression analysis to find the curve that minimizes the sum of squared residuals.

Data Input

Data Points:
X
Y
1
2
3

Calculation Options

Understanding Quadratic Regression

Quadratic regression finds the best-fitting parabola (second-degree polynomial) for a set of data points. The equation takes the form:

y = ax² + bx + c

where a, b, and c are constants determined by minimizing the sum of squared differences between observed and predicted y-values.

When to Use Quadratic Regression
  • When data shows a clear curvilinear trend (one bend)
  • For modeling phenomena with acceleration or deceleration
  • When linear regression doesn't adequately fit the data
  • For projectile motion, simple harmonic motion, or quadratic growth/decay
Interpreting the Coefficients
  • a: Controls the parabola's curvature. If a > 0, the parabola opens upward; if a < 0, it opens downward.
  • b: Influences the parabola's horizontal position and the slope at x = 0.
  • c: The y-intercept, where the curve crosses the y-axis (when x = 0).
Coefficient of Determination (R²)
  • Measures how well the model explains the variation in the data
  • Ranges from 0 to 1, where 1 indicates a perfect fit
  • Calculated as: R² = 1 - (Sum of squared residuals) / (Total sum of squares)
  • Higher values indicate a better fit of the model to the data

Disclaimer: This calculator performs quadratic regression analysis for educational and informational purposes. The accuracy of predictions depends on the quality and appropriateness of the data. A high R² value doesn't necessarily indicate causation between variables.

Quadratic Regression Formula:
y = ax² + bx + c

What quadratic regression is

The Quadratic Regression Calculator is an easy-to-use statistical analysis tool that helps you determine the best-fit quadratic equation for a given set of data points. This is especially useful when your data follows a curved pattern that a straight line cannot effectively represent.

It operates by applying a mathematical process known as quadratic regression, which finds the equation of a parabola (a second-degree polynomial) that best fits your data. This can be valuable in many fields such as physics, economics, and biology, where patterns like acceleration or curved growth trends are common.

Entering data for curve fitting

You can analyse data using one of three methods:

  • Manual Entry: Type in your X and Y data points directly.
  • Paste Data: Copy and paste data from a spreadsheet or CSV file.
  • Sample Data: Choose from preset examples like projectile motion or temperature trends.

After entering your data:

  • Choose whether to force the curve to pass through the origin (c = 0).
  • Select the desired number of decimal places for your results.
  • Optionally enter an X value to predict the corresponding Y value based on the fitted equation.
  • Click "Calculate Quadratic Regression" to see the results.

Equation, metrics and predictions

  • Fits a curved model to your data using the equation y = ax² + bx + c.
  • Displays the regression equation and coefficients (a, b, c).
  • Calculates performance metrics like R² (coefficient of determination) and standard error.
  • Predicts Y values for any given X using the fitted curve.
  • Provides a clear chart and a detailed table showing observed vs predicted values.
  • Offers a step-by-step breakdown of the regression calculation (optional view).

When to fit a parabola to data

This data analysis helper is ideal when your data shows a curved or U-shaped pattern, such as in:

  • Projectile motion or physical trajectories
  • Price trends over time
  • Growth and decay patterns in populations or investments
  • Weather or temperature fluctuations

Unlike a linear regression tool, which fits straight lines, this calculator captures turning points and curvature in the data, offering deeper data insights and more accurate modelling.

Questions about quadratic regression

What is quadratic regression used for?

Quadratic regression is used when data trends show curvature. It helps create models for situations involving acceleration, deceleration, or parabolic behaviour.

What do the coefficients a, b, and c mean?

  • a: Controls how wide or narrow the curve is, and whether it opens upward or downward.
  • b: Affects the tilt and position of the curve.
  • c: Indicates where the curve intersects the Y-axis.

What is R², and why is it important?

R² (the coefficient of determination) measures how well the equation fits your data. A value closer to 1 means the model explains the variation in the data well.

Can I use this for prediction?

Yes. After calculating the regression, enter an X value to get the corresponding predicted Y value based on the model.

How is this different from a linear regression calculator?

While a linear regression calculator finds the best straight-line fit, this tool fits a curve. Use it when your data forms a parabola rather than a line.

Complementing other statistical tools

This calculator is part of a wider set of statistical tools used for analysing data. Whether you're working with a statistics calculator, a standard deviation tool, or looking to understand data variance, this quadratic regression tool adds powerful curve-fitting capabilities to your data analysis efforts.

It complements other statistical computation resources like the Linear Regression Calculator, Mean, Median, Mode Calculator, and Standard Deviation Calculator, making it easier to interpret trends, identify outliers, and make informed predictions.