This Quadratic Regression Calculator quickly and simply calculates the equation of the quadratic regression function and the associated correlation coefficient. In addition, it generates a scatter plot that depicts the curve of best fit.
Quadratic Regression Calculator
What is Quadratic Regression Calculator
A quadratic regression calculator is a tool used to analyze the relationship between two variables by fitting a quadratic equation to the data. It calculates the coefficients of the quadratic equation and provides statistical measures associated with the quadratic regression model.
The quadratic regression model assumes a curved relationship between the independent variable (X) and the dependent variable (Y), following a quadratic equation of the form Y = β₀ + β₁X + β₂X². By estimating the values of the coefficients β₀, β₁, and β₂, the model finds the bestfitting quadratic curve that minimizes the sum of squared differences between the observed Y values and the predicted Y values on the curve.
Here's how you can use a quadratic regression calculator:

Input your data: You need a set of paired data points, where you have observations for both the independent variable (X) and the dependent variable (Y).

Perform the calculation: The quadratic regression calculator will compute the coefficients β₀, β₁, and β₂, along with other statistical measures like the coefficient of determination (R²), standard error, and pvalues.

Interpret the results: The coefficient β₂ determines the shape of the curve. If β₂ is positive, the curve is concave up, and if β₂ is negative, the curve is concave down. The coefficients β₀ and β₁ represent the intercept and linear coefficient, respectively. The coefficient of determination (R²) indicates the proportion of variability in the dependent variable explained by the quadratic regression model.

Visualize the quadratic curve: The calculator may display a scatter plot of the data points with the fitted quadratic curve to visualize the relationship between the variables.
It's important to note that while a quadratic regression model allows for a curved relationship, it may not always be the most appropriate model for the data. It's recommended to check the assumptions of the quadratic regression model and consider alternative models if necessary.
If you provide me with the specific data points or any additional details, I can assist you further by performing a quadratic regression analysis.
The Quadratic Regression Calculator uses the following formulas:
Quadratic regression: y = ax^{2} + bx + c, where a ≠ 0
Coefficients (a, b, c):
Mean x: x̄ = ∑x_{i} / n
Mean y: ȳ = ∑y_{i} / n
Correlation coefficient r:
Where:
n is the total number of samples,
x_{i} (x_{1}, x_{2}, ... ,x_{n}) are the x values,
y_{i} (y_{1}, y_{2}, ... ,y_{n}) are the y values,
∑x_{i} is the sum of x values,
∑y_{i} is the sum of y values,
∑x_{i} y_{i} is the sum of products of x and y values,
∑x_{i}^{2} is the sum of squares of x values,
∑x_{i}^{3} is the sum of the cubes of x values,
∑x_{i}^{4} is the sum of the fourth powers of x values.
Quadratic Regression Calculator example
Certainly! Here's an example of how you can calculate quadratic regression using a simple dataset:
Let's say we have the following dataset:
X = [1, 2, 3, 4, 5] # Independent variable Y = [3, 6, 11, 18, 27] # Dependent variable
To calculate the quadratic regression, we need to find the equation of a quadratic function that best fits the data. The equation for a quadratic function is given by:
Y = a * X^2 + b * X + c
where "a" is the coefficient of the quadratic term, "b" is the coefficient of the linear term, and "c" is the constant term.
We can use the least squares method to estimate the values of "a", "b", and "c" that minimize the sum of squared differences between the actual Y values and the predicted Y values from the quadratic function.
In this example, we can calculate the quadratic regression with the following steps:
Step 1: Calculate the mean of X and Y.
 Mean(X) = (1 + 2 + 3 + 4 + 5) / 5 = 3
 Mean(Y) = (3 + 6 + 11 + 18 + 27) / 5 = 13
Step 2: Calculate the deviations from the mean for X and Y.
 Deviations(X) = [1  3, 2  3, 3  3, 4  3, 5  3] = [2, 1, 0, 1, 2]
 Deviations(Y) = [3  13, 6  13, 11  13, 18  13, 27  13] = [10, 7, 2, 5, 14]
Step 3: Calculate the product of deviations for X and Y.
 Product(XY) = [2 * 10, 1 * 7, 0 * 2, 1 * 5, 2 * 14] = [20, 7, 0, 5, 28]
Step 4: Calculate the squared deviations for X.
 Squared(X) = [(2)^2, (1)^2, 0^2, 1^2, 2^2] = [4, 1, 0, 1, 4]
Step 5: Calculate the product of squared deviations for X and Y.
 Product(X^2*Y) = [4 * 10, 1 * 7, 0 * 2, 1 * 5, 4 * 14] = [40, 7, 0, 5, 56]
Step 6: Calculate the product of squared deviations for X^2.
 Product(X^2*X) = [4 * 2, 1 * 1, 0 * 0, 1 * 1, 4 * 2] = [8, 1, 0, 1, 8]
Step 7: Calculate the sum of squared deviations for X.
 Sum(Squared(X)) = 4 + 1 + 0 + 1 + 4 = 10
Step 8: Calculate the sum of product of squared deviations for X and Y.
 Sum(Product(X^2*Y)) = 40 + 7 + 0 + 5 + 56 = 14
Step 9: Calculate the sum of product of squared deviations for X^2.
 Sum(Product(X^2*X)) = 8 + 1 + 0 + 1 + 8 = 0
Step 10: Calculate the determinant of the coefficients matrix.
 Determinant = (Sum(Squared(X)) * Sum(Product(X^2X)))  (Sum(Product(X^2Y)) * Sum(Product(X^2*X))) = (10 * 0)  (14 * 0) = 0
Since the determinant is zero, we cannot use the traditional method to directly solve for "a", "b", and "c". In this case, we can use other methods like matrix inversion or optimization algorithms to estimate the coefficients.
Please note that quadratic regression requires more data points to obtain accurate estimates, as it involves fitting a curve rather than a straight line.