Find a quadratic line of best fit with this free online quadratic regression calculator

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## How to perform quadratic regression online 2. Select the independent (X) and dependent (Y) variables
3. Select the fit order
4. The regression analysis will be performed

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The quadratic regression calculator can be used to fit a quadratic equation to a set of input data points.

The quadratic regression calculator will find a line of best fit according to the value of the order parameter.

The fit is found using the least squares method. The least squares method finds a regression fit that minimizes the distance between input x values and observed y values.

Statisticians sometimes describe quadratic regression as a type of linear regression. Fitting a quadratic equation to a set of input points requires solving a set of linear equations.

This quadratic regression calculator fits a quadratic regression model to input predictor variables.

The equation below shows the second-order quadratic regression formula.

$y = ax^2 + bx + c$

Where $y$ is the predicted response variable and x is the measured predictor variable. $a$, $b$ and $c$ are the calculated regression coefficients.

## What Is the Difference Between Quadratic Regression and Multiple Linear Regression

Quadratic regression is used to find a quadratic line of best fit for one response variable based on one predictor variable. Statisticians sometimes call this a form of simple linear regression because there is one predictor variable, one response variable and the regression equations are linear.

Multiple linear regression is used to find a line of best fit for one response variable based on the values of one or more predictor variables.

You could model a car's fuel efficiency based on its weight using quadratic regression. You could model a car's fuel efficiency based on its weight and its horsepower using multiple linear regression.

## What Is the Difference Between the Correlation Coefficient and Regression Fit

The correlation coefficient is used to measure how strong the linear relationship is between two variables. It is a number between -1 and 1.

Quadratic regression is used to fit a function to the relationship between input x and y values.

If two variables have a non-linear relationship (e.g. they are best fit with $y=x^2$), then the quadratic regression calculator might find a good fit, but the two variables might have a poor Pearson's correlation coefficient.