A residual is the difference between an observed value and a predicted value from a regression model. It tells us how far the prediction is from the observed value. A residual is calculated as follows:
Residual = observed value – predicted value
Or
$$r_{i}= y_{i} ~-~ \hat{y_{i}} $$
Where the regression line has the equation:
$$\hat{y_{i}} = a + bx_{i}$$
Therefore the residual of an observation is:
$$r_{i}= y_{i} ~-~ \hat{y_{i}} = y_{i} ~-~ (a + bx_{i})$$
The aim of least squares linear regression is to find the regression parameters that minimize the sum of square residuals.
This calculator finds the residuals of a linear regression analysis for the predictor and response values provided.
Please enter a list of values for the predictor and response variables in the boxes below then click the Do Linear Regression
button.
Regression Line:
y = a + bx
Goodness of fit:
Correlation coefficient r =
Residuals =