Answer:
residual is the difference between what is plotted in your scatter plot at a specific point, and what the regression equation predicts "should be plotted" at this specific point. If the scatter plot and the regression equation "agree" on a y-value (no difference), the residual will be zero.
definition
Residual = Observed y-value - Predicted y-value
A residual is the difference between the observed y-value (from scatter plot) and the predicted y-value (from regression equation line).
It is the vertical distance from the actual plotted point to the point on the regression line.
You can think of a residual as how far the data "fall" from the regression line
(sometimes referred to as "observed error").
Linear associations are the most popular statistical relationships since they are easy to read and interpret. We will spend the majority of our time working with linear relationships, and residuals can tell us when we have an appropriate linear model.
When you look at your scatter plot, and you are unsure if the shape (curve) you chose for your regression equation will create the best model, a residual plot will help you make a decision as to whether the model you chose will, or will not, be an appropriate linear model.
A residual plot is a scatter plot that shows the residuals on the vertical axis and the independent variable on the horizontal axis. The plot will help you to decide on whether a linear model is appropriate for your data.
Appropriate linear model: when plots are randomly placed, abov
