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11.7: Residual Plots

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Residual Plots

11.7: Residual Plots

A residual plot is a statistical representation of data used to analyze correlation and regression results. It helps verify the requirements for drawing specific conclusions about correlation and regression. To obtain the residual plot, first, the residual for each data value is calculated, which is simply the vertical distance between the observed and the predicted value obtained from the regression equation.

When the residual values are plotted against the variable x, it is called a residual plot. The pattern formed by the scatter points in such a plot can be used to draw inferences about the data set. For example, if the scatter points have a linear pattern, it confirms that the regression line is a good fit for the dataset containing x and y values. Conversely, a non-linear pattern in the residual plot with predominantly positive residuals in some ranges, whereas negative in others, indicates that the regression equation is not a good model for the given set of x and y values. Additionally, a residual plot that shows a thickening pattern when viewed from left to right indicates that the regression line isn't a good model.


Residual Plots Statistical Representation Correlation Regression Results Residual Values Vertical Distance Observed Value Predicted Value Regression Equation Scatter Points Linear Pattern Non-linear Pattern Positive Residuals Negative Residuals Model Fit

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