![]() Predicted residual if residuals are sampled from a Gaussian distribution Prism provides five types of graphs that can be used to investigate the residuals of a model fit:Ībsolute value of residual or weighted residual Weighted nonlinear regression minimizes the sum of the squares of these weighted residuals.Įarlier versions of Prism (up to Prism 4) always plotted basic unweighted residuals, even if you chose to weight the points unequally. The weighted residual is defined as the residual divided by Y. This means that the squared residual is divided by Y 2. The Prism dialog gives the choice to weight by 1/Y 2. Note the ambiguity in defining weighting. Weighted nonlinear regression minimizes the sum of the square of these residuals. In this case, the residual is defined to be the distance of the point from the curve divided by the Y value of the curve. ![]() The most common common alternative weighting is "Weight by 1/Y 2 (minimize relative distances squared)". The residual that Prism tabulates and plots equals the residual defined in the prior paragraph, divided by the weighting factor. If you choose to weight your data unequally, Prism adjusts the definition of the residuals accordingly. Mild deviations of data from a model are often easier to spot on a residual plot than on the plot of data with curve. Create a residual plot to see how well your data follow the model you selected. A residual is positive when the point is above the curve, and is negative when the point is below the curve. Least-squares regression works to minimize the sum of the squares of these residuals. A residual is the distance of a point from the curve.
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