However, even a cursory glance at the data ( see Figure 2 in the documentation for the VARIOGRAM procedure) indicates that the thickness is not linear in the North-South direction, so the following DATA step creates a quadratic effect. A naive model might attempt to fit the thickness of the coal seam as a linear function of the East-West position (the East variable) and the South-North position (the North variable). The data are 75 locations and measurements of the thickness of a coal seam. This section creates a regression model that (intentionally) does NOT fit the data. This article also includes graphs of the residuals plotted against the explanatory variables.Ĭreate a model that does not fit the data (Lower left of panel.)Ī plot that compares the cumulative distributions of the centered predicted values and the residuals. Two graphs that assess the normality of the residuals. Two graphs that indicate outliers, high-leverage observations, and influential observations. Two graphs of residual values versus the predicted responses. The eight plots can be classified into five groups:Ī plot of the observed versus predicted responses. To make the discussion as simple as possible, this article uses PROC REG to fit an ordinary least squares model to the data. This article discusses the eight plots in the DiagnosticsPanel plot, which is produced by several SAS regression procedures. For an ill-fitting model, the diagnostic plots should indicate the lack of fit.
#Sas jmp negative binomial regression output how to
This article shows how to interpret diagnostic plots for a model that does not fit the data. Some procedures (most notably PROC REG and PROC LOGISTIC) support dozens of graphs that help you to evaluate the fit of the model, to determine whether the data satisfy various assumptions, and to identify outliers and high-leverage points.ĭiagnostic plots are most useful when the size of the data is not too large, such as less than 5,000 observations. Most SAS regression procedures support the PLOTS= option, which you can use to generate a panel of diagnostic plots. SAS, like most statistical software, makes it easy to generate regression diagnostics plots. When you fit a regression model, it is useful to check diagnostic plots to assess the quality of the fit.
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