Download MATLAB CURVE FITTING TOOLBOX - RELEASE NOTES User`s guide
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3 Fitting Data Again, the graphical results show that the linear model is reasonable for the majority of data points, and the residuals appear to be randomly scattered around zero. However, three residuals stand out. The largest residual corresponds to Palm Beach County. The other residuals are at the two largest predictor values, and correspond to Miami/Dade County and Broward County. The numerical results are shown below. The inverse slope of the robust fit indicates that Buchanan should receive one vote for every 189.3 Gore votes. Using the fitted slope value, you can determine the expected number of votes that Buchanan should have received for each fit. For the Buchanan versus Bush data, you evaluate the fit at a predictor value of 152,951. For the Buchanan versus Gore data, you evaluate the fit at a predictor value of 269,732. These results are shown below for both data sets and both fits. Table 3-5: Expected Buchanan Votes in Palm Beach County Data Set Fit Expected Buchanan Votes Buchanan vs. Bush Regular least squares 814 Robust least squares 775 Regular least squares 1246 Robust least squares 1425 Buchanan vs. Gore The robust results for the Buchanan versus Bush data suggest that Buchanan received 3411 – 775 = 2636 excess votes, while robust results for the Buchanan versus Gore data suggest that Buchanan received 3411 – 1425 = 1986 excess votes. 3-66