![]() Don't forget though that interpreting these plots is subjective. fits plots to look something like the above plot. This suggests that there are no outliers. No one residual "stands out" from the basic random pattern of residuals.This suggests that the variances of the error terms are equal. The residuals roughly form a "horizontal band" around the residual = 0 line.This suggests that the assumption that the relationship is linear is reasonable. The residuals "bounce randomly" around the residual = 0 line.fits plot and what they suggest about the appropriateness of the simple linear regression model: Here are the characteristics of a well-behaved residual vs. This plot is a classical example of a well-behaved residual vs. Therefore, the residual = 0 line corresponds to the estimated regression line. Do you see the connection? Any data point that falls directly on the estimated regression line has a residual of 0. Their fitted value is about 14 and their deviation from the residual = 0 line shares the same pattern as their deviation from the estimated regression line. Now, look at how and where these five data points appear in the residuals versus fits plot. Also, note the pattern in which the five data points deviate from the estimated regression line. Note that the predicted response (fitted value) of these men (whose alcohol consumption is around 40) is about 14. ![]() In case you're having trouble with doing that, look at the five data points in the original scatter plot that appears in red. ![]() You should be able to look back at the scatter plot of the data and see how the data points there correspond to the data points in the residual versus fits plot here. Note that, as defined, the residuals appear on the y-axis and the fitted values appear on the x-axis.
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