Fuel efficiency. Figure 1.31 is a Normal quantile plot for the fuel efficiency data. We looked at these data in Example 1.28. A histogram was used to display the distribution in Figure 1.17 (page 39). This distribution is approximately Normal.

(a) How is this fact displayed in the Normal quantile plot?

(b) Does the plot reveal any deviations from Normality? Explain your answer.

There are several variations on the way that diagnostic plots are used to assess Normality. We have chosen to plot the data on the y axis and the normal scores on the x axis. Some software packages switch the axes. These plots are sometimes called “Q-Q Plots.’’ Other plots transform the data and the normal scores into cumulative probabilities and are called “P-P Plots.’’ The basic idea behind all these plots is the same.

Plots with points that lie close to a straight line indicate that the data are approximately Normal. When using these diagnostic plots, you should always look at a histogram or other graphical summary of the distribution to help you interpret the plot.