The Pearson correlation coefficient measures the linear association between two scale variables. The correlation reported in the table is negative(!), although not significantly different from 0, which suggests that designers should not focus their efforts on making cars more fuel efficient because there isn't an appreciable effect on sales.

However, the Pearson correlation coefficient works best when the variables are approximately normally distributed and have no outliers. A scatterplot can reveal these possible problems.

To produce a scatterplot of Sales in thousands by Fuel efficiency, from the menus choose:

Graphs
Scatter...

Click Define.

Select Sales in thousands as the y variable and Fuel efficiency as the x variable.

Click OK.

The resulting scatterplot shows a point that is far to the right of the others.

To identify the point, activate the graph by double-clicking on it.

Click the Point Identification tool.

Select the point. It is identified as case 27.

The scatterplot shows that Sales in thousands has a skewed distribution. Moreover, there is an outlier in Fuel efficiency. By fixing these problems, you can improve the estimates of the correlation.