This is easy. Select the Space - Univariate Moran's I menu option and specify the variable to use, and the contiguity matrix to use. GeoDa will think for a while, and then present you with a display that shows the calculated value of Moran's I and a scatterplot (Figure 4.2).
The Moran scatterplot is an illustration of the relationship between the values of the chosen attribute at each location and the average value of the same attribute at neighboring locations. In the case shown, large Percentages of Europeans (points on the right-hand side of the plot) tend to be associated with high local average values of Percentage of Europeans (points toward the top of the plot).
It is instructive to consider each quadrant of the plot. In the upper-right quadrant are cases where both the value and local average value of the attribute are higher than the overall average value. Similarly, in the lower-left quadrant are cases where both the value and local average value of the attribute are lower than the overall average value. These cases confirm positive spatial autocorrelation. Cases in the other two quadrants indicate negative spatial autocorrelation. Depending on which groups are dominant, there will be an overall tendency towards positive or negative (or perhaps no) spatial autocorrelation.
Using linked brushing, you should be able to identify which areas of the map are most responsible for high or low observed spatial autocorrelation, and which, if any, locations run counter to the overall pattern.
For a single variable on a single map, describe the results of a global Moran's I spatial autocorrelation analysis in your write-up. Include a choropleth map and Moran scatterplot in your write-up along with commentary and your interpretation of the results. In particular, identify map areas that contribute strongly to the global outcome.