The interpolation methods that are used to generate a surface give the best results if the data is normally distributed (a bell-shaped curve). If your data is skewed (lopsided), you might choose to transform the data to make it normal. Thus, it is important to understand the distribution of your data before creating a surface. The Histogram tool plots frequency histograms for the attributes in the dataset, enabling you to examine the univariate (one-variable) distribution for each attribute in the dataset. Next, you will explore the distribution of ozone for the ca_ozone_pts layer.

To do so, click the Geostatistical Analyst drop-down arrow, point to Explore Data, then click Histogram. Click the Layer drop-down arrow and click ca_ozone_pts. Click the Attribute drop-down arrow and click OZONE. You may want to resize the Histogram dialog box so you can also see the map.

The distribution of the ozone attribute is depicted by a histogram with the range of values separated into 10 classes. The frequency of data within each class is represented by the height of each bar.

Generally, the important features of the distribution are its central value, spread, and symmetry. As a quick check, if the mean and the median are approximately the same value, you have one piece of evidence that the data may be normally distributed.

The histogram indicates that the data is unimodal (one hump) and fairly symmetric. It appears to be close to a normal distribution. The right tail of the distribution indicates the presence of a relatively small number of sample points with large ozone concentration values.

Click the histogram bar with ozone values ranging from 0.162 to 0.175 ppm. Note that the x-axis values have been rescaled by a factor of 10 to make them easier to read.

The sample points within this range are selected on the map. Note that these sample points are located within the Los Angeles region. Close the histogram dialog box.