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Basic Analysis and Graphing - SAS

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Chapter 5 Performing Oneway <strong>Analysis</strong> 161<br />

CDF Plot<br />

CDF Plot<br />

A CDF plot shows the cumulative distribution function for all of the groups in the Oneway report. CDF<br />

plots are useful if you want to compare the distributions of the response across levels of the X factor.<br />

Related Information<br />

• “Example of a CDF Plot” on page 175<br />

Densities<br />

The Densities options provide several ways to compare the distribution <strong>and</strong> composition of the response<br />

across the levels of the X factor. There are three density options:<br />

• Compare Densities shows a smooth curve estimating the density of each group. The smooth curve is<br />

the kernel density estimate for each group.<br />

• Composition of Densities shows the summed densities, weighted by each group’s counts. At each X<br />

value, the Composition of Densities plot shows how each group contributes to the total.<br />

• Proportion of Densities shows the contribution of the group as a proportion of the total at each X level.<br />

Related Information<br />

• “Example of the Densities Options” on page 176<br />

Matching Column<br />

Use the Matching Column option to specify a matching (ID) variable for a matching model analysis. The<br />

Matching Column option addresses the case when the data in a one-way analysis come from matched<br />

(paired) data, such as when observations in different groups come from the same subject.<br />

Note: A special case of matching leads to the paired t-test. The Matched Pairs platform h<strong>and</strong>les this type<br />

of data, but the data must be organized with the pairs in different columns, not in different rows.<br />

The Matching Column option performs two primary actions:<br />

• It fits an additive model (using an iterative proportional fitting algorithm) that includes both the<br />

grouping variable (the X variable in the Fit Y by X analysis) <strong>and</strong> the matching variable that you select.<br />

The iterative proportional fitting algorithm makes a difference if there are hundreds of subjects, because<br />

the equivalent linear model would be very slow <strong>and</strong> would require huge memory resources.<br />

• It draws lines between the points that match across the groups. If there are multiple observations with<br />

the same matching ID value, lines are drawn from the mean of the group of observations.<br />

The Matching Column option automatically activates the Matching Lines option connecting the matching<br />

points. To turn the lines off, select Display Options > Matching Lines.

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