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Paper 75006 What's happening if there is B” in the general linear modeling output? Shillong Huang, Kelley Blue Book Inc., Irvine, CA ABSTRACT The SAS GLM procedure is an efficient tool in statistical

This paper is a brief discussion of this topic. BACKGROUND Our data come from three different population registries (the U.S. Census Bureau, the U.S. Bureau of Labor Statistics, and NLS) and come from a sample of more than 100 families. We have four different sets of equations for using the U.S. Census data to estimate the population for each family. The first of these sets of equations includes the terms for the “state” variable, the U.S. Census variable, the “race” variable, and the “census region.” After we have added the “state” and “race” variables, the second set of equations have not been fully calculated: the model for state and race now has eight equations, including the four that had only five variables in the first set of equations. It became clear to each of us during the evaluation that we would not be able to estimate state and race for the population as a whole. We used the third set of equations, the first of the four that had only five variables, to estimate state and race for the U.S. Census sample. We can see in Table 1 that state and race for the U.S. Census sample are slightly better than for the third and fourth groups of equations (with four and 10 variables). TABLE 1 Estimate from the 5-state (U.S. Census) General Linear Models of state and race with only the state variable, the census variable and the race variable, but no other variables We can easily understand why the four sets of equations differ in how far the estimated values move from 0 when there is a B” than when there is not. The results of these sets of equations are shown in Figure 1. Figure 1. Distribution of the estimated state and racial variables in the models for the five-state (U.S. Census) General Linear Models: The three variables (states, races, and census regions) represent the four sets of equations that we used to get the state and racial variables in the U.S. Census sample. We can see that state and race for the U.S. Census sample are slightly better than the state and race given for the third and fourth sets of equations (with four and 10 variables). As can be seen in the figure, the coefficients in the fourth set of equations are lower, indicating higher uncertainty in the estimates under B” than under the other three sets of equations.

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