By Allison S. K., Duane W.
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Additional info for Absorption Measurements of Certain Changes in the Average Wave-Length of Tertiary X-Rays
254-5) When this table is analyzed as a binary process, with the methods of the previous section, we do not find any dependence over time. Let us see if anything changes when we take into account the different numbers of patients arriving each day. Since there is only one day with four patients entering, we shall take the fourth and last state as being three or more entries. Our four possible events are zero, one, two, and three or more patients during the day. 000 We, then, simply fit the independence model as in the previous section.
000 1. 000 28 $UNits 64 $ERror P $Yvariate tab2 $FActor fffO 4 fff1 4 fff2 4 $Fit fffO+fff1 *fff2 $Use CHIT $Display E We now have a considerable number of zeroes in the model and, as expected, three parameters cannot be estimated. These are indicated in the GUM output by their very large standard errors. In addition, the independence model appears to fit satisfactorily. f. f. e. 1073 FFFO(3} -1. 000 1 2 3 4 5 6 7 8 9 Nevertheless, we fit the two models for dependence one and two steps back, but without displaying the parameter values.
35704 25 above. Since we did not fit the saturated model above, we cannot compare parameter values, but they would be identical. $CAlculate w(2)=O $Fit . $Fit +f2 $Display E scaled deviance d. f. f. e. 1 -1. 3218 scale parameter taken as 1. 975 above. FFF2(2) above, giving the dependence one and two steps back. Only the second is significantly different from zero. $CAlculate w(3)=O $Fit . f. f. e. 000 We did not fit the three-step model above. Here we see from the standard error for F3(2) that the third step is not necessary.
Absorption Measurements of Certain Changes in the Average Wave-Length of Tertiary X-Rays by Allison S. K., Duane W.