The next step is to model the trend as a function of hour. The chart in Figure 18.165 suggests that the mean level of the process (saved as DiameterX
in the OUTLIMITS=
data set submeans
) grows as the log of hour
. The following statements fit a simple linear regression model in which DiameterX
is the response variable and loghour
(the log transformation of hour
) is the predictor variable. Part of the printed output produced by PROC REG is shown in Figure 18.166.
data submeans; set submeans; loghour=log(hour); run;
proc reg data=submeans ; model Diameterx=loghour; output out=regdata predicted=fitted ; run;
Figure 18.166: Trend Analysis for Diameter
from PROC REG
Figure 18.166 shows that the fitted equation can be expressed as
where is the fitted subgroup average.[39] A partial listing of the OUT= data set REGDATA created by the REG procedure is shown in Figure 18.167.
Figure 18.167: Partial Listing of the Output Data Set regdata
from the REG Procedure
[39] Although this example does not check for the existence of a trend, you should do so by using the hypothesis tests provided by the REG procedure.