The next step is to model the trend as a function of hour. The chart in Figure 17.165 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 17.166.
data submeans; set submeans; loghour=log(hour); run;
proc reg data=submeans ; model Diameterx=loghour; output out=regdata predicted=fitted ; run;
Figure 17.166 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 17.167.
[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.