Note: See Median Chart Examples in the SAS/QC Sample Library.
You can save the control limits for a median chart in a SAS data set; this enables you to apply the control limits to future data (see Reading Preestablished Control Limits) or modify the limits with a DATA step program.
The following statements read measurements from the data set Detergent
(see Creating Charts for Medians from Raw Data) and save the control limits displayed in Figure 17.35 in a data set named Detlim
:
proc shewhart data=Detergent; mchart Weight*Lot / outlimits=Detlim nochart; run;
The OUTLIMITS= option names the data set containing the control limits, and the NOCHART option suppresses the display of the
charts. The data set Detlim
is listed in Figure 17.40.
The data set Detlim
contains one observation with the limits for the process Weight
. The variables _LCLM_
and _UCLM_
contain the lower and upper control limits for the medians, and the variable _MEAN_
contains the central line. The value of _MEAN_
is an estimate of the process mean, and the value of _STDDEV_
is an estimate of the process standard deviation . The value of _LIMITN_
is the nominal sample size associated with the control limits, and the value of _SIGMAS_
is the multiple of associated with the control limits. The variables _VAR_
and _SUBGRP_
are bookkeeping variables that save the process and subgroup-variable. The variable _TYPE_
is a bookkeeping variable that indicates whether the values of _MEAN_
and _STDDEV_
are estimates or standard values.
The variables _LCLR_
, _R_
, and _UCLR_
are not used to create median charts, but they are included so the data set Detlim
can be used to create an R chart; see MRCHART Statement: SHEWHART Procedure and RCHART Statement: SHEWHART Procedure. If you specify the STDDEVIATIONS option in the MCHART statement, the variables _LCLS_
, _S_
, and _UCLS_
are included in the OUTLIMITS= data set. These variables can be used to create an s chart; see SCHART Statement: SHEWHART Procedure. For more information, see OUTLIMITS= Data Set.
You can create an output data set containing both control limits and summary statistics with the OUTTABLE= option, as illustrated by the following statements:
proc shewhart data=Detergent; mchart Weight*Lot / outtable=Dtable nochart; run;
The data set Dtable
is listed in Figure 17.41.
Figure 17.41: The Data Set Dtable
Summary Statistics and Control Limit Information |
_VAR_ | Lot | _SIGMAS_ | _LIMITN_ | _SUBN_ | _LCLM_ | _SUBMED_ | _MEAN_ | _UCLM_ | _STDDEV_ | _EXLIM_ |
---|---|---|---|---|---|---|---|---|---|---|
Weight | 1 | 3 | 5 | 5 | 20.7554 | 22.56 | 24.4996 | 28.2439 | 2.33041 | |
Weight | 2 | 3 | 5 | 5 | 20.7554 | 23.54 | 24.4996 | 28.2439 | 2.33041 | |
Weight | 3 | 3 | 5 | 5 | 20.7554 | 24.35 | 24.4996 | 28.2439 | 2.33041 | |
Weight | 4 | 3 | 5 | 5 | 20.7554 | 25.52 | 24.4996 | 28.2439 | 2.33041 | |
Weight | 5 | 3 | 5 | 5 | 20.7554 | 23.25 | 24.4996 | 28.2439 | 2.33041 | |
Weight | 6 | 3 | 5 | 5 | 20.7554 | 23.01 | 24.4996 | 28.2439 | 2.33041 | |
Weight | 7 | 3 | 5 | 5 | 20.7554 | 24.19 | 24.4996 | 28.2439 | 2.33041 | |
Weight | 8 | 3 | 5 | 5 | 20.7554 | 26.27 | 24.4996 | 28.2439 | 2.33041 | |
Weight | 9 | 3 | 5 | 5 | 20.7554 | 22.31 | 24.4996 | 28.2439 | 2.33041 | |
Weight | 10 | 3 | 5 | 5 | 20.7554 | 22.66 | 24.4996 | 28.2439 | 2.33041 | |
Weight | 11 | 3 | 5 | 5 | 20.7554 | 26.38 | 24.4996 | 28.2439 | 2.33041 | |
Weight | 12 | 3 | 5 | 5 | 20.7554 | 23.01 | 24.4996 | 28.2439 | 2.33041 | |
Weight | 13 | 3 | 5 | 5 | 20.7554 | 23.15 | 24.4996 | 28.2439 | 2.33041 | |
Weight | 14 | 3 | 5 | 5 | 20.7554 | 24.73 | 24.4996 | 28.2439 | 2.33041 | |
Weight | 15 | 3 | 5 | 5 | 20.7554 | 25.86 | 24.4996 | 28.2439 | 2.33041 | |
Weight | 16 | 3 | 5 | 5 | 20.7554 | 23.99 | 24.4996 | 28.2439 | 2.33041 | |
Weight | 17 | 3 | 5 | 5 | 20.7554 | 24.30 | 24.4996 | 28.2439 | 2.33041 | |
Weight | 18 | 3 | 5 | 5 | 20.7554 | 24.88 | 24.4996 | 28.2439 | 2.33041 | |
Weight | 19 | 3 | 5 | 5 | 20.7554 | 25.92 | 24.4996 | 28.2439 | 2.33041 | |
Weight | 20 | 3 | 5 | 5 | 20.7554 | 25.63 | 24.4996 | 28.2439 | 2.33041 | |
Weight | 21 | 3 | 5 | 5 | 20.7554 | 25.17 | 24.4996 | 28.2439 | 2.33041 | |
Weight | 22 | 3 | 5 | 5 | 20.7554 | 26.72 | 24.4996 | 28.2439 | 2.33041 | |
Weight | 23 | 3 | 5 | 5 | 20.7554 | 23.67 | 24.4996 | 28.2439 | 2.33041 | |
Weight | 24 | 3 | 5 | 5 | 20.7554 | 24.92 | 24.4996 | 28.2439 | 2.33041 | |
Weight | 25 | 3 | 5 | 5 | 20.7554 | 24.51 | 24.4996 | 28.2439 | 2.33041 | |
Weight | 26 | 3 | 5 | 5 | 20.7554 | 24.75 | 24.4996 | 28.2439 | 2.33041 | |
Weight | 27 | 3 | 5 | 5 | 20.7554 | 25.10 | 24.4996 | 28.2439 | 2.33041 | |
Weight | 28 | 3 | 5 | 5 | 20.7554 | 25.64 | 24.4996 | 28.2439 | 2.33041 |
This data set contains one observation for each subgroup sample. The variables _SUBMED_
and _SUBN_
contain the subgroup medians and subgroup sample sizes. The variables _LCLM_
and _UCLM_
contain the lower and upper control limits, and the variable _MEAN_
contains the central line. The variables _VAR_
and Lot
contain the process name and values of the subgroup-variable, respectively. For more information, see OUTTABLE= Data Set.
An OUTTABLE= data set can be read later as a TABLE= data set. For example, the following statements read Dtable
and display a median chart (not shown here) identical to the chart in Figure 17.35:
title 'Median Chart for Detergent Box Weight'; proc shewhart table=Dtable; mchart Weight*Lot; run;
Because the SHEWHART procedure simply displays the information in a TABLE= data set, you can use TABLE= data sets to create specialized control charts (see Specialized Control Charts: SHEWHART Procedure). For more information, see TABLE= Data Set.