Note: See np Chart Examples in the SAS/QC Sample Library.
You can save the control limits for an 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 the number of nonconforming items per subgroup from the data set Circuits
(see Creating np Charts from Count Data) and save the control limits displayed in Figure 17.51 in a data set named Cirlim
:
proc shewhart data=Circuits; npchart Fail*Batch / subgroupn=500 outlimits=Cirlim nochart; run;
The OUTLIMITS= option names the data set containing the control limits, and the NOCHART option suppresses the display of the
chart. The data set Cirlim
is listed in Figure 17.55.
Figure 17.55: The Data Set Cirlim
Containing Control Limit Information
Control Limits for the Number of Failing Circuits |
_VAR_ | _SUBGRP_ | _TYPE_ | _LIMITN_ | _ALPHA_ | _SIGMAS_ | _P_ | _LCLNP_ | _NP_ | _UCLNP_ |
---|---|---|---|---|---|---|---|---|---|
Fail | Batch | ESTIMATE | 500 | .005040334 | 3 | 0.019467 | 0.46539 | 9.73333 | 19.0013 |
The data set Cirlim
contains one observation with the limits for process Fail
. The variables _LCLNP_
and _UCLNP_
contain the lower and upper control limits, and the variable _NP_
contains the central line. The variable _P_
contains the average proportion of nonconforming items. 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 value of _P_
is an estimate or a standard value.
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=Circuits; npchart Fail*Batch / subgroupn=500 outtable=Cirtable nochart; run;
The Cirtable
data set contains one observation for each subgroup sample. The variables _SUBNP_
and _SUBN_
contain the subgroup numbers of nonconforming items and subgroup sample sizes, respectively. The variables _LCLNP_
and _UCLNP_
contain the lower and upper control limits, and the variable _NP_
contains the central line. The variables _VAR_
and Batch
contain the process name and values of the subgroup-variable, respectively. For more information, see OUTTABLE= Data Set.
The data set Cirtable
is listed in Figure 17.56.
Figure 17.56: The Data Set Cirtable
Number Nonconforming and Control Limit Information |
_VAR_ | Batch | _SIGMAS_ | _LIMITN_ | _SUBN_ | _LCLNP_ | _SUBNP_ | _NP_ | _UCLNP_ | _EXLIM_ |
---|---|---|---|---|---|---|---|---|---|
Fail | 1 | 3 | 500 | 500 | 0.46539 | 5 | 9.73333 | 19.0013 | |
Fail | 2 | 3 | 500 | 500 | 0.46539 | 6 | 9.73333 | 19.0013 | |
Fail | 3 | 3 | 500 | 500 | 0.46539 | 11 | 9.73333 | 19.0013 | |
Fail | 4 | 3 | 500 | 500 | 0.46539 | 6 | 9.73333 | 19.0013 | |
Fail | 5 | 3 | 500 | 500 | 0.46539 | 4 | 9.73333 | 19.0013 | |
Fail | 6 | 3 | 500 | 500 | 0.46539 | 9 | 9.73333 | 19.0013 | |
Fail | 7 | 3 | 500 | 500 | 0.46539 | 17 | 9.73333 | 19.0013 | |
Fail | 8 | 3 | 500 | 500 | 0.46539 | 10 | 9.73333 | 19.0013 | |
Fail | 9 | 3 | 500 | 500 | 0.46539 | 12 | 9.73333 | 19.0013 | |
Fail | 10 | 3 | 500 | 500 | 0.46539 | 9 | 9.73333 | 19.0013 | |
Fail | 11 | 3 | 500 | 500 | 0.46539 | 8 | 9.73333 | 19.0013 | |
Fail | 12 | 3 | 500 | 500 | 0.46539 | 7 | 9.73333 | 19.0013 | |
Fail | 13 | 3 | 500 | 500 | 0.46539 | 7 | 9.73333 | 19.0013 | |
Fail | 14 | 3 | 500 | 500 | 0.46539 | 15 | 9.73333 | 19.0013 | |
Fail | 15 | 3 | 500 | 500 | 0.46539 | 8 | 9.73333 | 19.0013 | |
Fail | 16 | 3 | 500 | 500 | 0.46539 | 18 | 9.73333 | 19.0013 | |
Fail | 17 | 3 | 500 | 500 | 0.46539 | 12 | 9.73333 | 19.0013 | |
Fail | 18 | 3 | 500 | 500 | 0.46539 | 16 | 9.73333 | 19.0013 | |
Fail | 19 | 3 | 500 | 500 | 0.46539 | 4 | 9.73333 | 19.0013 | |
Fail | 20 | 3 | 500 | 500 | 0.46539 | 7 | 9.73333 | 19.0013 | |
Fail | 21 | 3 | 500 | 500 | 0.46539 | 17 | 9.73333 | 19.0013 | |
Fail | 22 | 3 | 500 | 500 | 0.46539 | 12 | 9.73333 | 19.0013 | |
Fail | 23 | 3 | 500 | 500 | 0.46539 | 8 | 9.73333 | 19.0013 | |
Fail | 24 | 3 | 500 | 500 | 0.46539 | 7 | 9.73333 | 19.0013 | |
Fail | 25 | 3 | 500 | 500 | 0.46539 | 15 | 9.73333 | 19.0013 | |
Fail | 26 | 3 | 500 | 500 | 0.46539 | 6 | 9.73333 | 19.0013 | |
Fail | 27 | 3 | 500 | 500 | 0.46539 | 8 | 9.73333 | 19.0013 | |
Fail | 28 | 3 | 500 | 500 | 0.46539 | 12 | 9.73333 | 19.0013 | |
Fail | 29 | 3 | 500 | 500 | 0.46539 | 7 | 9.73333 | 19.0013 | |
Fail | 30 | 3 | 500 | 500 | 0.46539 | 9 | 9.73333 | 19.0013 |
An OUTTABLE= data set can be read later as a TABLE= data set. For example, the following statements read Cirtable
and display an chart (not shown here) identical to the chart in Figure 17.51:
title 'np Chart for the Number of Failing Circuits'; proc shewhart table=Cirtable; npchart Fail*Batch; 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.