SPPCHART procedure

Plots p or np charts for binomial testing for defective items (A.F. Kane & R.W. Payne).

 

Options

PRINT = string token What to print (warnings); default * i.e. nothing
PLOT = string token Type of chart to plot (p, np); default p
METHOD = string token Method to use to obtain the control limits (complementaryloglog, given, logit, probit, untransformed); default untr
TOLERANCEMULTIPLIER = scalar Multiplier to use to test whether to use mean sample size for control limits; default 1
WINDOW = scalar Which high-resolution graphics window to use; default 3
SCREEN = string token Whether or not to clear the graphics screen before plotting (clear, keep); default clea

 

Parameters

NDEFECTIVE = variates Number of defective items
NTESTED = scalars or variates Number of items tested
CENTRELINE = scalars Sets or saves centre line
LOWERCONTROLLIMIT = scalars or variates Sets or saves lower control limit
UPPERCONTROLLIMIT = scalars or variates Sets or saves upper control limit

 

Description

The p and np charts are used in statistical process control to evaluate testing schemes in which successive batches of items are classified as either good or defective. The number of defective items in each batch is specified, in a variate, by the NDEFECTIVE parameter. The NTESTED parameter supplies the number of items in each batch – this can be a scalar if the batches are all of the same size, otherwise it is a variate.

The PLOT option controls the type of chart: the p chart plots the proportion of defective items while the np chart (which is most useful each batch of items has the same total size) plots the number of defective items.

The charts contain not only the observed numbers or proportions but also a centre line (indicating a target value) and lines showing upper and lower control limits (bounding the zone outside which the process is said to be out of control). The control limits relevant to each batch will depend on the batch sizes. The TOLERANCE option determines whether an average total size is used if the individual totals are not exactly equal: this will happen unless either

MIN(NTESTED) * TOLERANCE < MEAN(TESTED)

or

MEAN(TESTED) * TOLERANCE < MAX(NTESTED)

The METHOD option specifies how the various lines are to be defined, with the following settings. They are defined below for a p chart. For an np chart, the values are simple multiplied by the batch size(s).

    untransformed this is the default setting, and requests the method conventionally used in SPC. The centre line is at
p = (total number defective) / (total number tested)
and the limits are at p + 3 × √(p / (1-p))
    given specifies that the values are supplied by the CENTRELINE, LOWERCONTROLLIMIT and UPPERCONTROLLIMIT parameters.
    logit obtains the values as the batch mean +/- three times its standard error as estimated on the logit scale of a generalized linear model (with binomial distribution).
    probit obtains the values as the batch mean +/- three times its standard error as estimated on the probit scale of a generalized linear model
    complementaryloglog obtains the values as the batch mean +/- three times its standard error as estimated on the complementary-log-log scale of a generalized linear model.

For settings of METHOD other than given, the CENTRELINE, LOWERCONTROLLIMIT and UPPERCONTROLLIMIT parameters can be used to save the centre line and limits.

You can set PRINT=warnings to list any batches that are outside the control limits; by default these are suppressed. As usual, the WINDOW option specifies which high-resolution graphics window to use for the plot, and the SCREEN option controls whether or not to clear the graphics screen before plotting.

Options: PRINT, PLOT, METHOD, TOLERANCEMULTIPLIER, WINDOW, SCREEN.

Parameters: NDEFECTIVE, NTESTED, CENTRELINE, LOWERCONTROLLIMIT, UPPERCONTROLLIMIT.

 

Method

For further information about the standard SPC methods see for example Chapter 5 of Montgomery (1985). Section 3.5 of the Guide to the Genstat Command Language, Part 2 Statistics gives more details about generalized linear models.

 

Action with RESTRICT

Any restrictions are ignored.

 

Reference

Montgomery, D.C. (1985). Introduction to Statistical Process Control. Wiley, New York.

 

See also

Procedures: SPCAPABILITY, SPCCHART, SPCUSUM, SPEWMA, SPSHEWHART.

Commands for: Six sigma.

Example

CAPTION  'SPPCHART example',\
         !t('Data from Montgomery (1985), Introduction to',\
         'Statistical Process Control, page 152.');\
         STYLE=meta,plain
VARIATE  [VALUES=12,15,8,10,4,7,16,9,14,10,5,6,17,12,22,\
         8,10,5,13,11,20,18,24,15,9,12,7,13,9,6] Cans
SPPCHART [PRINT=warnings] Cans; NTESTED=50
Updated on January 17, 2018

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