This example illustrates
the use of *STATISTICA Attribute Gage Study (Analytic Method)* (part
of the *Process Analysis - Industrial Statistics* module) for attribute
gage bias and repeatability study in process analysis.

**Data file.** Consider a
fictitious study where an attribute gage is used to measure a dimension
that has tolerance of +/- 0.010. The gage is an end-of-line 100% automatic
inspection gage and is affected by repeatability and bias. The data used
for the analysis are taken from the example data published in the AIAG
(2002) *Measurement System Analysis* (*MSA*) Reference Manual,
volume III. This data are contained in the *STATISTICA* data file
*AttributeGageStudy.sta* located in the *STATISTICA*/Examples/Datasets
directory. This data set contains three variables, the first (*Part*)
which identifies the parts for which measurements were made. The second
column (*Reference*) contains the reference values belonging to each
part, and the third column records the number of times the part was accepted
in 20 trials.

**Specifying the analysis.**
From the *Statistics - Industrial Statistics & Six Sigma* submenu,
select *Process Analysis* to display the *Process Analysis Procedures*
Startup Panel. On the *Quick* tab, select *Attribute gage study
(Analytic method)* and click *OK* to display the *Attribute
gage study (Analytic method)* dialog. This dialog has one tab, *Attribute
gage study*.

First, we will select the
variables for our analysis. On the *Attribute gage study* tab, click
the *Variables* button to display a standard four variable selection
dialog. In the first column, select *Part #*. In the second column,
select *Reference*, and in the third column select *Acceptance*.
The fourth column can be used to select an option variable that records
the *Number of trials*. Since our data used a constant trial size
(20), we will specify that information in the *Attribute gage study
(Analytic method)* dialog.

After selecting the variables
as shown above, click *OK* to return to the *Attribute gage study
(Analytic method)* dialog.

**Testing
for bias.** In *STATISTICA*, two methods are available for testing
bias = 0. To use the AIAG method, you must have eight parts for which
exactly 20 trials have been conducted. One part must have 0 acceptances
and one part must have 20 acceptances. The parts in between these two
extreme parts can have any number of acceptances between 1 and 19. When
testing with the regression method, you are still required to have the
two extreme parts; however, you can have more than six parts between them
and you are only required to conduct a minimum of 15 trials. For this
example, we will use the default *AIAG method*.

**Binary attributes.**
When the data contain aggregated counts (such as ours), it is necessary
to specify how many trials were conducted and to designate and *Attribute
label*. As mentioned above, you can include a variable in your data
set the records the number of trials per part; but when a constant trial
size is used, you can also enter that information here. For our example,
leave the *Number of trials* set to *20* and use the default
Attribute* label*, *Acceptance*.

**Tolerance
limit for calculation.** Because the exact measurement for the part
is not known (i.e., we only know if the part was accepted or rejected
based on established standards), the calculation for bias is based on
a specified tolerance limit. For more details on this calculation, see
Computational
Details. In general, you only need to specify one tolerance limit.
If the probability of acceptance follows an increasing trend as the references
increases, i.e., parts with a lower reference number have a lower probability
of acceptance and parts with a higher reference number have a higher probability
of acceptance, then you need to specify a lower tolerance limit. When
the number of acceptances decreases as the reference value increases,
then you need to specify an upper tolerance limit. For our example, the
number of acceptances increases as the reference value increases, so we
will specify the lower limit. As indicated above, this lower limit is
-0.010. The *Attribute gage study (Analytic method)* dialog should
now look as shown below.

Next, click the *OK*
button to display the Results:
*AttributeGageStudy *dialog.

**Reviewing the results.**
Summary results for a gage attribute study typically include a normal
probability plot of the reference values, a Gage Performance Curve, results
for the t-test of bias = 0, and
certain gage statistics. Click the *Summary* button to display a
multiple plot that contains all of this information.

There are slight discrepancies between the results here and the results in the AIAG manual due to rounding of specific constants and reference values (see the Computational Details), the final results are very similar. As you can see, the adjusted repeatability is 0.0079 and the bias is 0.0024. The t-statistic for testing bias = 0 is 9.6. We are able to reject the hypothesis that there is no bias in the gage.