**Neyer Software LLC** announces the release of **SenTest **^{TM} Version 1.0, Sensitivity Test and
Analysis software. The software is a full function Windows^{®} based test and analysis program for
conducting and analyzing Sensitivity Tests. This software replaces a number of
separate DOS programs which have been in use since 1988. The software allows
the user to efficiently choose test levels using the Neyer
D-Optimal test method and to analyze the results of the tests using the Likelihood Ratio Test
method. These techniques are widely used in a number of industries. They are
the methods of choice for testing explosive components for the aerospace,
automotive airbag, and military markets. They are the prefered method of the OEDC Guidelines for
testing of chemicals. Testing material for fracture toughness and analysis
of the effects of brain pressure on stroke are other applications.

The experimenter specifies initial guesses for the parameters of the
distribution. **SenTest **^{TM}
then guides the user by picking the stimulus levels. The experimenter tells the
software whether the specimen responded or failed to respond to the stimulus,
and **SenTest **^{TM}
then picks the next stimulus level.

In addition to picking the test levels, **SenTest **^{TM} also contains a variety of analysis
tools to analyze the results of the test. Any combination of these analysis
views can be selected at any time, including during the performance of the
test. The analysis is performed using the utilizing the likelihood
ratio method. This method has been shown to produce more reliable
confidence regions than the older methods. These analysis tools display the
results on the analysis in a total of 6 different analysis views. These are the
numerical analysis, history
plot, probability plot, contour plot, multi
contour plot, and different populations.

**SenTest **^{TM} uses the well established Neyer D-Optimal test
procedure that utilizes knowledge of ** all** of the past
responses in order to more efficiently choose stimulus levels. This is the same
method as the earlier DOS program Optimal. Because it continuously refines the
knowledge of the parameters of the population, the procedure used by

In addition to using the Neyer
D-Optimal test, **SenTest **^{TM}
will also chose test levels according to the (Original) Neyer Test, Bruceton Test, Robbins-Monro Test,
Langlie Test, and Adaptive
Langlie Test methods, depending on the options ordered. These test methods
are included for historical purposes only and should not be used if efficient
testing is required. **SenTest **^{TM
}includes the functionality of the following DOS programs: Bruceton, Langlie, OldTest, Optimal, and Sensit.

The **Bruceton test** was developed during the 1940s.
It was developed, not as an efficient test method, but rather a method which
allowed easy analysis of the tests results. The **Bruceton test** uses a
starting point and a fixed step size. The efficiency of the test is critically
dependent on the step size chosen.

The **Robbins-Monro test** was
developed during the 1950s. It was developed to "home in" on the mean
value. The **Robbins-Monro test** uses a starting point and a decreasing
step size. If the starting point is not chosen properly, or if the initial step
size is too small, the test could waste many samples getting close to the mean.

The **Langlie test** was developed during the 1960s.
It was developed to be a less parameter dependent test than the Bruceton test . With the advent of electronic computers,
the restriction on ability to perform analysis that motivated the Bruceton test test was no longer an issue. The **Langlie**
test uses a lower and upper limit and averages the last test with a previous
level.

The **Adaptive Langlie test**, developed
during the 1970s, is, as the name implies, an adaptation of the original Langlie
test. It was developed to overcome the problem of specification of one test
limit too close to the mean value. The test protocol is the same, except that
the levels shift up if a test level near the upper limit results in a failure,
with a similar shift down if a test level near the lower level results in a
success.

The original Neyer test was developed during the late 1980s. The motivation was to design a test that would use knowledge of all of the test results to pick the test levels most efficiently. It was further refined and became known as the Neyer D-Optimal test (developed in the 1990's). With the advent of computers in the laboratory, it was finally possible to perform the difficult calculations needed to choose as the test level that one level which would provide the most information. The Neyer D-Optimal test uses a lower and upper guess for the mean and a guess for the standard deviation. Unlike the Bruceton test and Langlie test, the Neyer D-Optimal test is almost completely independent of the experimenters guess for the parameters.

The primary analysis method that **SenTest **^{TM} uses is the likelihood
ratio method. This method of analyzing sensitivity tests can be used to
analyze the results of any sensitivity test. It is more general than the test
specific methods that had been developed to analyze the results of Bruceton and
other tests. Unlike any other known analysis method, it can also be used to
analyze the results of tests where there is no unique estimate of the standard
deviation (no overlap of the mixed test results).

In addition to performing the analysis using the likelihood
ratio method, **SenTest **^{TM}
will also analyze tests using the ASENT (asymptotic), Bruceton Analysis, and
Langlie analysis methods, depending on the options ordered. These analysis
methods are included for historical purposes only and should not be used if
reliable analysis is required. All of the analysis methods with the exception
of different populations can be performed
with these alternative analysis methods. **SenTest **^{TM }includes the functionality of the
following DOS programs: ASENT, BrucAnal,
ComSen, LangAnal, MuSig,
PlotSen, and ProbPlot.

The **Numerical** analysis computes the
maximum likelihood estimates of several parameters (the mean, standard
deviation, and requested response levels). It also calculates confidence
regions for these parameters.

The** History Plot **is used to provide a
graphical analysis of sensitivity tests. The data of the test are plotted as a
function of stimulus level versus specimen number. Successes are plotted in one
color with an "X" and failures in another color with a "0".
The routine also prints the values of the parameters Mu and Sigma, as well as
displaying a graph of both the probability curve and integrated curve.

The** Probability Plot **provides plots of
the probability versus stress level. Plotted are the maximum likelihood
estimates of probability as well as the specified confidence bands. The graph
shows the expected probability of success versus stress level. Also plotted are
various confidence bands for upper and lower limits of these stress values. The
user also has the option of displaying the test data overlaid with the
probability graph.

The** Contour Plot **is used to provide a plot
of the confidence regions as a function of confidence. The confidence regions
are those regions that contain the true parameters a certain fraction of the
time. These confidence regions are displayed as a contour map. The contour

The** Multi Contour Plot **is used to
provide a plot of the confidence regions as a function of confidence. This
analysis displays the confidence regions of a number of tests simultaneously.
The confidence regions are those regions that contain the true parameters a
certain fraction of the time. These confidence regions are displayed as a
contour map.

The** Different Populations **analysis
is used to compare the results of two or more test sequences to see if the
samples were chosen from similar or different populations. This analysis method
uses the likelihood
ratio method to compare the two sets of data. It can be used to compare
results of two tests conducted with any test method. It can even compare the
results of two tests conducted under different procedures with different sample
sizes.

Contact Neyer Software LLC at (513) 777-6672

7275 Willowood Dr., Cincinnati OH 45241-3703

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Down load a copy of demonstration software. (1.8 MB).

Contact us at Neyer Software LLC