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1.
Exploratory Data Analysis
1.4. EDA Case Studies 1.4.2. Case Studies 1.4.2.9. Airplane Polished Window Strength
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| Generation |
This data set was provided by Ed Fuller of the NIST Ceramics
Division in December, 1993. It contains polished window strength
data that was used with two other sets of data (constant
stress-rate data and strength of indented glass data). A paper
by Fuller, et. al. describes the
use of all three data sets to predict lifetime and confidence
intervals for a glass airplane window. A paper by
Pepi describes the all-glass
airplane window design.
For this case study, we restrict ourselves to the problem of finding a good distributional model of the polished window strength data. |
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| Purpose of Analysis |
The goal of this case study is to find a good distributional model
for the polished window strength data. Once a good distributional
model has been determined, various percent points for the polished
widow strength will be computed.
Since the data were used in a study to predict failure times, this case study is a form of reliability analysis. The assessing product reliability chapter contains a more complete discussion of reliabilty methods. This case study is meant to complement that chapter by showing the use of graphical techniques in one aspect of reliability modeling. Data in reliability analysis do not typically follow a normal distribution; non-parametric methods (techniques that do not rely on a specific distribution) are frequently recommended for developing confidence intervals for failure data. One problem with this approach is that sample sizes are often small due to the expense involved in collecting the data, and non-parametric methods do not work well for small sample sizes. For this reason, a parametric method based on a specific distributional model of the data is preferred if the data can be shown to follow a specific distribution. Parametric models typically have greater efficiency at the cost of more specific assumptions about the data, but, it is important to verify that the distributional assumption is indeed valid. If the distributional assumption is not justified, then the conclusions drawn from the model may not be valid. This file can be read by Dataplot with the following commands:
READ FULLER2.DAT Y |
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| Resulting Data |
The following are the data used for this case study. The data are
in ksi (= 1,000 psi).
18.830 20.800 21.657 23.030 23.230 24.050 24.321 25.500 25.520 25.800 26.690 26.770 26.780 27.050 27.670 29.900 31.110 33.200 33.730 33.760 33.890 34.760 35.750 35.910 36.980 37.080 37.090 39.580 44.045 45.290 45.381 |
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