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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

1.4.2.9.8.

Work This Example Yourself
View Dataplot Macro for this Case Study This page allows you to repeat the analysis outlined in the case study description on the previous page using Dataplot . It is required that you have already downloaded and installed Dataplot and configured your browser. to run Dataplot. Output from each analysis step below will be displayed in one or more of the Dataplot windows. The four main windows are the Output window, the Graphics window, the Command History window, and the data sheet window. Across the top of the main windows there are menus for executing Dataplot commands. Across the bottom is a command entry window where commands can be typed in.
Data Analysis Steps Results and Conclusions

Click on the links below to start Dataplot and run this case study yourself. Each step may use results from previous steps, so please be patient. Wait until the software verifies that the current step is complete before clicking on the next step.


The links in this column will connect you with more detailed information about each analysis step from the case study description. 

1. Invoke Dataplot and read data.

   1. Read in the data.



                              

 1. You have read 1 column of numbers 
    into Dataplot, variable Y.

2. 4-plot of the data.

   1. 4-plot of Y.







 1. The polished window strengths are in the
    range 15 to 50.  The histogram and normal
    probability plot indicate a normal distribution
    fits the data reasonably well, but we can
    probably do better.

3. Generate the Weibull analysis.

   1. Generate 2 iterations of the
      Weibull PPCC plot, a Weibull
      probability plot, and estimate
      some percent points.

   2. Generate a Weibull plot.



   3. Generate a Weibull hazard plot.






 1. The Weibull analysis results in a
    maximum PPCC value of 0.988.



 2. The Weibull plot permits the
    estimation of a 2-parameter Weibull
    model.

 3. The Weibull hazard plot is
    approximately linear, indicating
    that the Weibull provides a good
    distributional model for these data.

4. Generate the lognormal analysis.

   1. Generate 2 iterations of the
      lognormal PPCC plot and a
      lognormal probability plot.



 1. The lognormal analysis results in
    a maximum PPCC value of 0.986.


5. Generate the gamma analysis.

   1. Generate 2 iterations of the
      gamma PPCC plot and a
      gamma probability plot.



 1. The gamma analysis results in
    a maximum PPCC value of 0.987.


6. Generate the power normal analysis.

   1. Generate 2 iterations of the
      power normal PPCC plot and a
      power normal probability plot.



 1. The power normal analysis results
    in a maximum PPCC value of 0.988.


7. Generate the fatigue life analysis.

   1. Generate 2 iterations of the
      fatigue life PPCC plot and
      a fatigue life probability
      plot.



 1. The fatigue life analysis
    results in a maximum PPCC value
    of 0.987.


8. Generate quantitative goodness of fit tests

   1. Generate Anderson-Darling test
      for normality.



   2. Generate Anderson-Darling test
      for lognormal distribution.



   3. Generate Anderson-Darling test
      for Weibull distribution.





 1. The Anderson-Darling normality
    test indicates the normal
    distribution provides an adequate
    fit to the data.

 2. The Anderson-Darling lognormal
    test indicates the lognormal
    distribution provides an adequate
    fit to the data.

 3. The Anderson-Darling Weibull
    test indicates the lognormal
    distribution provides an adequate
    fit to the data.
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