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1. Exploratory Data Analysis
1.4. EDA Case Studies
1.4.2. Case Studies
1.4.2.10. Ceramic Strength

1.4.2.10.6.

Work This Example Yourself

View Dataplot Macro for this Case Study This page allows you to use Dataplot to repeat the analysis outlined in the case study description on the previous page. 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. Plot of the response variable
   1. Numerical summary of Y.



   2. 4-plot of Y.




 1. The summary shows the mean strength
    is 650.08 and the standard deviation
    of the strength is 74.64.

 2. The 4-plot shows no drift in
    the location and scale and a
    bimodal distribution.

3. Determine if there is a batch effect.
   1. Generate a bihistogram based on
      the 2 batches.

   2. Generate a q-q plot.


   3. Generate a box plot.



   4. Generate block plots.



   5. Perform a 2-sample t-test for
      equal means.


   6. Perform an F-test for equal
      standard deviations.



 1. The bihistogram shows a distinct
    batch effect of approximately
    75 units.
 2. The q-q plot shows that batch 1
    and batch 2 do not come from a
    common distribution.
 3. The box plot shows that there is
    a batch effect of approximately
    75 to 100 units and there are
    some outliers.
 4. The block plot shows that the batch
    effect is consistent across labs
    and levels of the primary factor.

 5. The t-test confirms the batch
    effect with respect to the means.


 6. The F-test does not indicate any
    significant batch effect with
    respect to the standard deviations.

4. Determine if there is a lab effect.
   1. Generate a box plot for the labs
      with the 2 batches combined.

   2. Generate a box plot for the labs
      for batch 1 only.

   3. Generate a box plot for the labs
      for batch 2 only.


 1. The box plot does not show a
    significant lab effect.

 2. The box plot does not show a
    significant lab effect for batch 1.

 3. The box plot does not show a
    significant lab effect for batch 2.

5. Analysis of primary factors.
   1. Generate a DOE scatter plot for
      batch 1.

   2. Generate a DOE mean plot for
      batch 1.

   3. Generate a DOE sd plot for
      batch 1.

   4. Generate a DOE scatter plot for
      batch 2.

   5. Generate a DOE mean plot for
      batch 2.


   6. Generate a DOE sd plot for
      batch 2.

   7. Generate a DOE interaction
      effects matrix plot for
      batch 1.

   8. Generate a DOE interaction
      effects matrix plot for
      batch 2.


 1. The DOE scatter plot shows the
    range of the points and the
    presence of outliers.
 2. The DOE mean plot shows that
    table speed is the most
    significant factor for batch 1.
 3. The DOE sd plot shows that
    table speed has the most
    variability for batch 1.
 4. The DOE scatter plot shows
    the range of the points and
    the presence of outliers.
 5. The DOE mean plot shows that
    feed rate and wheel grit are
    the most significant factors
    for batch 2.
 6. The DOE sd plot shows that
    the variability is comparable
    for all 3 factors for batch 2.
 7. The DOE interaction effects
    matrix plot provides a ranked
    list of factors with the 
    estimated effects.
 8. The DOE interaction effects
    matrix plot provides a ranked
    list of factors with the 
    estimated effects.
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