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1. Exploratory Data Analysis
1.4. EDA Case Studies
1.4.2. Case Studies Standard Resistor

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.

NOTE: This case study has 1,000 points. For better performance, it is highly recommended that you check the "No Update" box on the Spreadsheet window for this case study. This will suppress subsequent updating of the Spreadsheet window as the data are created or modified.

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. Based on the 4-plot, there are shifts
    in location and variation and the data
    are not random.

3. Generate the individual plots.
   1. Generate a run sequence plot.

   2. Generate a lag plot.

 1. The run sequence plot indicates that
    there are shifts of location and
 2. The lag plot shows a strong linear
    pattern, which indicates significant
4. Generate summary statistics, quantitative
   analysis, and print a univariate report.
   1. Generate a table of summary

   2. Generate the sample mean, a confidence
      interval for the population mean, and
      compute a linear fit to detect drift in

   3. Generate the sample standard deviation,
      a confidence interval for the population
      standard deviation, and detect drift in
      variation by dividing the data into
      quarters and computing Levene's test for
      equal standard deviations.

   4. Check for randomness by generating an
      autocorrelation plot and a runs test.

   5. Print a univariate report (this assumes
      steps 2 thru 5 have already been run).

 1. The summary statistics table displays
    25+ statistics.

 2. The mean is 28.0163 and a 95%
    confidence interval is (28.0124,28.02029).
    The linear fit indicates drift in
    location since the slope parameter
    estimate is statistically significant.

 3. The standard deviation is 0.0635 with
    a 95% confidence interval of (0.060829,0.066407).
    Levene's test indicates significant
    change in variation.

 4. The lag 1 autocorrelation is 0.97.
    From the autocorrelation plot, this is
    outside the 95% confidence interval
    bands, indicating significant non-randomness.

 5. The results are summarized in a
    convenient report.

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