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1. Exploratory Data Analysis
1.4. EDA Case Studies
1.4.2. Case Studies Heat Flow Meter 1

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. Based on the 4-plot, there are no shifts
    in location or scale, and the data seem to
    follow a normal distribution.
3. Generate the individual plots.
   1. Generate a run sequence plot.

   2. Generate a lag plot.

   3. Generate a histogram with an
      overlaid normal pdf.

   4. Generate a normal probability

 1. The run sequence plot indicates that
    there are no shifts of location or
 2. The lag plot does not indicate any
    significant patterns (which would
    show the data were not random).
 3. The histogram indicates that a 
    normal distribution is a good
    distribution for these data.
 4. The normal probability plot verifies
    that the normal distribution is a
    reasonable distribution for these data.
4. Generate summary statistics, quantitative
   analysis, and print a univariate report.
   1. Generate a table of summary

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

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

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

   5. Check for normality by computing the
      normal probability plot correlation

   6. Check for outliers using Grubbs' test.

   7. Print a univariate report (this assumes
      steps 2 thru 6 have already been run).

 1. The summary statistics table displays
    25+ statistics.

 2. The mean is 9.261 and a 95%
    confidence interval is (9.258,9.265).
    The linear fit indicates no drift in
    location since the slope parameter
    estimate is essentially zero.

 3. The standard deviation is 0.023 with
    a 95% confidence interval of (0.0207,0.0253).
    Bartlett's test indicates no significant
    change in variation.

 4. The lag 1 autocorrelation is 0.28.
    From the autocorrelation plot, this is
    statistically significant at the 95%

 5. The normal probability plot correlation
    coefficient is 0.999.  At the 5% level,
    we cannot reject the normality assumption.

 6. Grubbs' test detects no outliers at the
    5% level.

 7. The results are summarized in a
    convenient report.

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