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1. Exploratory Data Analysis
1.4. EDA Case Studies
1.4.2. Case Studies Random Walk

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. Validate assumptions.
   1. 4-plot of Y.

   2. Generate a table of summary
   3. Generate a linear fit to detect
      drift in location.

   4. Detect drift in variation by
      dividing the data into quarters and
      computing Levene's test for equal
      standard deviations.

   5. Check for randomness by generating
      a runs test.

 1. Based on the 4-plot, there are shifts
    in location and scale and the data are not
 2. The summary statistics table displays
    25+ statistics.
 3. The linear fit indicates drift in
    location since the slope parameter
    is statistically significant.
 4. Levene's test indicates significant
    drift in variation.

 5. The runs test indicates significant

3. Generate the randomness plots.
   1. Generate an autocorrelation plot.

   2. Generate a spectral plot.

 1. The autocorrelation plot shows
    significant autocorrelation at lag 1.
 2. The spectral plot shows a single dominant
    low frequency peak.
4. Fit Yi = A0 + A1*Yi-1 + Ei
   and validate.
   1. Generate the fit.

   2. Plot fitted line with original data.

   3. Generate a 4-plot of the residuals
      from the fit.

   4. Generate a uniform probability plot
      of the residuals.

 1. The residual standard deviation from the
    fit is 0.29 (compared to the standard
    deviation of 2.08 from the original

 2. The plot of the predicted values with
    the original data indicates a good fit.

 3. The 4-plot indicates that the assumptions
    of constant location and scale are valid.
    The lag plot indicates that the data are
    random.  However, the histogram and normal
    probability plot indicate that the uniform
    disribution might be a better model for
    the residuals than the normal

 4. The uniform probability plot verifies
    that the residuals can be fit by a
    uniform distribution.

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