1. Exploratory Data Analysis
1.4. EDA Case Studies
1.4.2. Case Studies
1.4.2.6. Filter Transmittance

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.
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```   1. Read in the data.

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``` 1. You have read 1 column of numbers
into Dataplot, variable Y.
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```2. 4-plot of the data.
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```   1. 4-plot of Y.

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``` 1. Based on the 4-plot, there is a shift
in location and the data are not random.

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

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```   2. Generate a lag plot.

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``` 1. The run sequence plot indicates that
there is a shift in location.

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``` 2. The strong linear pattern of the lag
plot indicates significant
non-randomness.
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```4. Generate summary statistics, quantitative
analysis, and print a univariate report.
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```   1. Generate a table of summary
statistics.

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```   2. Compute a linear fit based on
quarters of the data to detect
drift in location.

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```   3. Compute Levene's test based on
quarters of the data to detect
changes in variation.

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

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```   5. Print a univariate report (this assumes
steps 2 thru 4 have already been run).

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``` 1. The summary statistics table displays
25+ statistics.

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``` 2. The linear fit indicates a slight drift in
location since the slope parameter is
statistically significant, but small.

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``` 3. Levene's test indicates no significant
drift in variation.

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``` 4. The lag 1 autocorrelation is 0.94.
This is outside the 95% confidence
interval bands which indicates significant
non-randomness.

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``` 5. The results are summarized in a
convenient report.

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