 1. Exploratory Data Analysis
1.4. EDA Case Studies
1.4.2. Case Studies
1.4.2.4. Josephson Junction Cryothermometry

## 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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```
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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 are no shifts
in location or scale.  Due to the nature
of the data (a few distinct points with
many repeats), the normality assumption is
questionable.
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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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```   3. Generate a histogram with an
overlaid normal pdf.

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```   4. Generate a normal probability
plot.

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``` 1. The run sequence plot indicates that
there are no shifts of location or
scale.
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``` 2. The lag plot does not indicate any
significant patterns (which would
show the data were not random).
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``` 3. The histogram indicates that a
normal distribution is a good
distribution for these data.
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``` 4. The discrete nature of the data masks
the normality or non-normality of the
data somewhat.  The plot indicates that
a normal distribution provides a rough
approximation for the data.
```
```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. Generate the mean, a confidence
interval for the mean, and compute
a linear fit to detect drift in
location.

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```   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 Levene's test for equal
standard deviations.

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

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

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```   6. Check for outliers using Grubbs' test.

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

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```

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

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``` 2. The mean is 2898.56 and a 95%
confidence interval is (2898.46,2898.66).
The linear fit indicates no meaningful drift
in location since the value of the slope
parameter is near zero.

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``` 3. The standard devaition is 1.30 with
a 95% confidence interval of (1.24,1.38).
Levene's test indicates no significant
drift in variation.

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``` 4. The lag 1 autocorrelation is 0.31.
This indicates some mild non-randomness.

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``` 5. The normal probability plot correlation
coefficient is 0.975.  At the 5% level,
we reject the normality assumption.

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

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

``` 